fixed rebase conflicts

.gitmodules

@@ -1,5 +1,5 @@
-[submodule "code/components/esp32-camera-master"]
-	path = code/components/esp32-camera-master
+[submodule "code/components/esp32-camera"]
+	path = code/components/esp32-camera
 	url = https://github.com/espressif/esp32-camera.git
 [submodule "code/components/esp-nn"]
 	path = code/components/esp-nn

code/components/esp32-camera

@@ -0,0 +1 @@
+Subproject commit de025b8f40e45b4b264b2cdd385000d151d8986a

code/components/jomjol_controlcamera/CMakeLists.txt

@@ -4,6 +4,6 @@ list(APPEND EXTRA_COMPONENT_DIRS $ENV{IDF_PATH}/examples/common_components/proto
 
 idf_component_register(SRCS ${app_sources}
                     INCLUDE_DIRS "."
-                    REQUIRES esp32-camera-master esp_http_server jomjol_logfile jomjol_image_proc nvs_flash jomjol_fileserver_ota jomjol_controlGPIO)
+                    REQUIRES esp32-camera esp_http_server jomjol_logfile jomjol_image_proc nvs_flash jomjol_fileserver_ota jomjol_controlGPIO)
 
 

code/components/jomjol_tfliteclass/CMakeLists.txt

@@ -2,6 +2,6 @@ FILE(GLOB_RECURSE app_sources ${CMAKE_CURRENT_SOURCE_DIR}/*.*)
 
 idf_component_register(SRCS ${app_sources}
                     INCLUDE_DIRS "."
-                    REQUIRES jomjol_image_proc jomjol_logfile esp_http_server esp32-camera-master jomjol_controlcamera jomjol_flowcontroll jomjol_helper)
+                    REQUIRES jomjol_image_proc jomjol_logfile esp_http_server esp32-camera jomjol_controlcamera jomjol_flowcontroll jomjol_helper)
 
 

code/components/tflite-lib/CMakeLists.txt

@@ -1,82 +0,0 @@
-## TODO: GLOB is not a good way to collect files. Use explicit file list instead
-
-cmake_minimum_required(VERSION 3.5)
-
-set(tflite_dir "${CMAKE_CURRENT_SOURCE_DIR}/tensorflow/lite")
-set(tfmicro_dir "${tflite_dir}/micro")
-set(tfmicro_frontend_dir "${tflite_dir}/experimental/microfrontend/lib")
-set(tfmicro_kernels_dir "${tfmicro_dir}/kernels")
-
-file(GLOB srcs_micro
-          "${tfmicro_dir}/*.cc"
-          "${tfmicro_dir}/*.c")
-
-file(GLOB src_micro_frontend
-          "${tfmicro_frontend_dir}/*.c"
-          "${tfmicro_frontend_dir}/*.cc")
-file(GLOB srcs_kernels
-          "${tfmicro_kernels_dir}/*.c"
-          "${tfmicro_kernels_dir}/*.cc")
-
-# remove sources which will be provided by esp_nn
-list(REMOVE_ITEM srcs_kernels
-          "${tfmicro_kernels_dir}/add.cc"
-          "${tfmicro_kernels_dir}/conv.cc"
-          "${tfmicro_kernels_dir}/depthwise_conv.cc"
-          "${tfmicro_kernels_dir}/fully_connected.cc"
-          "${tfmicro_kernels_dir}/mul.cc"
-          "${tfmicro_kernels_dir}/pooling.cc"
-          "${tfmicro_kernels_dir}/softmax.cc")
-
-FILE(GLOB esp_nn_kernels
-          "${tfmicro_kernels_dir}/esp_nn/*.cc")
-
-set(lib_srcs
-          "${srcs_micro}"
-          "${srcs_kernels}"
-          "${esp_nn_kernels}"
-          "${src_micro_frontend}"
-          "${tflite_dir}/kernels/kernel_util.cc"
-          "${tflite_dir}/micro/memory_planner/greedy_memory_planner.cc"
-          "${tflite_dir}/micro/memory_planner/linear_memory_planner.cc"
-          "${tflite_dir}/micro/arena_allocator/non_persistent_arena_buffer_allocator.cc"
-          "${tflite_dir}/micro/arena_allocator/persistent_arena_buffer_allocator.cc"
-          "${tflite_dir}/micro/arena_allocator/recording_single_arena_buffer_allocator.cc"
-          "${tflite_dir}/micro/arena_allocator/single_arena_buffer_allocator.cc"
-          "${tflite_dir}/c/common.cc"
-          "${tflite_dir}/core/api/error_reporter.cc"
-          "${tflite_dir}/core/api/flatbuffer_conversions.cc"
-          "${tflite_dir}/core/api/op_resolver.cc"
-          "${tflite_dir}/core/api/tensor_utils.cc"
-          "${tflite_dir}/kernels/internal/quantization_util.cc"
-          "${tflite_dir}/schema/schema_utils.cc")
-
-set(priv_req esp-nn)
-
-# include component requirements which were introduced after IDF version 4.1
-if("${IDF_VERSION_MAJOR}.${IDF_VERSION_MINOR}" VERSION_GREATER "4.1")
-    list(APPEND priv_req esp_timer driver)
-endif()
-
-idf_component_register(
-            SRCS "${lib_srcs}"
-            INCLUDE_DIRS "." "third_party/gemmlowp"
-                         "third_party/flatbuffers/include"
-                         "third_party/ruy"
-                         "third_party/kissfft"
-            REQUIRES ${pub_req}
-            PRIV_REQUIRES ${priv_req})
-
-# Reduce the level of paranoia to be able to compile TF sources
-target_compile_options(${COMPONENT_LIB} PRIVATE
-  -Wno-maybe-uninitialized
-  -Wno-missing-field-initializers
-  -Wno-error=sign-compare
-  -Wno-error=double-promotion
-  -DESP_NN # enables ESP-NN optimizations by Espressif
-  -Wno-type-limits)
-
-target_compile_options(${COMPONENT_LIB} PRIVATE -fno-unwind-tables -ffunction-sections -fdata-sections -fmessage-length=0 -DTF_LITE_STATIC_MEMORY -DTF_LITE_DISABLE_X86_NEON -O3 -Wsign-compare -Wdouble-promotion -Wshadow -Wunused-variable -Wmissing-field-initializers -Wunused-function -Wswitch -Wvla -Wall -Wextra -Wstrict-aliasing -Wno-unused-parameter -Wno-nonnull)
-target_compile_options(${COMPONENT_LIB} PRIVATE $<$<COMPILE_LANGUAGE:CXX>: -std=c++11 -fno-rtti -fno-exceptions -fno-threadsafe-statics -fno-unwind-tables -ffunction-sections -fdata-sections -fmessage-length=0 -DTF_LITE_STATIC_MEMORY -DTF_LITE_DISABLE_X86_NEON -O3 -Werror -Wsign-compare -Wdouble-promotion -Wshadow -Wunused-variable -Wmissing-field-initializers -Wunused-function -Wswitch -Wvla -Wall -Wextra -Wstrict-aliasing -Wno-unused-parameter -Wno-return-type -Wno-strict-aliasing -std=gnu++14 >)
-target_compile_options(${COMPONENT_LIB} INTERFACE $<$<IN_LIST:-DTF_LITE_STATIC_MEMORY,$<TARGET_PROPERTY:${COMPONENT_LIB},COMPILE_OPTIONS>>:-DTF_LITE_STATIC_MEMORY>)
-target_link_libraries(${COMPONENT_LIB} PRIVATE -lm)

code/components/tflite-lib/tensorflow/lite/builtin_ops.h

@@ -1,194 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_BUILTIN_OPS_H_
-#define TENSORFLOW_LITE_BUILTIN_OPS_H_
-
-// DO NOT EDIT MANUALLY: This file is automatically generated by
-// `schema/builtin_ops_header/generator.cc`.
-
-#ifdef __cplusplus
-extern "C" {
-#endif  // __cplusplus
-
-// The enum for builtin operators.
-// Note: CUSTOM, DELEGATE, and PLACEHOLDER_FOR_GREATER_OP_CODES are 3 special
-// ops which are not real built-in ops.
-typedef enum {
-  kTfLiteBuiltinAdd = 0,
-  kTfLiteBuiltinAveragePool2d = 1,
-  kTfLiteBuiltinConcatenation = 2,
-  kTfLiteBuiltinConv2d = 3,
-  kTfLiteBuiltinDepthwiseConv2d = 4,
-  kTfLiteBuiltinDepthToSpace = 5,
-  kTfLiteBuiltinDequantize = 6,
-  kTfLiteBuiltinEmbeddingLookup = 7,
-  kTfLiteBuiltinFloor = 8,
-  kTfLiteBuiltinFullyConnected = 9,
-  kTfLiteBuiltinHashtableLookup = 10,
-  kTfLiteBuiltinL2Normalization = 11,
-  kTfLiteBuiltinL2Pool2d = 12,
-  kTfLiteBuiltinLocalResponseNormalization = 13,
-  kTfLiteBuiltinLogistic = 14,
-  kTfLiteBuiltinLshProjection = 15,
-  kTfLiteBuiltinLstm = 16,
-  kTfLiteBuiltinMaxPool2d = 17,
-  kTfLiteBuiltinMul = 18,
-  kTfLiteBuiltinRelu = 19,
-  kTfLiteBuiltinReluN1To1 = 20,
-  kTfLiteBuiltinRelu6 = 21,
-  kTfLiteBuiltinReshape = 22,
-  kTfLiteBuiltinResizeBilinear = 23,
-  kTfLiteBuiltinRnn = 24,
-  kTfLiteBuiltinSoftmax = 25,
-  kTfLiteBuiltinSpaceToDepth = 26,
-  kTfLiteBuiltinSvdf = 27,
-  kTfLiteBuiltinTanh = 28,
-  kTfLiteBuiltinConcatEmbeddings = 29,
-  kTfLiteBuiltinSkipGram = 30,
-  kTfLiteBuiltinCall = 31,
-  kTfLiteBuiltinCustom = 32,
-  kTfLiteBuiltinEmbeddingLookupSparse = 33,
-  kTfLiteBuiltinPad = 34,
-  kTfLiteBuiltinUnidirectionalSequenceRnn = 35,
-  kTfLiteBuiltinGather = 36,
-  kTfLiteBuiltinBatchToSpaceNd = 37,
-  kTfLiteBuiltinSpaceToBatchNd = 38,
-  kTfLiteBuiltinTranspose = 39,
-  kTfLiteBuiltinMean = 40,
-  kTfLiteBuiltinSub = 41,
-  kTfLiteBuiltinDiv = 42,
-  kTfLiteBuiltinSqueeze = 43,
-  kTfLiteBuiltinUnidirectionalSequenceLstm = 44,
-  kTfLiteBuiltinStridedSlice = 45,
-  kTfLiteBuiltinBidirectionalSequenceRnn = 46,
-  kTfLiteBuiltinExp = 47,
-  kTfLiteBuiltinTopkV2 = 48,
-  kTfLiteBuiltinSplit = 49,
-  kTfLiteBuiltinLogSoftmax = 50,
-  kTfLiteBuiltinDelegate = 51,
-  kTfLiteBuiltinBidirectionalSequenceLstm = 52,
-  kTfLiteBuiltinCast = 53,
-  kTfLiteBuiltinPrelu = 54,
-  kTfLiteBuiltinMaximum = 55,
-  kTfLiteBuiltinArgMax = 56,
-  kTfLiteBuiltinMinimum = 57,
-  kTfLiteBuiltinLess = 58,
-  kTfLiteBuiltinNeg = 59,
-  kTfLiteBuiltinPadv2 = 60,
-  kTfLiteBuiltinGreater = 61,
-  kTfLiteBuiltinGreaterEqual = 62,
-  kTfLiteBuiltinLessEqual = 63,
-  kTfLiteBuiltinSelect = 64,
-  kTfLiteBuiltinSlice = 65,
-  kTfLiteBuiltinSin = 66,
-  kTfLiteBuiltinTransposeConv = 67,
-  kTfLiteBuiltinSparseToDense = 68,
-  kTfLiteBuiltinTile = 69,
-  kTfLiteBuiltinExpandDims = 70,
-  kTfLiteBuiltinEqual = 71,
-  kTfLiteBuiltinNotEqual = 72,
-  kTfLiteBuiltinLog = 73,
-  kTfLiteBuiltinSum = 74,
-  kTfLiteBuiltinSqrt = 75,
-  kTfLiteBuiltinRsqrt = 76,
-  kTfLiteBuiltinShape = 77,
-  kTfLiteBuiltinPow = 78,
-  kTfLiteBuiltinArgMin = 79,
-  kTfLiteBuiltinFakeQuant = 80,
-  kTfLiteBuiltinReduceProd = 81,
-  kTfLiteBuiltinReduceMax = 82,
-  kTfLiteBuiltinPack = 83,
-  kTfLiteBuiltinLogicalOr = 84,
-  kTfLiteBuiltinOneHot = 85,
-  kTfLiteBuiltinLogicalAnd = 86,
-  kTfLiteBuiltinLogicalNot = 87,
-  kTfLiteBuiltinUnpack = 88,
-  kTfLiteBuiltinReduceMin = 89,
-  kTfLiteBuiltinFloorDiv = 90,
-  kTfLiteBuiltinReduceAny = 91,
-  kTfLiteBuiltinSquare = 92,
-  kTfLiteBuiltinZerosLike = 93,
-  kTfLiteBuiltinFill = 94,
-  kTfLiteBuiltinFloorMod = 95,
-  kTfLiteBuiltinRange = 96,
-  kTfLiteBuiltinResizeNearestNeighbor = 97,
-  kTfLiteBuiltinLeakyRelu = 98,
-  kTfLiteBuiltinSquaredDifference = 99,
-  kTfLiteBuiltinMirrorPad = 100,
-  kTfLiteBuiltinAbs = 101,
-  kTfLiteBuiltinSplitV = 102,
-  kTfLiteBuiltinUnique = 103,
-  kTfLiteBuiltinCeil = 104,
-  kTfLiteBuiltinReverseV2 = 105,
-  kTfLiteBuiltinAddN = 106,
-  kTfLiteBuiltinGatherNd = 107,
-  kTfLiteBuiltinCos = 108,
-  kTfLiteBuiltinWhere = 109,
-  kTfLiteBuiltinRank = 110,
-  kTfLiteBuiltinElu = 111,
-  kTfLiteBuiltinReverseSequence = 112,
-  kTfLiteBuiltinMatrixDiag = 113,
-  kTfLiteBuiltinQuantize = 114,
-  kTfLiteBuiltinMatrixSetDiag = 115,
-  kTfLiteBuiltinRound = 116,
-  kTfLiteBuiltinHardSwish = 117,
-  kTfLiteBuiltinIf = 118,
-  kTfLiteBuiltinWhile = 119,
-  kTfLiteBuiltinNonMaxSuppressionV4 = 120,
-  kTfLiteBuiltinNonMaxSuppressionV5 = 121,
-  kTfLiteBuiltinScatterNd = 122,
-  kTfLiteBuiltinSelectV2 = 123,
-  kTfLiteBuiltinDensify = 124,
-  kTfLiteBuiltinSegmentSum = 125,
-  kTfLiteBuiltinBatchMatmul = 126,
-  kTfLiteBuiltinPlaceholderForGreaterOpCodes = 127,
-  kTfLiteBuiltinCumsum = 128,
-  kTfLiteBuiltinCallOnce = 129,
-  kTfLiteBuiltinBroadcastTo = 130,
-  kTfLiteBuiltinRfft2d = 131,
-  kTfLiteBuiltinConv3d = 132,
-  kTfLiteBuiltinImag = 133,
-  kTfLiteBuiltinReal = 134,
-  kTfLiteBuiltinComplexAbs = 135,
-  kTfLiteBuiltinHashtable = 136,
-  kTfLiteBuiltinHashtableFind = 137,
-  kTfLiteBuiltinHashtableImport = 138,
-  kTfLiteBuiltinHashtableSize = 139,
-  kTfLiteBuiltinReduceAll = 140,
-  kTfLiteBuiltinConv3dTranspose = 141,
-  kTfLiteBuiltinVarHandle = 142,
-  kTfLiteBuiltinReadVariable = 143,
-  kTfLiteBuiltinAssignVariable = 144,
-  kTfLiteBuiltinBroadcastArgs = 145,
-  kTfLiteBuiltinRandomStandardNormal = 146,
-  kTfLiteBuiltinBucketize = 147,
-  kTfLiteBuiltinRandomUniform = 148,
-  kTfLiteBuiltinMultinomial = 149,
-  kTfLiteBuiltinGelu = 150,
-  kTfLiteBuiltinDynamicUpdateSlice = 151,
-  kTfLiteBuiltinRelu0To1 = 152,
-  kTfLiteBuiltinUnsortedSegmentProd = 153,
-  kTfLiteBuiltinUnsortedSegmentMax = 154,
-  kTfLiteBuiltinUnsortedSegmentSum = 155,
-  kTfLiteBuiltinAtan2 = 156,
-  kTfLiteBuiltinUnsortedSegmentMin = 157,
-  kTfLiteBuiltinSign = 158,
-} TfLiteBuiltinOperator;
-
-#ifdef __cplusplus
-}  // extern "C"
-#endif  // __cplusplus
-#endif  // TENSORFLOW_LITE_BUILTIN_OPS_H_

code/components/tflite-lib/tensorflow/lite/c/common.cc

@@ -1,307 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/common.h"
-
-#include "tensorflow/lite/c/c_api_types.h"
-#ifdef TF_LITE_TENSORFLOW_PROFILER
-#include "tensorflow/lite/tensorflow_profiler_logger.h"
-#endif
-
-#ifndef TF_LITE_STATIC_MEMORY
-#include <stdlib.h>
-#include <string.h>
-#endif  // TF_LITE_STATIC_MEMORY
-
-extern "C" {
-
-size_t TfLiteIntArrayGetSizeInBytes(int size) {
-  static TfLiteIntArray dummy;
-
-  size_t computed_size = sizeof(dummy) + sizeof(dummy.data[0]) * size;
-#if defined(_MSC_VER)
-  // Context for why this is needed is in http://b/189926408#comment21
-  computed_size -= sizeof(dummy.data[0]);
-#endif
-  return computed_size;
-}
-
-int TfLiteIntArrayEqual(const TfLiteIntArray* a, const TfLiteIntArray* b) {
-  if (a == b) return 1;
-  if (a == nullptr || b == nullptr) return 0;
-  return TfLiteIntArrayEqualsArray(a, b->size, b->data);
-}
-
-int TfLiteIntArrayEqualsArray(const TfLiteIntArray* a, int b_size,
-                              const int b_data[]) {
-  if (a == nullptr) return (b_size == 0);
-  if (a->size != b_size) return 0;
-  int i = 0;
-  for (; i < a->size; i++)
-    if (a->data[i] != b_data[i]) return 0;
-  return 1;
-}
-
-#ifndef TF_LITE_STATIC_MEMORY
-
-TfLiteIntArray* TfLiteIntArrayCreate(int size) {
-  size_t alloc_size = TfLiteIntArrayGetSizeInBytes(size);
-  if (alloc_size <= 0) return nullptr;
-  TfLiteIntArray* ret = (TfLiteIntArray*)malloc(alloc_size);
-  if (!ret) return ret;
-  ret->size = size;
-  return ret;
-}
-
-TfLiteIntArray* TfLiteIntArrayCopy(const TfLiteIntArray* src) {
-  if (!src) return nullptr;
-  TfLiteIntArray* ret = TfLiteIntArrayCreate(src->size);
-  if (ret) {
-    memcpy(ret->data, src->data, src->size * sizeof(int));
-  }
-  return ret;
-}
-
-void TfLiteIntArrayFree(TfLiteIntArray* a) { free(a); }
-
-#endif  // TF_LITE_STATIC_MEMORY
-
-int TfLiteFloatArrayGetSizeInBytes(int size) {
-  static TfLiteFloatArray dummy;
-
-  int computed_size = sizeof(dummy) + sizeof(dummy.data[0]) * size;
-#if defined(_MSC_VER)
-  // Context for why this is needed is in http://b/189926408#comment21
-  computed_size -= sizeof(dummy.data[0]);
-#endif
-  return computed_size;
-}
-
-#ifndef TF_LITE_STATIC_MEMORY
-
-TfLiteFloatArray* TfLiteFloatArrayCreate(int size) {
-  TfLiteFloatArray* ret =
-      (TfLiteFloatArray*)malloc(TfLiteFloatArrayGetSizeInBytes(size));
-  ret->size = size;
-  return ret;
-}
-
-void TfLiteFloatArrayFree(TfLiteFloatArray* a) { free(a); }
-
-void TfLiteTensorDataFree(TfLiteTensor* t) {
-  if (t->allocation_type == kTfLiteDynamic ||
-      t->allocation_type == kTfLitePersistentRo) {
-    if (t->data.raw) {
-#ifdef TF_LITE_TENSORFLOW_PROFILER
-      tflite::OnTfLiteTensorDealloc(t);
-#endif
-      free(t->data.raw);
-    }
-  }
-  t->data.raw = nullptr;
-}
-
-void TfLiteQuantizationFree(TfLiteQuantization* quantization) {
-  if (quantization->type == kTfLiteAffineQuantization) {
-    TfLiteAffineQuantization* q_params =
-        (TfLiteAffineQuantization*)(quantization->params);
-    if (q_params->scale) {
-      TfLiteFloatArrayFree(q_params->scale);
-      q_params->scale = nullptr;
-    }
-    if (q_params->zero_point) {
-      TfLiteIntArrayFree(q_params->zero_point);
-      q_params->zero_point = nullptr;
-    }
-    free(q_params);
-  }
-  quantization->params = nullptr;
-  quantization->type = kTfLiteNoQuantization;
-}
-
-void TfLiteSparsityFree(TfLiteSparsity* sparsity) {
-  if (sparsity == nullptr) {
-    return;
-  }
-
-  if (sparsity->traversal_order) {
-    TfLiteIntArrayFree(sparsity->traversal_order);
-    sparsity->traversal_order = nullptr;
-  }
-
-  if (sparsity->block_map) {
-    TfLiteIntArrayFree(sparsity->block_map);
-    sparsity->block_map = nullptr;
-  }
-
-  if (sparsity->dim_metadata) {
-    int i = 0;
-    for (; i < sparsity->dim_metadata_size; i++) {
-      TfLiteDimensionMetadata metadata = sparsity->dim_metadata[i];
-      if (metadata.format == kTfLiteDimSparseCSR) {
-        TfLiteIntArrayFree(metadata.array_segments);
-        metadata.array_segments = nullptr;
-        TfLiteIntArrayFree(metadata.array_indices);
-        metadata.array_indices = nullptr;
-      }
-    }
-    free(sparsity->dim_metadata);
-    sparsity->dim_metadata = nullptr;
-  }
-
-  free(sparsity);
-}
-
-void TfLiteTensorFree(TfLiteTensor* t) {
-  TfLiteTensorDataFree(t);
-  if (t->dims) TfLiteIntArrayFree(t->dims);
-  t->dims = nullptr;
-
-  if (t->dims_signature) {
-    TfLiteIntArrayFree((TfLiteIntArray*)t->dims_signature);
-  }
-  t->dims_signature = nullptr;
-
-  TfLiteQuantizationFree(&t->quantization);
-  TfLiteSparsityFree(t->sparsity);
-  t->sparsity = nullptr;
-}
-
-void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims,
-                       TfLiteQuantizationParams quantization, char* buffer,
-                       size_t size, TfLiteAllocationType allocation_type,
-                       const void* allocation, bool is_variable,
-                       TfLiteTensor* tensor) {
-  TfLiteTensorFree(tensor);
-  tensor->type = type;
-  tensor->name = name;
-  tensor->dims = dims;
-  tensor->params = quantization;
-  tensor->data.raw = buffer;
-  tensor->bytes = size;
-  tensor->allocation_type = allocation_type;
-  tensor->allocation = allocation;
-  tensor->is_variable = is_variable;
-
-  tensor->quantization.type = kTfLiteNoQuantization;
-  tensor->quantization.params = nullptr;
-}
-
-TfLiteStatus TfLiteTensorCopy(const TfLiteTensor* src, TfLiteTensor* dst) {
-  if (!src || !dst) return kTfLiteOk;
-  if (src->bytes != dst->bytes) return kTfLiteError;
-  if (src == dst) return kTfLiteOk;
-
-  dst->type = src->type;
-  if (dst->dims) TfLiteIntArrayFree(dst->dims);
-  dst->dims = TfLiteIntArrayCopy(src->dims);
-  memcpy(dst->data.raw, src->data.raw, src->bytes);
-  dst->buffer_handle = src->buffer_handle;
-  dst->data_is_stale = src->data_is_stale;
-  dst->delegate = src->delegate;
-
-  return kTfLiteOk;
-}
-
-void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor) {
-  if (tensor->allocation_type != kTfLiteDynamic &&
-      tensor->allocation_type != kTfLitePersistentRo) {
-    return;
-  }
-  // TODO(b/145340303): Tensor data should be aligned.
-  if (!tensor->data.raw) {
-    tensor->data.raw = (char*)malloc(num_bytes);
-#ifdef TF_LITE_TENSORFLOW_PROFILER
-    tflite::OnTfLiteTensorAlloc(tensor, num_bytes);
-#endif
-  } else if (num_bytes > tensor->bytes) {
-#ifdef TF_LITE_TENSORFLOW_PROFILER
-    tflite::OnTfLiteTensorDealloc(tensor);
-#endif
-    tensor->data.raw = (char*)realloc(tensor->data.raw, num_bytes);
-#ifdef TF_LITE_TENSORFLOW_PROFILER
-    tflite::OnTfLiteTensorAlloc(tensor, num_bytes);
-#endif
-  }
-  tensor->bytes = num_bytes;
-}
-#endif  // TF_LITE_STATIC_MEMORY
-
-const char* TfLiteTypeGetName(TfLiteType type) {
-  switch (type) {
-    case kTfLiteNoType:
-      return "NOTYPE";
-    case kTfLiteFloat32:
-      return "FLOAT32";
-    case kTfLiteUInt16:
-      return "UINT16";
-    case kTfLiteInt16:
-      return "INT16";
-    case kTfLiteInt32:
-      return "INT32";
-    case kTfLiteUInt32:
-      return "UINT32";
-    case kTfLiteUInt8:
-      return "UINT8";
-    case kTfLiteInt8:
-      return "INT8";
-    case kTfLiteInt64:
-      return "INT64";
-    case kTfLiteUInt64:
-      return "UINT64";
-    case kTfLiteBool:
-      return "BOOL";
-    case kTfLiteComplex64:
-      return "COMPLEX64";
-    case kTfLiteComplex128:
-      return "COMPLEX128";
-    case kTfLiteString:
-      return "STRING";
-    case kTfLiteFloat16:
-      return "FLOAT16";
-    case kTfLiteFloat64:
-      return "FLOAT64";
-    case kTfLiteResource:
-      return "RESOURCE";
-    case kTfLiteVariant:
-      return "VARIANT";
-  }
-  return "Unknown type";
-}
-
-TfLiteDelegate TfLiteDelegateCreate() { return TfLiteDelegate{}; }
-
-struct TfLiteOpaqueDelegateStruct* TfLiteOpaqueDelegateCreate(
-    const TfLiteOpaqueDelegateBuilder* opaque_delegate_builder) {
-  if (!opaque_delegate_builder) return nullptr;
-
-  TfLiteDelegate* result = new TfLiteDelegate{};
-  result->opaque_delegate_builder = new TfLiteOpaqueDelegateBuilder{};
-  *(result->opaque_delegate_builder) = *opaque_delegate_builder;
-
-  return reinterpret_cast<struct TfLiteOpaqueDelegateStruct*>(result);
-}
-
-void TfLiteOpaqueDelegateDelete(
-    const struct TfLiteOpaqueDelegateStruct* opaque_delegate) {
-  if (!opaque_delegate) return;
-
-  const TfLiteDelegate* tflite_delegate =
-      reinterpret_cast<const TfLiteDelegate*>(opaque_delegate);
-  delete tflite_delegate->opaque_delegate_builder;
-  delete tflite_delegate;
-}
-
-}  // extern "C"

code/components/tflite-lib/tensorflow/lite/c/common.h

@@ -1,1095 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-// This file defines common C types and APIs for implementing operations,
-// delegates and other constructs in TensorFlow Lite. The actual operations and
-// delegates can be defined using C++, but the interface between the interpreter
-// and the operations are C.
-//
-// Summary of abstractions
-// TF_LITE_ENSURE - Self-sufficient error checking
-// TfLiteStatus - Status reporting
-// TfLiteIntArray - stores tensor shapes (dims),
-// TfLiteContext - allows an op to access the tensors
-// TfLiteTensor - tensor (a multidimensional array)
-// TfLiteNode - a single node or operation
-// TfLiteRegistration - the implementation of a conceptual operation.
-// TfLiteDelegate - allows delegation of nodes to alternative backends.
-//
-// Some abstractions in this file are created and managed by Interpreter.
-//
-// NOTE: The order of values in these structs are "semi-ABI stable". New values
-// should be added only to the end of structs and never reordered.
-
-#ifndef TENSORFLOW_LITE_C_COMMON_H_
-#define TENSORFLOW_LITE_C_COMMON_H_
-
-#include <stdbool.h>
-#include <stddef.h>
-#include <stdint.h>
-
-#include "tensorflow/lite/c/c_api_types.h"  // IWYU pragma: export
-
-#ifdef __cplusplus
-extern "C" {
-#endif  // __cplusplus
-
-// The list of external context types known to TF Lite. This list exists solely
-// to avoid conflicts and to ensure ops can share the external contexts they
-// need. Access to the external contexts is controlled by one of the
-// corresponding support files.
-typedef enum TfLiteExternalContextType {
-  kTfLiteEigenContext = 0,       // include eigen_support.h to use.
-  kTfLiteGemmLowpContext = 1,    // include gemm_support.h to use.
-  kTfLiteEdgeTpuContext = 2,     // Placeholder for Edge TPU support.
-  kTfLiteCpuBackendContext = 3,  // include cpu_backend_context.h to use.
-  kTfLiteMaxExternalContexts = 4
-} TfLiteExternalContextType;
-
-// Forward declare so dependent structs and methods can reference these types
-// prior to the struct definitions.
-struct TfLiteContext;
-struct TfLiteDelegate;
-struct TfLiteRegistration;
-struct TfLiteOpaqueDelegateStruct;
-struct TfLiteOpaqueDelegateBuilder;
-
-// An external context is a collection of information unrelated to the TF Lite
-// framework, but useful to a subset of the ops. TF Lite knows very little
-// about the actual contexts, but it keeps a list of them, and is able to
-// refresh them if configurations like the number of recommended threads
-// change.
-typedef struct TfLiteExternalContext {
-  TfLiteExternalContextType type;
-  TfLiteStatus (*Refresh)(struct TfLiteContext* context);
-} TfLiteExternalContext;
-
-#define kTfLiteOptionalTensor (-1)
-
-// Fixed size list of integers. Used for dimensions and inputs/outputs tensor
-// indices
-typedef struct TfLiteIntArray {
-  int size;
-
-#if defined(_MSC_VER)
-  // Context for why this is needed is in http://b/189926408#comment21
-  int data[1];
-#elif (!defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
-       __GNUC_MINOR__ >= 1) ||                                      \
-    defined(HEXAGON) ||                                             \
-    (defined(__clang__) && __clang_major__ == 7 && __clang_minor__ == 1)
-  // gcc 6.1+ have a bug where flexible members aren't properly handled
-  // https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c
-  int data[0];
-#else
-  int data[];
-#endif
-} TfLiteIntArray;
-
-// Given the size (number of elements) in a TfLiteIntArray, calculate its size
-// in bytes.
-size_t TfLiteIntArrayGetSizeInBytes(int size);
-
-#ifndef TF_LITE_STATIC_MEMORY
-// Create a array of a given `size` (uninitialized entries).
-// This returns a pointer, that you must free using TfLiteIntArrayFree().
-TfLiteIntArray* TfLiteIntArrayCreate(int size);
-#endif
-
-// Check if two intarrays are equal. Returns 1 if they are equal, 0 otherwise.
-int TfLiteIntArrayEqual(const TfLiteIntArray* a, const TfLiteIntArray* b);
-
-// Check if an intarray equals an array. Returns 1 if equals, 0 otherwise.
-int TfLiteIntArrayEqualsArray(const TfLiteIntArray* a, int b_size,
-                              const int b_data[]);
-
-#ifndef TF_LITE_STATIC_MEMORY
-// Create a copy of an array passed as `src`.
-// You are expected to free memory with TfLiteIntArrayFree
-TfLiteIntArray* TfLiteIntArrayCopy(const TfLiteIntArray* src);
-
-// Free memory of array `a`.
-void TfLiteIntArrayFree(TfLiteIntArray* a);
-#endif  // TF_LITE_STATIC_MEMORY
-
-// Fixed size list of floats. Used for per-channel quantization.
-typedef struct TfLiteFloatArray {
-  int size;
-#if defined(_MSC_VER)
-  // Context for why this is needed is in http://b/189926408#comment21
-  float data[1];
-#elif (!defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
-       __GNUC_MINOR__ >= 1) ||                                      \
-    defined(HEXAGON) ||                                             \
-    (defined(__clang__) && __clang_major__ == 7 && __clang_minor__ == 1)
-  // gcc 6.1+ have a bug where flexible members aren't properly handled
-  // https://github.com/google/re2/commit/b94b7cd42e9f02673cd748c1ac1d16db4052514c
-  float data[0];
-#else
-  float data[];
-#endif
-} TfLiteFloatArray;
-
-// Given the size (number of elements) in a TfLiteFloatArray, calculate its size
-// in bytes.
-int TfLiteFloatArrayGetSizeInBytes(int size);
-
-#ifndef TF_LITE_STATIC_MEMORY
-// Create a array of a given `size` (uninitialized entries).
-// This returns a pointer, that you must free using TfLiteFloatArrayFree().
-TfLiteFloatArray* TfLiteFloatArrayCreate(int size);
-
-// Free memory of array `a`.
-void TfLiteFloatArrayFree(TfLiteFloatArray* a);
-#endif  // TF_LITE_STATIC_MEMORY
-
-// Since we must not depend on any libraries, define a minimal subset of
-// error macros while avoiding names that have pre-conceived meanings like
-// assert and check.
-
-// Try to make all reporting calls through TF_LITE_KERNEL_LOG rather than
-// calling the context->ReportError function directly, so that message strings
-// can be stripped out if the binary size needs to be severely optimized.
-#ifndef TF_LITE_STRIP_ERROR_STRINGS
-#define TF_LITE_KERNEL_LOG(context, ...)            \
-  do {                                              \
-    (context)->ReportError((context), __VA_ARGS__); \
-  } while (false)
-
-#define TF_LITE_MAYBE_KERNEL_LOG(context, ...)        \
-  do {                                                \
-    if ((context) != nullptr) {                       \
-      (context)->ReportError((context), __VA_ARGS__); \
-    }                                                 \
-  } while (false)
-#else  // TF_LITE_STRIP_ERROR_STRINGS
-#define ARGS_UNUSED(...) (void)sizeof(#__VA_ARGS__)
-#define TF_LITE_KERNEL_LOG(context, ...) ARGS_UNUSED(__VA_ARGS__)
-#define TF_LITE_MAYBE_KERNEL_LOG(context, ...) ARGS_UNUSED(__VA_ARGS__)
-#endif  // TF_LITE_STRIP_ERROR_STRINGS
-
-// Check whether value is true, and if not return kTfLiteError from
-// the current function (and report the error string msg).
-#define TF_LITE_ENSURE_MSG(context, value, msg)        \
-  do {                                                 \
-    if (!(value)) {                                    \
-      TF_LITE_KERNEL_LOG((context), __FILE__ " " msg); \
-      return kTfLiteError;                             \
-    }                                                  \
-  } while (0)
-
-// Check whether the value `a` is true, and if not return kTfLiteError from
-// the current function, while also reporting the location of the error.
-#define TF_LITE_ENSURE(context, a)                                      \
-  do {                                                                  \
-    if (!(a)) {                                                         \
-      TF_LITE_KERNEL_LOG((context), "%s:%d %s was not true.", __FILE__, \
-                         __LINE__, #a);                                 \
-      return kTfLiteError;                                              \
-    }                                                                   \
-  } while (0)
-
-#define TF_LITE_ENSURE_STATUS(a) \
-  do {                           \
-    const TfLiteStatus s = (a);  \
-    if (s != kTfLiteOk) {        \
-      return s;                  \
-    }                            \
-  } while (0)
-
-// Check whether the value `a == b` is true, and if not return kTfLiteError from
-// the current function, while also reporting the location of the error.
-// `a` and `b` may be evaluated more than once, so no side effects or
-// extremely expensive computations should be done.
-// NOTE: Use TF_LITE_ENSURE_TYPES_EQ if comparing TfLiteTypes.
-#define TF_LITE_ENSURE_EQ(context, a, b)                                   \
-  do {                                                                     \
-    if ((a) != (b)) {                                                      \
-      TF_LITE_KERNEL_LOG((context), "%s:%d %s != %s (%d != %d)", __FILE__, \
-                         __LINE__, #a, #b, (a), (b));                      \
-      return kTfLiteError;                                                 \
-    }                                                                      \
-  } while (0)
-
-#define TF_LITE_ENSURE_TYPES_EQ(context, a, b)                             \
-  do {                                                                     \
-    if ((a) != (b)) {                                                      \
-      TF_LITE_KERNEL_LOG((context), "%s:%d %s != %s (%s != %s)", __FILE__, \
-                         __LINE__, #a, #b, TfLiteTypeGetName(a),           \
-                         TfLiteTypeGetName(b));                            \
-      return kTfLiteError;                                                 \
-    }                                                                      \
-  } while (0)
-
-#define TF_LITE_ENSURE_NEAR(context, a, b, epsilon)                          \
-  do {                                                                       \
-    auto delta = ((a) > (b)) ? ((a) - (b)) : ((b) - (a));                    \
-    if (delta > epsilon) {                                                   \
-      TF_LITE_KERNEL_LOG((context), "%s:%d %s not near %s (%f != %f)",       \
-                         __FILE__, __LINE__, #a, #b, static_cast<double>(a), \
-                         static_cast<double>(b));                            \
-      return kTfLiteError;                                                   \
-    }                                                                        \
-  } while (0)
-
-#define TF_LITE_ENSURE_OK(context, status) \
-  do {                                     \
-    const TfLiteStatus s = (status);       \
-    if ((s) != kTfLiteOk) {                \
-      return s;                            \
-    }                                      \
-  } while (0)
-
-// Single-precision complex data type compatible with the C99 definition.
-typedef struct TfLiteComplex64 {
-  float re, im;  // real and imaginary parts, respectively.
-} TfLiteComplex64;
-
-// Double-precision complex data type compatible with the C99 definition.
-typedef struct TfLiteComplex128 {
-  double re, im;  // real and imaginary parts, respectively.
-} TfLiteComplex128;
-
-// Half precision data type compatible with the C99 definition.
-typedef struct TfLiteFloat16 {
-  uint16_t data;
-} TfLiteFloat16;
-
-// Return the name of a given type, for error reporting purposes.
-const char* TfLiteTypeGetName(TfLiteType type);
-
-// SupportedQuantizationTypes.
-typedef enum TfLiteQuantizationType {
-  // No quantization.
-  kTfLiteNoQuantization = 0,
-  // Affine quantization (with support for per-channel quantization).
-  // Corresponds to TfLiteAffineQuantization.
-  kTfLiteAffineQuantization = 1,
-} TfLiteQuantizationType;
-
-// Structure specifying the quantization used by the tensor, if-any.
-typedef struct TfLiteQuantization {
-  // The type of quantization held by params.
-  TfLiteQuantizationType type;
-  // Holds an optional reference to a quantization param structure. The actual
-  // type depends on the value of the `type` field (see the comment there for
-  // the values and corresponding types).
-  void* params;
-} TfLiteQuantization;
-
-// Parameters for asymmetric quantization across a dimension (i.e per output
-// channel quantization).
-// quantized_dimension specifies which dimension the scales and zero_points
-// correspond to.
-// For a particular value in quantized_dimension, quantized values can be
-// converted back to float using:
-//     real_value = scale * (quantized_value - zero_point)
-typedef struct TfLiteAffineQuantization {
-  TfLiteFloatArray* scale;
-  TfLiteIntArray* zero_point;
-  int32_t quantized_dimension;
-} TfLiteAffineQuantization;
-
-/* A union of pointers that points to memory for a given tensor. */
-typedef union TfLitePtrUnion {
-  /* Do not access these members directly, if possible, use
-   * GetTensorData<TYPE>(tensor) instead, otherwise only access .data, as other
-   * members are deprecated. */
-  int32_t* i32;
-  uint32_t* u32;
-  int64_t* i64;
-  uint64_t* u64;
-  float* f;
-  TfLiteFloat16* f16;
-  double* f64;
-  char* raw;
-  const char* raw_const;
-  uint8_t* uint8;
-  bool* b;
-  int16_t* i16;
-  uint16_t* ui16;
-  TfLiteComplex64* c64;
-  TfLiteComplex128* c128;
-  int8_t* int8;
-  /* Only use this member. */
-  void* data;
-} TfLitePtrUnion;
-
-// Memory allocation strategies.
-//  * kTfLiteMmapRo: Read-only memory-mapped data, or data externally allocated.
-//  * kTfLiteArenaRw: Arena allocated with no guarantees about persistence,
-//        and available during eval.
-//  * kTfLiteArenaRwPersistent: Arena allocated but persistent across eval, and
-//        only available during eval.
-//  * kTfLiteDynamic: Allocated during eval, or for string tensors.
-//  * kTfLitePersistentRo: Allocated and populated during prepare. This is
-//        useful for tensors that can be computed during prepare and treated
-//        as constant inputs for downstream ops (also in prepare).
-//  * kTfLiteCustom: Custom memory allocation provided by the user. See
-//        TfLiteCustomAllocation below.
-typedef enum TfLiteAllocationType {
-  kTfLiteMemNone = 0,
-  kTfLiteMmapRo,
-  kTfLiteArenaRw,
-  kTfLiteArenaRwPersistent,
-  kTfLiteDynamic,
-  kTfLitePersistentRo,
-  kTfLiteCustom,
-} TfLiteAllocationType;
-
-// The delegates should use zero or positive integers to represent handles.
-// -1 is reserved from unallocated status.
-typedef int TfLiteBufferHandle;
-enum {
-  kTfLiteNullBufferHandle = -1,
-};
-
-// Storage format of each dimension in a sparse tensor.
-typedef enum TfLiteDimensionType {
-  kTfLiteDimDense = 0,
-  kTfLiteDimSparseCSR,
-} TfLiteDimensionType;
-
-// Metadata to encode each dimension in a sparse tensor.
-typedef struct TfLiteDimensionMetadata {
-  TfLiteDimensionType format;
-  int dense_size;
-  TfLiteIntArray* array_segments;
-  TfLiteIntArray* array_indices;
-} TfLiteDimensionMetadata;
-
-// Parameters used to encode a sparse tensor. For detailed explanation of each
-// field please refer to lite/schema/schema.fbs.
-typedef struct TfLiteSparsity {
-  TfLiteIntArray* traversal_order;
-  TfLiteIntArray* block_map;
-  TfLiteDimensionMetadata* dim_metadata;
-  int dim_metadata_size;
-} TfLiteSparsity;
-
-// Defines a custom memory allocation not owned by the runtime.
-// `data` should be aligned to kDefaultTensorAlignment defined in
-// lite/util.h. (Currently 64 bytes)
-// NOTE: See Interpreter.SetCustomAllocationForTensor for details on usage.
-typedef struct TfLiteCustomAllocation {
-  void* data;
-  size_t bytes;
-} TfLiteCustomAllocation;
-
-// The flags used in `Interpreter::SetCustomAllocationForTensor`.
-// Note that this is a bitmask, so the values should be 1, 2, 4, 8, ...etc.
-typedef enum TfLiteCustomAllocationFlags {
-  kTfLiteCustomAllocationFlagsNone = 0,
-  // Skips checking whether allocation.data points to an aligned buffer as
-  // expected by the TFLite runtime.
-  // NOTE: Setting this flag can cause crashes when calling Invoke().
-  // Use with caution.
-  kTfLiteCustomAllocationFlagsSkipAlignCheck = 1,
-} TfLiteCustomAllocationFlags;
-
-// A tensor in the interpreter system which is a wrapper around a buffer of
-// data including a dimensionality (or NULL if not currently defined).
-#ifndef TF_LITE_STATIC_MEMORY
-typedef struct TfLiteTensor {
-  // The data type specification for data stored in `data`. This affects
-  // what member of `data` union should be used.
-  TfLiteType type;
-  // A union of data pointers. The appropriate type should be used for a typed
-  // tensor based on `type`.
-  TfLitePtrUnion data;
-  // A pointer to a structure representing the dimensionality interpretation
-  // that the buffer should have. NOTE: the product of elements of `dims`
-  // and the element datatype size should be equal to `bytes` below.
-  TfLiteIntArray* dims;
-  // Quantization information.
-  TfLiteQuantizationParams params;
-  // How memory is mapped
-  //  kTfLiteMmapRo: Memory mapped read only.
-  //  i.e. weights
-  //  kTfLiteArenaRw: Arena allocated read write memory
-  //  (i.e. temporaries, outputs).
-  TfLiteAllocationType allocation_type;
-  // The number of bytes required to store the data of this Tensor. I.e.
-  // (bytes of each element) * dims[0] * ... * dims[n-1].  For example, if
-  // type is kTfLiteFloat32 and dims = {3, 2} then
-  // bytes = sizeof(float) * 3 * 2 = 4 * 3 * 2 = 24.
-  size_t bytes;
-
-  // An opaque pointer to a tflite::MMapAllocation
-  const void* allocation;
-
-  // Null-terminated name of this tensor.
-  const char* name;
-
-  // The delegate which knows how to handle `buffer_handle`.
-  // WARNING: This is an experimental interface that is subject to change.
-  struct TfLiteDelegate* delegate;
-
-  // An integer buffer handle that can be handled by `delegate`.
-  // The value is valid only when delegate is not null.
-  // WARNING: This is an experimental interface that is subject to change.
-  TfLiteBufferHandle buffer_handle;
-
-  // If the delegate uses its own buffer (e.g. GPU memory), the delegate is
-  // responsible to set data_is_stale to true.
-  // `delegate->CopyFromBufferHandle` can be called to copy the data from
-  // delegate buffer.
-  // WARNING: This is an // experimental interface that is subject to change.
-  bool data_is_stale;
-
-  // True if the tensor is a variable.
-  bool is_variable;
-
-  // Quantization information. Replaces params field above.
-  TfLiteQuantization quantization;
-
-  // Parameters used to encode a sparse tensor.
-  // This is optional. The field is NULL if a tensor is dense.
-  // WARNING: This is an experimental interface that is subject to change.
-  TfLiteSparsity* sparsity;
-
-  // Optional. Encodes shapes with unknown dimensions with -1. This field is
-  // only populated when unknown dimensions exist in a read-write tensor (i.e.
-  // an input or output tensor). (e.g.  `dims` contains [1, 1, 1, 3] and
-  // `dims_signature` contains [1, -1, -1, 3]). Note that this field only
-  // exists when TF_LITE_STATIC_MEMORY is not defined.
-  const TfLiteIntArray* dims_signature;
-} TfLiteTensor;
-
-// A structure representing an instance of a node.
-// This structure only exhibits the inputs, outputs, user defined data and some
-// node properties (like statefulness), not other features like the type.
-typedef struct TfLiteNode {
-  // Inputs to this node expressed as indices into the simulator's tensors.
-  TfLiteIntArray* inputs;
-
-  // Outputs to this node expressed as indices into the simulator's tensors.
-  TfLiteIntArray* outputs;
-
-  // intermediate tensors to this node expressed as indices into the simulator's
-  // tensors.
-  TfLiteIntArray* intermediates;
-
-  // Temporary tensors uses during the computations. This usually contains no
-  // tensors, but ops are allowed to change that if they need scratch space of
-  // any sort.
-  TfLiteIntArray* temporaries;
-
-  // Opaque data provided by the node implementer through `Registration.init`.
-  void* user_data;
-
-  // Opaque data provided to the node if the node is a builtin. This is usually
-  // a structure defined in builtin_op_data.h
-  void* builtin_data;
-
-  // Custom initial data. This is the opaque data provided in the flatbuffer.
-  // WARNING: This is an experimental interface that is subject to change.
-  const void* custom_initial_data;
-  int custom_initial_data_size;
-
-  // The pointer to the delegate. This is non-null only when the node is
-  // created by calling `interpreter.ModifyGraphWithDelegate`.
-  // WARNING: This is an experimental interface that is subject to change.
-  struct TfLiteDelegate* delegate;
-
-  // Whether this op might have side effect (e.g. stateful op).
-  bool might_have_side_effect;
-} TfLiteNode;
-#else   // defined(TF_LITE_STATIC_MEMORY)?
-// NOTE: This flag is opt-in only at compile time.
-//
-// Specific reduced TfLiteTensor struct for TF Micro runtime. This struct
-// contains only the minimum fields required to initialize and prepare a micro
-// inference graph. The fields in this struct have been ordered from
-// largest-to-smallest for optimal struct sizeof.
-//
-// This struct does not use:
-// - allocation
-// - buffer_handle
-// - data_is_stale
-// - delegate
-// - dims_signature
-// - name
-// - sparsity
-typedef struct TfLiteTensor {
-  // TODO(b/155784997): Consider consolidating these quantization fields:
-  // Quantization information. Replaces params field above.
-  TfLiteQuantization quantization;
-
-  // Quantization information.
-  TfLiteQuantizationParams params;
-
-  // A union of data pointers. The appropriate type should be used for a typed
-  // tensor based on `type`.
-  TfLitePtrUnion data;
-
-  // A pointer to a structure representing the dimensionality interpretation
-  // that the buffer should have. NOTE: the product of elements of `dims`
-  // and the element datatype size should be equal to `bytes` below.
-  TfLiteIntArray* dims;
-
-  // The number of bytes required to store the data of this Tensor. I.e.
-  // (bytes of each element) * dims[0] * ... * dims[n-1].  For example, if
-  // type is kTfLiteFloat32 and dims = {3, 2} then
-  // bytes = sizeof(float) * 3 * 2 = 4 * 3 * 2 = 24.
-  size_t bytes;
-
-  // The data type specification for data stored in `data`. This affects
-  // what member of `data` union should be used.
-  TfLiteType type;
-
-  // How memory is mapped
-  //  kTfLiteMmapRo: Memory mapped read only.
-  //  i.e. weights
-  //  kTfLiteArenaRw: Arena allocated read write memory
-  //  (i.e. temporaries, outputs).
-  TfLiteAllocationType allocation_type;
-
-  // True if the tensor is a variable.
-  bool is_variable;
-} TfLiteTensor;
-
-// Specific reduced TfLiteNode struct for TF Micro runtime. This struct contains
-// only the minimum fields required to represent a node.
-//
-// This struct does not use:
-// - delegate
-// - intermediates
-// - temporaries
-typedef struct TfLiteNode {
-  // Inputs to this node expressed as indices into the simulator's tensors.
-  TfLiteIntArray* inputs;
-
-  // Outputs to this node expressed as indices into the simulator's tensors.
-  TfLiteIntArray* outputs;
-
-  // intermediate tensors to this node expressed as indices into the simulator's
-  // tensors.
-  TfLiteIntArray* intermediates;
-
-  // Opaque data provided by the node implementer through `Registration.init`.
-  void* user_data;
-
-  // Opaque data provided to the node if the node is a builtin. This is usually
-  // a structure defined in builtin_op_data.h
-  void* builtin_data;
-
-  // Custom initial data. This is the opaque data provided in the flatbuffer.
-  // WARNING: This is an experimental interface that is subject to change.
-  const void* custom_initial_data;
-  int custom_initial_data_size;
-} TfLiteNode;
-#endif  // TF_LITE_STATIC_MEMORY
-
-// Light-weight tensor struct for TF Micro runtime. Provides the minimal amount
-// of information required for a kernel to run during TfLiteRegistration::Eval.
-// TODO(b/160955687): Move this field into TF_LITE_STATIC_MEMORY when TFLM
-// builds with this flag by default internally.
-typedef struct TfLiteEvalTensor {
-  // A union of data pointers. The appropriate type should be used for a typed
-  // tensor based on `type`.
-  TfLitePtrUnion data;
-
-  // A pointer to a structure representing the dimensionality interpretation
-  // that the buffer should have.
-  TfLiteIntArray* dims;
-
-  // The data type specification for data stored in `data`. This affects
-  // what member of `data` union should be used.
-  TfLiteType type;
-} TfLiteEvalTensor;
-
-#ifndef TF_LITE_STATIC_MEMORY
-// Free data memory of tensor `t`.
-void TfLiteTensorDataFree(TfLiteTensor* t);
-
-// Free quantization data.
-void TfLiteQuantizationFree(TfLiteQuantization* quantization);
-
-// Free sparsity parameters.
-void TfLiteSparsityFree(TfLiteSparsity* sparsity);
-
-// Free memory of tensor `t`.
-void TfLiteTensorFree(TfLiteTensor* t);
-
-// Set all of a tensor's fields (and free any previously allocated data).
-void TfLiteTensorReset(TfLiteType type, const char* name, TfLiteIntArray* dims,
-                       TfLiteQuantizationParams quantization, char* buffer,
-                       size_t size, TfLiteAllocationType allocation_type,
-                       const void* allocation, bool is_variable,
-                       TfLiteTensor* tensor);
-
-// Copies the contents of 'src' in 'dst'.
-// Function does nothing if either 'src' or 'dst' is passed as nullptr and
-// return kTfLiteOk.
-// Returns kTfLiteError if 'src' and 'dst' doesn't have matching data size.
-// Note function copies contents, so it won't create new data pointer
-// or change allocation type.
-// All Tensor related properties will be copied from 'src' to 'dst' like
-// quantization, sparsity, ...
-TfLiteStatus TfLiteTensorCopy(const TfLiteTensor* src, TfLiteTensor* dst);
-
-// Resize the allocated data of a (dynamic) tensor. Tensors with allocation
-// types other than kTfLiteDynamic will be ignored.
-void TfLiteTensorRealloc(size_t num_bytes, TfLiteTensor* tensor);
-#endif  // TF_LITE_STATIC_MEMORY
-
-// WARNING: This is an experimental interface that is subject to change.
-//
-// Currently, TfLiteDelegateParams has to be allocated in a way that it's
-// trivially destructable. It will be stored as `builtin_data` field in
-// `TfLiteNode` of the delegate node.
-//
-// See also the `CreateDelegateParams` function in `interpreter.cc` details.
-typedef struct TfLiteDelegateParams {
-  struct TfLiteDelegate* delegate;
-  TfLiteIntArray* nodes_to_replace;
-  TfLiteIntArray* input_tensors;
-  TfLiteIntArray* output_tensors;
-} TfLiteDelegateParams;
-
-typedef struct TfLiteContext {
-  // Number of tensors in the context.
-  size_t tensors_size;
-
-  // The execution plan contains a list of the node indices in execution
-  // order. execution_plan->size is the current number of nodes. And,
-  // execution_plan->data[0] is the first node that needs to be run.
-  // TfLiteDelegates can traverse the current execution plan by iterating
-  // through each member of this array and using GetNodeAndRegistration() to
-  // access details about a node. i.e.
-  //
-  // TfLiteIntArray* execution_plan;
-  // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &execution_plan));
-  // for (int exec_index = 0; exec_index < execution_plan->size; exec_index++) {
-  //    int node_index = execution_plan->data[exec_index];
-  //    TfLiteNode* node;
-  //    TfLiteRegistration* reg;
-  //    context->GetNodeAndRegistration(context, node_index, &node, &reg);
-  // }
-  // Note: the memory pointed by '`*execution_plan` is OWNED by TfLite runtime.
-  // Future calls to GetExecutionPlan invalidates earlier outputs. The following
-  // code snippet shows the issue of such an invocation pattern. After calling
-  // CheckNode, subsequent access to `plan_1st` is undefined.
-  //
-  // void CheckNode(const TfLiteNode* node) {
-  //   ...
-  //   TfLiteIntArray* plan_2nd;
-  //   TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &plan_2nd));
-  //   ...
-  // }
-  //
-  // TfLiteIntArray* plan_1st;
-  // TF_LITE_ENSURE_STATUS(context->GetExecutionPlan(context, &plan_1st));
-  // for (int exec_index = 0; exec_index < plan_1st->size; exec_index++) {
-  //    int node_index = plan_1st->data[exec_index];
-  //    TfLiteNode* node;
-  //    TfLiteRegistration* reg;
-  //    context->GetNodeAndRegistration(context, node_index, &node, &reg);
-  //    CheckNode(node);
-  // }
-  //
-  // WARNING: This is an experimental interface that is subject to change.
-  TfLiteStatus (*GetExecutionPlan)(struct TfLiteContext* context,
-                                   TfLiteIntArray** execution_plan);
-
-  // An array of tensors in the interpreter context (of length `tensors_size`)
-  TfLiteTensor* tensors;
-
-  // opaque full context ptr (an opaque c++ data structure)
-  void* impl_;
-
-  // Request memory pointer be resized. Updates dimensions on the tensor.
-  // NOTE: ResizeTensor takes ownership of newSize.
-  TfLiteStatus (*ResizeTensor)(struct TfLiteContext*, TfLiteTensor* tensor,
-                               TfLiteIntArray* new_size);
-  // Request that an error be reported with format string msg.
-  void (*ReportError)(struct TfLiteContext*, const char* msg, ...);
-
-  // Add `tensors_to_add` tensors, preserving pre-existing Tensor entries.  If
-  // non-null, the value pointed to by `first_new_tensor_index` will be set to
-  // the index of the first new tensor.
-  TfLiteStatus (*AddTensors)(struct TfLiteContext*, int tensors_to_add,
-                             int* first_new_tensor_index);
-
-  // Get a Tensor node by node_index.
-  // WARNING: This is an experimental interface that is subject to change.
-  TfLiteStatus (*GetNodeAndRegistration)(
-      struct TfLiteContext*, int node_index, TfLiteNode** node,
-      struct TfLiteRegistration** registration);
-
-  // Replace ops with one or more stub delegate operations. This function
-  // does not take ownership of `nodes_to_replace`.
-  TfLiteStatus (*ReplaceNodeSubsetsWithDelegateKernels)(
-      struct TfLiteContext*, struct TfLiteRegistration registration,
-      const TfLiteIntArray* nodes_to_replace, struct TfLiteDelegate* delegate);
-
-  // Number of threads that are recommended to subsystems like gemmlowp and
-  // eigen.
-  int recommended_num_threads;
-
-  // Access external contexts by type.
-  // WARNING: This is an experimental interface that is subject to change.
-  TfLiteExternalContext* (*GetExternalContext)(struct TfLiteContext*,
-                                               TfLiteExternalContextType);
-  // Set the value of a external context. Does not take ownership of the
-  // pointer.
-  // WARNING: This is an experimental interface that is subject to change.
-  void (*SetExternalContext)(struct TfLiteContext*, TfLiteExternalContextType,
-                             TfLiteExternalContext*);
-
-  // Flag for allowing float16 precision for FP32 calculation.
-  // default: false.
-  // WARNING: This is an experimental API and subject to change.
-  bool allow_fp32_relax_to_fp16;
-
-  // Pointer to the op-level profiler, if set; nullptr otherwise.
-  void* profiler;
-
-  // Allocate persistent buffer which has the same life time as the interpreter.
-  // Returns nullptr on failure.
-  // The memory is allocated from heap for TFL, and from tail in TFLM.
-  // This method is only available in Init or Prepare stage.
-  // WARNING: This is an experimental interface that is subject to change.
-  void* (*AllocatePersistentBuffer)(struct TfLiteContext* ctx, size_t bytes);
-
-  // Allocate a buffer which will be deallocated right after invoke phase.
-  // The memory is allocated from heap in TFL, and from volatile arena in TFLM.
-  // This method is only available in invoke stage.
-  // NOTE: If possible use RequestScratchBufferInArena method to avoid memory
-  // allocation during inference time.
-  // WARNING: This is an experimental interface that is subject to change.
-  TfLiteStatus (*AllocateBufferForEval)(struct TfLiteContext* ctx, size_t bytes,
-                                        void** ptr);
-
-  // Request a scratch buffer in the arena through static memory planning.
-  // This method is only available in Prepare stage and the buffer is allocated
-  // by the interpreter between Prepare and Eval stage. In Eval stage,
-  // GetScratchBuffer API can be used to fetch the address.
-  // WARNING: This is an experimental interface that is subject to change.
-  TfLiteStatus (*RequestScratchBufferInArena)(struct TfLiteContext* ctx,
-                                              size_t bytes, int* buffer_idx);
-
-  // Get the scratch buffer pointer.
-  // This method is only available in Eval stage.
-  // WARNING: This is an experimental interface that is subject to change.
-  void* (*GetScratchBuffer)(struct TfLiteContext* ctx, int buffer_idx);
-
-  // Resize the memory pointer of the `tensor`. This method behaves the same as
-  // `ResizeTensor`, except that it makes a copy of the shape array internally
-  // so the shape array could be deallocated right afterwards.
-  // WARNING: This is an experimental interface that is subject to change.
-  TfLiteStatus (*ResizeTensorExplicit)(struct TfLiteContext* ctx,
-                                       TfLiteTensor* tensor, int dims,
-                                       const int* shape);
-
-  // This method provides a preview of post-delegation partitioning. Each
-  // TfLiteDelegateParams in the referenced array corresponds to one instance of
-  // the delegate kernel.
-  // Example usage:
-  //
-  // TfLiteIntArray* nodes_to_replace = ...;
-  // TfLiteDelegateParams* params_array;
-  // int num_partitions = 0;
-  // TF_LITE_ENSURE_STATUS(context->PreviewDelegatePartitioning(
-  //    context, delegate, nodes_to_replace, &params_array, &num_partitions));
-  // for (int idx = 0; idx < num_partitions; idx++) {
-  //    const auto& partition_params = params_array[idx];
-  //    ...
-  // }
-  //
-  // NOTE: The context owns the memory referenced by partition_params_array. It
-  // will be cleared with another call to PreviewDelegateParitioning, or after
-  // TfLiteDelegateParams::Prepare returns.
-  //
-  // WARNING: This is an experimental interface that is subject to change.
-  TfLiteStatus (*PreviewDelegatePartitioning)(
-      struct TfLiteContext* context, const TfLiteIntArray* nodes_to_replace,
-      TfLiteDelegateParams** partition_params_array, int* num_partitions);
-
-  // Returns a TfLiteTensor struct for a given index.
-  // WARNING: This is an experimental interface that is subject to change.
-  // WARNING: This method may not be available on all platforms.
-  TfLiteTensor* (*GetTensor)(const struct TfLiteContext* context,
-                             int tensor_idx);
-
-  // Returns a TfLiteEvalTensor struct for a given index.
-  // WARNING: This is an experimental interface that is subject to change.
-  // WARNING: This method may not be available on all platforms.
-  TfLiteEvalTensor* (*GetEvalTensor)(const struct TfLiteContext* context,
-                                     int tensor_idx);
-
-  // Retrieves named metadata buffer from the TFLite model.
-  // Returns kTfLiteOk if metadata is successfully obtained from the flatbuffer
-  // Model: that is, there exists a `metadata` entry with given `name` string.
-  // (see TFLite's schema.fbs).
-  // The corresponding `buffer` information is populated in `ptr` & `bytes`.
-  // The data from `ptr` is valid for the lifetime of the Interpreter.
-  //
-  // WARNING: This is an experimental interface that is subject to change.
-  TfLiteStatus (*GetModelMetadata)(const struct TfLiteContext* context,
-                                   const char* name, const char** ptr,
-                                   size_t* bytes);
-} TfLiteContext;
-
-// `TfLiteRegistrationExternal` is an external version of `TfLiteRegistration`
-// for C API which doesn't use internal types (such as `TfLiteContext`) but only
-// uses stable API types (such as `TfLiteOpaqueContext`). The purpose of each
-// field is the exactly the same as with `TfLiteRegistration`.
-typedef struct TfLiteRegistrationExternal TfLiteRegistrationExternal;
-
-typedef struct TfLiteRegistration {
-  // Initializes the op from serialized data.
-  // Called only *once* for the lifetime of the op, so any one-time allocations
-  // should be made here (unless they depend on tensor sizes).
-  //
-  // If a built-in op:
-  //   `buffer` is the op's params data (TfLiteLSTMParams*).
-  //   `length` is zero.
-  // If custom op:
-  //   `buffer` is the op's `custom_options`.
-  //   `length` is the size of the buffer.
-  //
-  // Returns a type-punned (i.e. void*) opaque data (e.g. a primitive pointer
-  // or an instance of a struct).
-  //
-  // The returned pointer will be stored with the node in the `user_data` field,
-  // accessible within prepare and invoke functions below.
-  // NOTE: if the data is already in the desired format, simply implement this
-  // function to return `nullptr` and implement the free function to be a no-op.
-  void* (*init)(TfLiteContext* context, const char* buffer, size_t length);
-
-  // The pointer `buffer` is the data previously returned by an init invocation.
-  void (*free)(TfLiteContext* context, void* buffer);
-
-  // prepare is called when the inputs this node depends on have been resized.
-  // context->ResizeTensor() can be called to request output tensors to be
-  // resized.
-  // Can be called multiple times for the lifetime of the op.
-  //
-  // Returns kTfLiteOk on success.
-  TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node);
-
-  // Execute the node (should read node->inputs and output to node->outputs).
-  // Returns kTfLiteOk on success.
-  TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node);
-
-  // profiling_string is called during summarization of profiling information
-  // in order to group executions together. Providing a value here will cause a
-  // given op to appear multiple times is the profiling report. This is
-  // particularly useful for custom ops that can perform significantly
-  // different calculations depending on their `user-data`.
-  const char* (*profiling_string)(const TfLiteContext* context,
-                                  const TfLiteNode* node);
-
-  // Builtin codes. If this kernel refers to a builtin this is the code
-  // of the builtin. This is so we can do marshaling to other frameworks like
-  // NN API.
-  // Note: It is the responsibility of the registration binder to set this
-  // properly.
-  int32_t builtin_code;
-
-  // Custom op name. If the op is a builtin, this will be null.
-  // Note: It is the responsibility of the registration binder to set this
-  // properly.
-  // WARNING: This is an experimental interface that is subject to change.
-  const char* custom_name;
-
-  // The version of the op.
-  // Note: It is the responsibility of the registration binder to set this
-  // properly.
-  int version;
-
-  // The external version of `TfLiteRegistration`. Since we can't use internal
-  // types (such as `TfLiteContext`) for C API to maintain ABI stability.
-  // C API user will provide `TfLiteRegistrationExternal` to implement custom
-  // ops. We keep it inside of `TfLiteRegistration` and use it to route
-  // callbacks properly.
-  TfLiteRegistrationExternal* registration_external;
-} TfLiteRegistration;
-
-// Old version of `TfLiteRegistration` to maintain binary backward
-// compatibility.
-// WARNING: This structure is deprecated / not an official part of the API.
-// It should be only used for binary backward compatibility.
-typedef struct TfLiteRegistration_V1 {
-  void* (*init)(TfLiteContext* context, const char* buffer, size_t length);
-  void (*free)(TfLiteContext* context, void* buffer);
-  TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node);
-  TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node);
-  const char* (*profiling_string)(const TfLiteContext* context,
-                                  const TfLiteNode* node);
-  int32_t builtin_code;
-  const char* custom_name;
-  int version;
-} TfLiteRegistration_V1;
-
-// The flags used in `TfLiteDelegate`. Note that this is a bitmask, so the
-// values should be 1, 2, 4, 8, ...etc.
-typedef enum TfLiteDelegateFlags {
-  kTfLiteDelegateFlagsNone = 0,
-  // The flag is set if the delegate can handle dynamic sized tensors.
-  // For example, the output shape of a `Resize` op with non-constant shape
-  // can only be inferred when the op is invoked.
-  // In this case, the Delegate is responsible for calling
-  // `SetTensorToDynamic` to mark the tensor as a dynamic tensor, and calling
-  // `ResizeTensor` when invoking the op.
-  //
-  // If the delegate isn't capable to handle dynamic tensors, this flag need
-  // to be set to false.
-  kTfLiteDelegateFlagsAllowDynamicTensors = 1,
-
-  // This flag can be used by delegates (that allow dynamic tensors) to ensure
-  // applicable tensor shapes are automatically propagated in the case of tensor
-  // resizing.
-  // This means that non-dynamic (allocation_type != kTfLiteDynamic) I/O tensors
-  // of a delegate kernel will have correct shapes before its Prepare() method
-  // is called. The runtime leverages TFLite builtin ops in the original
-  // execution plan to propagate shapes.
-  //
-  // A few points to note:
-  // 1. This requires kTfLiteDelegateFlagsAllowDynamicTensors. If that flag is
-  // false, this one is redundant since the delegate kernels are re-initialized
-  // every time tensors are resized.
-  // 2. Enabling this flag adds some overhead to AllocateTensors(), since extra
-  // work is required to prepare the original execution plan.
-  // 3. This flag requires that the original execution plan only have ops with
-  // valid registrations (and not 'dummy' custom ops like with Flex).
-  // WARNING: This feature is experimental and subject to change.
-  kTfLiteDelegateFlagsRequirePropagatedShapes = 2
-} TfLiteDelegateFlags;
-
-// WARNING: This is an experimental interface that is subject to change.
-typedef struct TfLiteDelegate {
-  // Data that delegate needs to identify itself. This data is owned by the
-  // delegate. The delegate is owned in the user code, so the delegate is
-  // responsible for deallocating this when it is destroyed.
-  void* data_;
-
-  // Invoked by ModifyGraphWithDelegate. This prepare is called, giving the
-  // delegate a view of the current graph through TfLiteContext*. It typically
-  // will look at the nodes and call ReplaceNodeSubsetsWithDelegateKernels()
-  // to ask the TensorFlow lite runtime to create macro-nodes to represent
-  // delegated subgraphs of the original graph.
-  TfLiteStatus (*Prepare)(TfLiteContext* context,
-                          struct TfLiteDelegate* delegate);
-
-  // Copy the data from delegate buffer handle into raw memory of the given
-  // 'tensor'. Note that the delegate is allowed to allocate the raw bytes as
-  // long as it follows the rules for kTfLiteDynamic tensors, in which case this
-  // cannot be null.
-  TfLiteStatus (*CopyFromBufferHandle)(TfLiteContext* context,
-                                       struct TfLiteDelegate* delegate,
-                                       TfLiteBufferHandle buffer_handle,
-                                       TfLiteTensor* tensor);
-
-  // Copy the data from raw memory of the given 'tensor' to delegate buffer
-  // handle. This can be null if the delegate doesn't use its own buffer.
-  TfLiteStatus (*CopyToBufferHandle)(TfLiteContext* context,
-                                     struct TfLiteDelegate* delegate,
-                                     TfLiteBufferHandle buffer_handle,
-                                     TfLiteTensor* tensor);
-
-  // Free the Delegate Buffer Handle. Note: This only frees the handle, but
-  // this doesn't release the underlying resource (e.g. textures). The
-  // resources are either owned by application layer or the delegate.
-  // This can be null if the delegate doesn't use its own buffer.
-  void (*FreeBufferHandle)(TfLiteContext* context,
-                           struct TfLiteDelegate* delegate,
-                           TfLiteBufferHandle* handle);
-
-  // Bitmask flags. See the comments in `TfLiteDelegateFlags`.
-  int64_t flags;
-
-  // The opaque delegate builder associated with this object.  If set then the
-  // TF Lite runtime will give precedence to this field.  E.g. instead of
-  // invoking 'Prepare' via the function pointer inside the 'TfLiteDelegate'
-  // object, the runtime will first check if the corresponding function
-  // pointer inside 'opaque_delegate_builder' is set and if so invoke that.
-  //
-  // If this field is non-null, then the 'Prepare' field (of the
-  // 'TfLiteDelegate') should be null.
-  struct TfLiteOpaqueDelegateBuilder* opaque_delegate_builder;
-} TfLiteDelegate;
-
-// Build a 'null' delegate, with all the fields properly set to their default
-// values.
-TfLiteDelegate TfLiteDelegateCreate(void);
-
-// `TfLiteOpaqueDelegateBuilder` is used for constructing
-// `TfLiteOpaqueDelegateStruct`, see `TfLiteOpaqueDelegateCreate` below.  Note:
-// This struct is not ABI stable.
-//
-// For forward source compatibility `TfLiteOpaqueDelegateBuilder` objects should
-// be brace-initialized, so that all fields (including any that might be added
-// in the future) get zero-initialized.  The purpose of each field is exactly
-// the same as with `TfLiteDelegate`.
-//
-// WARNING: This is an experimental interface that is subject to change.
-typedef struct TfLiteOpaqueDelegateBuilder {
-  // Data that delegate needs to identify itself. This data is owned by the
-  // delegate. The delegate is owned in the user code, so the delegate is
-  // responsible for deallocating this when it is destroyed.
-  void* data;
-  // Invoked by ModifyGraphWithDelegate. This prepare is called, giving the
-  // delegate a view of the current graph through TfLiteContext*. It typically
-  // will look at the nodes and call ReplaceNodeSubsetsWithDelegateKernels()
-  // to ask the TensorFlow lite runtime to create macro-nodes to represent
-  // delegated subgraphs of the original graph.
-  TfLiteStatus (*Prepare)(TfLiteOpaqueContext* context,  // NOLINT
-                          struct TfLiteOpaqueDelegateStruct* delegate,
-                          void* data);
-  // Copies the data from delegate buffer handle into raw memory of the given
-  // 'tensor'. Note that the delegate is allowed to allocate the raw bytes as
-  // long as it follows the rules for kTfLiteDynamic tensors, in which case this
-  // cannot be null.
-  TfLiteStatus (*CopyFromBufferHandle)(  // NOLINT
-      TfLiteOpaqueContext* context, struct TfLiteOpaqueDelegateStruct* delegate,
-      void* data, TfLiteBufferHandle buffer_handle, TfLiteOpaqueTensor* tensor);
-  // Copies the data from raw memory of the given 'tensor' to delegate buffer
-  // handle. This can be null if the delegate doesn't use its own buffer.
-  TfLiteStatus (*CopyToBufferHandle)(  // NOLINT
-      TfLiteOpaqueContext* context, struct TfLiteOpaqueDelegateStruct* delegate,
-      void* data, TfLiteBufferHandle buffer_handle, TfLiteOpaqueTensor* tensor);
-  // Frees the Delegate Buffer Handle. Note: This only frees the handle, but
-  // this doesn't release the underlying resource (e.g. textures). The
-  // resources are either owned by application layer or the delegate.
-  // This can be null if the delegate doesn't use its own buffer.
-  void (*FreeBufferHandle)(TfLiteOpaqueContext* context,  // NOLINT
-                           struct TfLiteOpaqueDelegateStruct* delegate,
-                           void* data, TfLiteBufferHandle* handle);
-  // Bitmask flags. See the comments in `TfLiteDelegateFlags`.
-  int64_t flags;
-} TfLiteOpaqueDelegateBuilder;
-
-// Creates an opaque delegate and returns its address.  The opaque delegate will
-// behave according to the provided 'opaque_delegate_builder'.  The lifetime of
-// the fields within the 'opaque_delegate_builder' must outlive any interaction
-// between the runtime and the returned 'TfLiteOpaqueDelegateStruct'.  The
-// returned address should be passed to 'TfLiteOpaqueDelegateDelete' for
-// deletion.  If 'opaque_delegate_builder' is a null pointer, then a null
-// pointer will be returned.
-struct TfLiteOpaqueDelegateStruct* TfLiteOpaqueDelegateCreate(
-    const TfLiteOpaqueDelegateBuilder* opaque_delegate_builder);
-
-// Deletes the provided opaque 'delegate'.  This function has no effect if the
-// 'delegate' is a null pointer.
-void TfLiteOpaqueDelegateDelete(
-    const struct TfLiteOpaqueDelegateStruct* delegate);
-
-#ifdef __cplusplus
-}  // extern "C"
-#endif  // __cplusplus
-#endif  // TENSORFLOW_LITE_C_COMMON_H_

code/components/tflite-lib/tensorflow/lite/context_util.h

@@ -1,53 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-/// \file
-/// This provides a few C++ helpers that are useful for manipulating C
-/// structures in C++.
-#ifndef TENSORFLOW_LITE_CONTEXT_UTIL_H_
-#define TENSORFLOW_LITE_CONTEXT_UTIL_H_
-
-#include <stddef.h>
-
-#include "tensorflow/lite/c/common.h"
-
-namespace tflite {
-
-/// Provides a range iterable wrapper for TfLiteIntArray* (C lists) that TfLite
-/// C api uses.
-// Can't use the google array_view, since we can't depend on even
-// absl for embedded device reasons.
-class TfLiteIntArrayView {
- public:
-  /// Construct a view of a TfLiteIntArray*. Note, `int_array` should be
-  /// non-null and this view does not take ownership of it.
-  explicit TfLiteIntArrayView(const TfLiteIntArray* int_array)
-      : int_array_(int_array) {}
-
-  TfLiteIntArrayView(const TfLiteIntArrayView&) = default;
-  TfLiteIntArrayView& operator=(const TfLiteIntArrayView& rhs) = default;
-
-  typedef const int* const_iterator;
-  const_iterator begin() const { return int_array_->data; }
-  const_iterator end() const { return &int_array_->data[int_array_->size]; }
-  size_t size() const { return end() - begin(); }
-  int operator[](size_t pos) const { return int_array_->data[pos]; }
-
- private:
-  const TfLiteIntArray* int_array_;
-};
-
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_CONTEXT_UTIL_H_

code/components/tflite-lib/tensorflow/lite/core/api/flatbuffer_conversions.cc

@@ -1,2469 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/core/api/flatbuffer_conversions.h"
-
-#include <cstddef>
-#include <cstdint>
-#include <memory>
-
-#include "flatbuffers/flatbuffers.h"  // from @flatbuffers
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/core/api/error_reporter.h"
-#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-
-namespace tflite {
-
-namespace {
-
-// Utility class for safely allocating POD data. This is useful for avoiding
-// leaks in cases where op params are allocated but fail to propagate to the
-// parsed op data (e.g., when model parameters are invalid).
-class SafeBuiltinDataAllocator {
- public:
-  class BuiltinDataDeleter {
-   public:
-    explicit BuiltinDataDeleter(BuiltinDataAllocator* allocator)
-        : allocator_(allocator) {}
-
-    void operator()(void* data) { allocator_->Deallocate(data); }
-
-   private:
-    BuiltinDataAllocator* allocator_;
-  };
-
-  template <typename T>
-  using BuiltinDataPtr = std::unique_ptr<T, BuiltinDataDeleter>;
-
-  explicit SafeBuiltinDataAllocator(BuiltinDataAllocator* allocator)
-      : allocator_(allocator) {}
-
-  template <typename T>
-  BuiltinDataPtr<T> Allocate() {
-    return BuiltinDataPtr<T>(allocator_->AllocatePOD<T>(),
-                             BuiltinDataDeleter(allocator_));
-  }
-
- private:
-  BuiltinDataAllocator* allocator_;
-};
-
-// All the Parse functions take some pointers as params and this function has
-// the common DCHECKs to catch if any of those are nullptr.
-void CheckParsePointerParams(const Operator* op, ErrorReporter* error_reporter,
-                             BuiltinDataAllocator* allocator,
-                             void** builtin_data) {
-  TFLITE_DCHECK(op != nullptr);
-  TFLITE_DCHECK(error_reporter != nullptr);
-  TFLITE_DCHECK(allocator != nullptr);
-  TFLITE_DCHECK(builtin_data != nullptr);
-}
-
-// Copies the contents from the flatbuffer int vector `flatbuffer` into the
-// int array `buffer`. `flat_vector` and `buffer` represent the same
-// configuration operation for a given operation.
-TfLiteStatus FlatBufferIntVectorToArray(
-    int max_size_of_buffer, const flatbuffers::Vector<int32_t>* flat_vector,
-    int* buffer, ErrorReporter* error_reporter, const char* op_name) {
-  if (!flat_vector) {
-    TF_LITE_REPORT_ERROR(error_reporter,
-                         "Input array not provided for operation '%s'.\n",
-                         op_name);
-    return kTfLiteError;
-  } else {
-    size_t num_dimensions = flat_vector->size();
-    if (num_dimensions > max_size_of_buffer / sizeof(int)) {
-      TF_LITE_REPORT_ERROR(
-          error_reporter,
-          "Found too many dimensions in the input array of operation '%s'.\n",
-          op_name);
-      return kTfLiteError;
-    } else {
-      for (size_t i = 0; i < num_dimensions; ++i) {
-        buffer[i] = flat_vector->Get(i);
-      }
-    }
-  }
-  return kTfLiteOk;
-}
-
-// Converts the flatbuffer activation to what is used at runtime.
-TfLiteFusedActivation ConvertActivation(ActivationFunctionType activation) {
-  switch (activation) {
-    case ActivationFunctionType_NONE:
-      return kTfLiteActNone;
-    case ActivationFunctionType_RELU:
-      return kTfLiteActRelu;
-    case ActivationFunctionType_RELU_N1_TO_1:
-      return kTfLiteActReluN1To1;
-    case ActivationFunctionType_RELU6:
-      return kTfLiteActRelu6;
-    case ActivationFunctionType_TANH:
-      return kTfLiteActTanh;
-    case ActivationFunctionType_SIGN_BIT:
-      return kTfLiteActSignBit;
-  }
-  return kTfLiteActNone;
-}
-
-// Converts the flatbuffer padding enum to what is used at runtime.
-TfLitePadding ConvertPadding(Padding padding) {
-  switch (padding) {
-    case Padding_SAME:
-      return kTfLitePaddingSame;
-    case Padding_VALID:
-      return kTfLitePaddingValid;
-  }
-  return kTfLitePaddingUnknown;
-}
-
-// Converts the flatbuffer mirror padding enum to what is used at runtime.
-TfLiteMirrorPaddingMode ConvertMirrorPadding(MirrorPadMode padding) {
-  switch (padding) {
-    case MirrorPadMode_REFLECT:
-      return kTfLiteMirrorPaddingReflect;
-    case MirrorPadMode_SYMMETRIC:
-      return kTfLiteMirrorPaddingSymmetric;
-  }
-  return kTfLiteMirrorPaddingUnknown;
-}
-
-#ifndef TF_LITE_STATIC_MEMORY
-TfLiteStatus ParseOpDataTfLite(const Operator* op, BuiltinOperator op_type,
-                               ErrorReporter* error_reporter,
-                               BuiltinDataAllocator* allocator,
-                               void** builtin_data) {
-  auto parseLSHProjectionType = [](LSHProjectionType type) {
-    switch (type) {
-      case LSHProjectionType_SPARSE:
-        return kTfLiteLshProjectionSparse;
-      case LSHProjectionType_DENSE:
-        return kTfLiteLshProjectionDense;
-      default:
-        return kTfLiteLshProjectionUnknown;
-    }
-  };
-  auto parseCombinerType = [](CombinerType type) {
-    switch (type) {
-      case CombinerType_MEAN:
-        return kTfLiteCombinerTypeMean;
-      case CombinerType_SQRTN:
-        return kTfLiteCombinerTypeSqrtn;
-      case CombinerType_SUM:
-      default:
-        return kTfLiteCombinerTypeSum;
-    }
-  };
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  *builtin_data = nullptr;
-  switch (op_type) {
-    case BuiltinOperator_ABS: {
-      return ParseAbs(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_ADD: {
-      return ParseAdd(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_ADD_N: {
-      return ParseAddN(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_ARG_MAX: {
-      return ParseArgMax(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_ARG_MIN: {
-      return ParseArgMin(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_ASSIGN_VARIABLE: {
-      return ParseAssignVariable(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_AVERAGE_POOL_2D: {
-      return ParsePool(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_BATCH_MATMUL: {
-      return ParseBatchMatMul(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_BATCH_TO_SPACE_ND: {
-      return ParseBatchToSpaceNd(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_BROADCAST_ARGS: {
-      return ParseBroadcastArgs(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_BROADCAST_TO: {
-      return ParseBroadcastTo(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_CALL_ONCE: {
-      return ParseCallOnce(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_CEIL: {
-      return ParseCeil(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_CONCATENATION: {
-      return ParseConcatenation(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_CONV_2D: {
-      return ParseConv2D(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_CUMSUM: {
-      return ParseCumsum(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_DEPTH_TO_SPACE: {
-      return ParseDepthToSpace(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_DEPTHWISE_CONV_2D: {
-      return ParseDepthwiseConv2D(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_DEQUANTIZE: {
-      return ParseDequantize(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_DIV: {
-      return ParseDiv(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_ELU: {
-      return ParseElu(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_EXP: {
-      return ParseExp(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_EXPAND_DIMS: {
-      return ParseExpandDims(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_FILL: {
-      return ParseFill(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_FLOOR: {
-      return ParseFloor(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_FLOOR_DIV: {
-      return ParseFloorDiv(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_FLOOR_MOD: {
-      return ParseFloorMod(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_FULLY_CONNECTED: {
-      return ParseFullyConnected(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_GATHER_ND: {
-      return ParseGatherNd(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_GREATER: {
-      return ParseGreater(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_GREATER_EQUAL: {
-      return ParseGreaterEqual(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_HARD_SWISH: {
-      return ParseHardSwish(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_L2_NORMALIZATION: {
-      return ParseL2Normalization(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_L2_POOL_2D: {
-      return ParsePool(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_LEAKY_RELU: {
-      return ParseLeakyRelu(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_LESS: {
-      return ParseLess(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_LESS_EQUAL: {
-      return ParseLessEqual(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_LOG: {
-      return ParseLog(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_LOGICAL_AND: {
-      return ParseLogicalAnd(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_LOGICAL_NOT: {
-      return ParseLogicalNot(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_LOGICAL_OR: {
-      return ParseLogicalOr(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_LOGISTIC: {
-      return ParseLogistic(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_LOG_SOFTMAX: {
-      return ParseLogSoftmax(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_LSTM: {
-      return ParseLSTM(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_MAXIMUM: {
-      return ParseMaximum(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_MAX_POOL_2D: {
-      return ParsePool(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_MIRROR_PAD: {
-      return ParseMirrorPad(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_MEAN: {
-      return ParseReducer(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_MINIMUM: {
-      return ParseMinimum(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_MUL: {
-      return ParseMul(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_NEG: {
-      return ParseNeg(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_NOT_EQUAL: {
-      return ParseNotEqual(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_PACK: {
-      return ParsePack(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_PAD: {
-      return ParsePad(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_PADV2: {
-      return ParsePadV2(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_POW: {
-      return ParsePow(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_PRELU: {
-      return ParsePrelu(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_QUANTIZE: {
-      return ParseQuantize(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_READ_VARIABLE: {
-      return ParseReadVariable(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_REDUCE_ANY: {
-      return ParseReducer(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_REDUCE_ALL: {
-      return ParseReducer(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_REDUCE_MAX: {
-      return ParseReducer(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_REDUCE_MIN: {
-      return ParseReducer(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_REDUCE_PROD: {
-      return ParseReducer(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_RELU: {
-      return ParseRelu(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_RELU6: {
-      return ParseRelu6(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_RESHAPE: {
-      return ParseReshape(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_RESIZE_BILINEAR: {
-      return ParseResizeBilinear(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_RESIZE_NEAREST_NEIGHBOR: {
-      return ParseResizeNearestNeighbor(op, error_reporter, allocator,
-                                        builtin_data);
-    }
-
-    case BuiltinOperator_ROUND: {
-      return ParseRound(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_RSQRT: {
-      return ParseRsqrt(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SELECT_V2: {
-      return ParseSelectV2(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SHAPE: {
-      return ParseShape(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SIN: {
-      return ParseSin(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SOFTMAX: {
-      return ParseSoftmax(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SPACE_TO_BATCH_ND: {
-      return ParseSpaceToBatchNd(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SPACE_TO_DEPTH: {
-      return ParseSpaceToDepth(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SPLIT: {
-      return ParseSplit(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SPLIT_V: {
-      return ParseSplitV(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SQRT: {
-      return ParseSqrt(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SQUARE: {
-      return ParseSquare(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SQUARED_DIFFERENCE: {
-      return ParseSquaredDifference(op, error_reporter, allocator,
-                                    builtin_data);
-    }
-
-    case BuiltinOperator_SQUEEZE: {
-      return ParseSqueeze(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_STRIDED_SLICE: {
-      return ParseStridedSlice(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SUB: {
-      return ParseSub(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SUM: {
-      return ParseReducer(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_SVDF: {
-      return ParseSvdf(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_TANH: {
-      return ParseTanh(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_TRANSPOSE_CONV: {
-      return ParseTransposeConv(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_UNPACK: {
-      return ParseUnpack(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_VAR_HANDLE: {
-      return ParseVarHandle(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_ZEROS_LIKE: {
-      return ParseZerosLike(op, error_reporter, allocator, builtin_data);
-    }
-
-    case BuiltinOperator_CAST: {
-      return ParseCast(op, error_reporter, allocator, builtin_data);
-    }
-    case BuiltinOperator_LSH_PROJECTION: {
-      auto params = safe_allocator.Allocate<TfLiteLSHProjectionParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* lshParams =
-              op->builtin_options_as_LSHProjectionOptions()) {
-        params->type = parseLSHProjectionType(lshParams->type());
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN: {
-      auto params = safe_allocator.Allocate<TfLiteSequenceRNNParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* sequence_rnn_params =
-              op->builtin_options_as_SequenceRNNOptions()) {
-        params->activation =
-            ConvertActivation(sequence_rnn_params->fused_activation_function());
-        params->time_major = sequence_rnn_params->time_major();
-        params->asymmetric_quantize_inputs =
-            sequence_rnn_params->asymmetric_quantize_inputs();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN: {
-      auto params =
-          safe_allocator.Allocate<TfLiteBidirectionalSequenceRNNParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* bidi_sequence_rnn_params =
-              op->builtin_options_as_BidirectionalSequenceRNNOptions()) {
-        params->activation = ConvertActivation(
-            bidi_sequence_rnn_params->fused_activation_function());
-        params->time_major = bidi_sequence_rnn_params->time_major();
-        params->merge_outputs = bidi_sequence_rnn_params->merge_outputs();
-        params->asymmetric_quantize_inputs =
-            bidi_sequence_rnn_params->asymmetric_quantize_inputs();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_RNN: {
-      auto params = safe_allocator.Allocate<TfLiteRNNParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* rnn_params = op->builtin_options_as_RNNOptions()) {
-        params->activation =
-            ConvertActivation(rnn_params->fused_activation_function());
-        params->asymmetric_quantize_inputs =
-            rnn_params->asymmetric_quantize_inputs();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_EMBEDDING_LOOKUP_SPARSE: {
-      auto params =
-          safe_allocator.Allocate<TfLiteEmbeddingLookupSparseParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* embedding_params =
-              op->builtin_options_as_EmbeddingLookupSparseOptions()) {
-        params->combiner = parseCombinerType(embedding_params->combiner());
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-
-    case BuiltinOperator_HASHTABLE_LOOKUP:
-      // no-op.
-      return kTfLiteOk;
-
-    case BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION: {
-      auto params = safe_allocator.Allocate<TfLiteLocalResponseNormParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* schema_params =
-              op->builtin_options_as_LocalResponseNormalizationOptions()) {
-        params->radius = schema_params->radius();
-        params->bias = schema_params->bias();
-        params->alpha = schema_params->alpha();
-        params->beta = schema_params->beta();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM: {
-      return ParseUnidirectionalSequenceLSTM(op, error_reporter, allocator,
-                                             builtin_data);
-    }
-    case BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM: {
-      auto params =
-          safe_allocator.Allocate<TfLiteBidirectionalSequenceLSTMParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* bidi_lstm_params =
-              op->builtin_options_as_BidirectionalSequenceLSTMOptions()) {
-        params->activation =
-            ConvertActivation(bidi_lstm_params->fused_activation_function());
-        params->cell_clip = bidi_lstm_params->cell_clip();
-        params->proj_clip = bidi_lstm_params->proj_clip();
-        params->merge_outputs = bidi_lstm_params->merge_outputs();
-        params->time_major = bidi_lstm_params->time_major();
-        params->asymmetric_quantize_inputs =
-            bidi_lstm_params->asymmetric_quantize_inputs();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_SKIP_GRAM: {
-      auto params = safe_allocator.Allocate<TfLiteSkipGramParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* skip_gram_params =
-              op->builtin_options_as_SkipGramOptions()) {
-        params->ngram_size = skip_gram_params->ngram_size();
-        params->max_skip_size = skip_gram_params->max_skip_size();
-        params->include_all_ngrams = skip_gram_params->include_all_ngrams();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-
-    case BuiltinOperator_GATHER: {
-      return ParseGather(op, error_reporter, allocator, builtin_data);
-    }
-    case BuiltinOperator_SPARSE_TO_DENSE: {
-      auto params = safe_allocator.Allocate<TfLiteSparseToDenseParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* sparse_to_dense_params =
-              op->builtin_options_as_SparseToDenseOptions()) {
-        params->validate_indices = sparse_to_dense_params->validate_indices();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_DELEGATE: {
-      TF_LITE_REPORT_ERROR(error_reporter,
-                           "DELEGATE op shouldn't exist in model.");
-      return kTfLiteError;
-    }
-    case BuiltinOperator_FAKE_QUANT: {
-      auto params = safe_allocator.Allocate<TfLiteFakeQuantParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* schema_params =
-              op->builtin_options_as_FakeQuantOptions()) {
-        params->min = schema_params->min();
-        params->max = schema_params->max();
-        params->num_bits = schema_params->num_bits();
-        params->narrow_range = schema_params->narrow_range();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_ONE_HOT: {
-      auto params = safe_allocator.Allocate<TfLiteOneHotParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* schema_params = op->builtin_options_as_OneHotOptions()) {
-        params->axis = schema_params->axis();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_UNIQUE: {
-      auto params = safe_allocator.Allocate<TfLiteUniqueParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      const auto* unique_params = op->builtin_options_as_UniqueOptions();
-      if (unique_params != nullptr) {
-        params->index_out_type =
-            unique_params->idx_out_type() == tflite::TensorType_INT64
-                ? TfLiteType::kTfLiteInt64
-                : TfLiteType::kTfLiteInt32;
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_REVERSE_SEQUENCE: {
-      auto params = safe_allocator.Allocate<TfLiteReverseSequenceParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* reverse_seq_params =
-              op->builtin_options_as_ReverseSequenceOptions()) {
-        params->seq_dim = reverse_seq_params->seq_dim();
-        params->batch_dim = reverse_seq_params->batch_dim();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_IF: {
-      auto params = safe_allocator.Allocate<TfLiteIfParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* if_params = op->builtin_options_as_IfOptions()) {
-        params->then_subgraph_index = if_params->then_subgraph_index();
-        params->else_subgraph_index = if_params->else_subgraph_index();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_WHILE: {
-      auto params = safe_allocator.Allocate<TfLiteWhileParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* while_params = op->builtin_options_as_WhileOptions()) {
-        params->cond_subgraph_index = while_params->cond_subgraph_index();
-        params->body_subgraph_index = while_params->body_subgraph_index();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_CONV_3D:
-    case BuiltinOperator_CONV_3D_TRANSPOSE: {
-      auto params = safe_allocator.Allocate<TfLiteConv3DParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* conv3d_params = op->builtin_options_as_Conv3DOptions()) {
-        params->padding = ConvertPadding(conv3d_params->padding());
-        params->activation =
-            ConvertActivation(conv3d_params->fused_activation_function());
-        params->stride_depth = conv3d_params->stride_d();
-        params->stride_height = conv3d_params->stride_h();
-        params->stride_width = conv3d_params->stride_w();
-        params->dilation_depth_factor = conv3d_params->dilation_d_factor();
-        params->dilation_height_factor = conv3d_params->dilation_h_factor();
-        params->dilation_width_factor = conv3d_params->dilation_w_factor();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_HASHTABLE: {
-      auto params = safe_allocator.Allocate<TfLiteHashtableParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* hashtable_params =
-              op->builtin_options_as_HashtableOptions()) {
-        params->table_id = hashtable_params->table_id();
-        TF_LITE_ENSURE_STATUS(ConvertTensorType(
-            hashtable_params->key_dtype(), &params->key_dtype, error_reporter));
-        TF_LITE_ENSURE_STATUS(ConvertTensorType(hashtable_params->value_dtype(),
-                                                &params->value_dtype,
-                                                error_reporter));
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_MULTINOMIAL: {
-      auto params = safe_allocator.Allocate<TfLiteRandomParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* multinomial_params =
-              op->builtin_options_as_RandomOptions()) {
-        params->seed = multinomial_params->seed();
-        params->seed2 = multinomial_params->seed2();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_RANDOM_STANDARD_NORMAL: {
-      auto params = safe_allocator.Allocate<TfLiteRandomParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* random_std_normal_params =
-              op->builtin_options_as_RandomOptions()) {
-        params->seed = random_std_normal_params->seed();
-        params->seed2 = random_std_normal_params->seed2();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_BUCKETIZE: {
-      auto params = safe_allocator.Allocate<TfLiteBucketizeParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* bucketize_params =
-              op->builtin_options_as_BucketizeOptions()) {
-        const flatbuffers::Vector<float>* boundaries =
-            bucketize_params->boundaries();
-        if (boundaries == nullptr) {
-          TF_LITE_REPORT_ERROR(
-              error_reporter,
-              "boundaries array not provided for operation 'bucketize'.\n");
-          return kTfLiteError;
-        }
-        params->num_boundaries = boundaries->size();
-        if (boundaries->data() == nullptr) {
-          TF_LITE_REPORT_ERROR(error_reporter,
-                               "boundaries.data() returned nullptr for "
-                               "operation 'bucketize'.\n");
-          return kTfLiteError;
-        }
-        params->boundaries = boundaries->data();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_RANDOM_UNIFORM: {
-      auto params = safe_allocator.Allocate<TfLiteRandomParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* random_uniform_params =
-              op->builtin_options_as_RandomOptions()) {
-        params->seed = random_uniform_params->seed();
-        params->seed2 = random_uniform_params->seed2();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    case BuiltinOperator_GELU: {
-      auto params = safe_allocator.Allocate<TfLiteGeluParams>();
-      TF_LITE_ENSURE(error_reporter, params != nullptr);
-      if (const auto* gelu_params = op->builtin_options_as_GeluOptions()) {
-        params->approximate = gelu_params->approximate();
-      }
-      *builtin_data = params.release();
-      return kTfLiteOk;
-    }
-    // Below are the ops with no builtin_data structure.
-    // TODO(aselle): Implement call in BuiltinOptions, but nullptrs are
-    // ok for now, since there is no call implementation either.
-    case BuiltinOperator_CALL:
-    case BuiltinOperator_COMPLEX_ABS:
-    case BuiltinOperator_CONCAT_EMBEDDINGS:
-    case BuiltinOperator_COS:
-    case BuiltinOperator_CUSTOM:
-    case BuiltinOperator_DENSIFY:
-    case BuiltinOperator_DYNAMIC_UPDATE_SLICE:
-    case BuiltinOperator_EMBEDDING_LOOKUP:
-    case BuiltinOperator_EQUAL:
-    case BuiltinOperator_HASHTABLE_FIND:
-    case BuiltinOperator_HASHTABLE_IMPORT:
-    case BuiltinOperator_HASHTABLE_SIZE:
-    case BuiltinOperator_IMAG:
-    case BuiltinOperator_MATRIX_DIAG:
-    case BuiltinOperator_MATRIX_SET_DIAG:
-    case BuiltinOperator_NON_MAX_SUPPRESSION_V4:
-    case BuiltinOperator_NON_MAX_SUPPRESSION_V5:
-    case BuiltinOperator_RELU_N1_TO_1:
-    case BuiltinOperator_RELU_0_TO_1:
-    case BuiltinOperator_SCATTER_ND:
-    case BuiltinOperator_SELECT:
-    case BuiltinOperator_SLICE:
-    case BuiltinOperator_TILE:
-    case BuiltinOperator_TOPK_V2:
-    case BuiltinOperator_TRANSPOSE:
-    case BuiltinOperator_RANGE:
-    case BuiltinOperator_RANK:
-    case BuiltinOperator_REAL:
-    case BuiltinOperator_RFFT2D:
-    case BuiltinOperator_SEGMENT_SUM:
-    case BuiltinOperator_REVERSE_V2:
-    case BuiltinOperator_UNSORTED_SEGMENT_MAX:
-    case BuiltinOperator_UNSORTED_SEGMENT_MIN:
-    case BuiltinOperator_UNSORTED_SEGMENT_PROD:
-    case BuiltinOperator_UNSORTED_SEGMENT_SUM:
-    case BuiltinOperator_ATAN2:
-    case BuiltinOperator_SIGN:
-    case BuiltinOperator_WHERE:
-      return kTfLiteOk;
-    case BuiltinOperator_PLACEHOLDER_FOR_GREATER_OP_CODES:
-      return kTfLiteError;
-  }
-  return kTfLiteError;
-}  // NOLINT[readability/fn_size]
-#endif  // !defined(TF_LITE_STATIC_MEMORY)
-}  // namespace
-
-TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type,
-                               ErrorReporter* error_reporter) {
-  switch (tensor_type) {
-    case TensorType_FLOAT16:
-      *type = kTfLiteFloat16;
-      return kTfLiteOk;
-    case TensorType_FLOAT32:
-      *type = kTfLiteFloat32;
-      return kTfLiteOk;
-    case TensorType_FLOAT64:
-      *type = kTfLiteFloat64;
-      return kTfLiteOk;
-    case TensorType_INT16:
-      *type = kTfLiteInt16;
-      return kTfLiteOk;
-    case TensorType_UINT16:
-      *type = kTfLiteUInt16;
-      return kTfLiteOk;
-    case TensorType_INT32:
-      *type = kTfLiteInt32;
-      return kTfLiteOk;
-    case TensorType_UINT32:
-      *type = kTfLiteUInt32;
-      return kTfLiteOk;
-    case TensorType_UINT8:
-      *type = kTfLiteUInt8;
-      return kTfLiteOk;
-    case TensorType_INT8:
-      *type = kTfLiteInt8;
-      return kTfLiteOk;
-    case TensorType_INT64:
-      *type = kTfLiteInt64;
-      return kTfLiteOk;
-    case TensorType_UINT64:
-      *type = kTfLiteUInt64;
-      return kTfLiteOk;
-    case TensorType_STRING:
-      *type = kTfLiteString;
-      return kTfLiteOk;
-    case TensorType_BOOL:
-      *type = kTfLiteBool;
-      return kTfLiteOk;
-    case TensorType_COMPLEX64:
-      *type = kTfLiteComplex64;
-      return kTfLiteOk;
-    case TensorType_COMPLEX128:
-      *type = kTfLiteComplex128;
-      return kTfLiteOk;
-    case TensorType_RESOURCE:
-      *type = kTfLiteResource;
-      return kTfLiteOk;
-    case TensorType_VARIANT:
-      *type = kTfLiteVariant;
-      return kTfLiteOk;
-    default:
-      *type = kTfLiteNoType;
-      TF_LITE_REPORT_ERROR(error_reporter,
-                           "Unsupported data type %d in tensor\n", tensor_type);
-      return kTfLiteError;
-  }
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseAbs(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                      void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseAdd(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteAddParams, SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteAddParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const AddOptions* schema_params = op->builtin_options_as_AddOptions();
-
-  if (schema_params != nullptr) {
-    params->activation =
-        ConvertActivation(schema_params->fused_activation_function());
-    params->pot_scale_int16 = schema_params->pot_scale_int16();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseAddN(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseArgMax(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteArgMaxParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteArgMaxParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const ArgMaxOptions* schema_params = op->builtin_options_as_ArgMaxOptions();
-
-  if (schema_params != nullptr) {
-    TF_LITE_ENSURE_STATUS(ConvertTensorType(
-        schema_params->output_type(), &params->output_type, error_reporter));
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseArgMin(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteArgMinParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteArgMinParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const ArgMinOptions* schema_params = op->builtin_options_as_ArgMinOptions();
-
-  if (schema_params != nullptr) {
-    TF_LITE_ENSURE_STATUS(ConvertTensorType(
-        schema_params->output_type(), &params->output_type, error_reporter));
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseAssignVariable(const Operator*, ErrorReporter*,
-                                 BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseBatchMatMul(const Operator* op, ErrorReporter* error_reporter,
-                              BuiltinDataAllocator* allocator,
-                              void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  auto params = safe_allocator.Allocate<TfLiteBatchMatMulParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-  if (const auto* bmm_params = op->builtin_options_as_BatchMatMulOptions()) {
-    params->adj_x = bmm_params->adj_x();
-    params->adj_y = bmm_params->adj_y();
-    params->asymmetric_quantize_inputs =
-        bmm_params->asymmetric_quantize_inputs();
-  }
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseBatchToSpaceNd(const Operator*, ErrorReporter*,
-                                 BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseBroadcastArgs(const Operator*, ErrorReporter*,
-                                BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseBroadcastTo(const Operator*, ErrorReporter*,
-                              BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseCallOnce(const Operator* op, ErrorReporter* error_reporter,
-                           BuiltinDataAllocator* allocator,
-                           void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteCallOnceParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteCallOnceParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const CallOnceOptions* schema_params =
-      op->builtin_options_as_CallOnceOptions();
-
-  if (schema_params != nullptr) {
-    params->init_subgraph_index = schema_params->init_subgraph_index();
-
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseCast(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  auto params = safe_allocator.Allocate<TfLiteCastParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-  if (const auto* schema_params = op->builtin_options_as_CastOptions()) {
-    TF_LITE_ENSURE_STATUS(ConvertTensorType(
-        schema_params->in_data_type(), &params->in_data_type, error_reporter));
-    TF_LITE_ENSURE_STATUS(ConvertTensorType(schema_params->out_data_type(),
-                                            &params->out_data_type,
-                                            error_reporter));
-  }
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseCeil(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                       void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseConcatenation(const Operator* op,
-                                ErrorReporter* error_reporter,
-                                BuiltinDataAllocator* allocator,
-                                void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteConcatenationParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteConcatenationParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const ConcatenationOptions* schema_params =
-      op->builtin_options_as_ConcatenationOptions();
-
-  if (schema_params != nullptr) {
-    params->activation =
-        ConvertActivation(schema_params->fused_activation_function());
-    params->axis = schema_params->axis();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseConv2D(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteConvParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteConvParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const Conv2DOptions* schema_params = op->builtin_options_as_Conv2DOptions();
-
-  if (schema_params != nullptr) {
-    params->padding = ConvertPadding(schema_params->padding());
-    params->stride_width = schema_params->stride_w();
-    params->stride_height = schema_params->stride_h();
-    params->activation =
-        ConvertActivation(schema_params->fused_activation_function());
-
-    params->dilation_width_factor = schema_params->dilation_w_factor();
-    params->dilation_height_factor = schema_params->dilation_h_factor();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseCumsum(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  auto params = safe_allocator.Allocate<TfLiteCumsumParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-  if (const auto* cumsum_params = op->builtin_options_as_CumsumOptions()) {
-    params->exclusive = cumsum_params->exclusive();
-    params->reverse = cumsum_params->reverse();
-  }
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseCos(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                      void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseDepthToSpace(const Operator* op,
-                               ErrorReporter* error_reporter,
-                               BuiltinDataAllocator* allocator,
-                               void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteDepthToSpaceParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteDepthToSpaceParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const auto* schema_params = op->builtin_options_as_DepthToSpaceOptions();
-  if (schema_params != nullptr) {
-    params->block_size = schema_params->block_size();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseDepthwiseConv2D(const Operator* op,
-                                  ErrorReporter* error_reporter,
-                                  BuiltinDataAllocator* allocator,
-                                  void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-
-  std::unique_ptr<TfLiteDepthwiseConvParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteDepthwiseConvParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const DepthwiseConv2DOptions* schema_params =
-      op->builtin_options_as_DepthwiseConv2DOptions();
-
-  if (schema_params != nullptr) {
-    params->padding = ConvertPadding(schema_params->padding());
-    params->stride_width = schema_params->stride_w();
-    params->stride_height = schema_params->stride_h();
-    params->depth_multiplier = schema_params->depth_multiplier();
-    params->activation =
-        ConvertActivation(schema_params->fused_activation_function());
-
-    params->dilation_width_factor = schema_params->dilation_w_factor();
-    params->dilation_height_factor = schema_params->dilation_h_factor();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseDequantize(const Operator*, ErrorReporter*,
-                             BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseDiv(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  auto params = safe_allocator.Allocate<TfLiteDivParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-  if (const auto* schema_params = op->builtin_options_as_DivOptions()) {
-    params->activation =
-        ConvertActivation(schema_params->fused_activation_function());
-  }
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseElu(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                      void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseEqual(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                        void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseExp(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                      void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseExpandDims(const Operator*, ErrorReporter*,
-                             BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseFill(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                       void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseFloor(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                        void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseFloorDiv(const Operator*, ErrorReporter*,
-                           BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseFloorMod(const Operator*, ErrorReporter*,
-                           BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseFullyConnected(const Operator* op,
-                                 ErrorReporter* error_reporter,
-                                 BuiltinDataAllocator* allocator,
-                                 void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-
-  std::unique_ptr<TfLiteFullyConnectedParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteFullyConnectedParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const FullyConnectedOptions* schema_params =
-      op->builtin_options_as_FullyConnectedOptions();
-
-  if (schema_params != nullptr) {
-    params->activation =
-        ConvertActivation(schema_params->fused_activation_function());
-    params->keep_num_dims = schema_params->keep_num_dims();
-    params->asymmetric_quantize_inputs =
-        schema_params->asymmetric_quantize_inputs();
-
-    switch (schema_params->weights_format()) {
-      case FullyConnectedOptionsWeightsFormat_DEFAULT:
-        params->weights_format = kTfLiteFullyConnectedWeightsFormatDefault;
-        break;
-      case FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8:
-        params->weights_format =
-            kTfLiteFullyConnectedWeightsFormatShuffled4x16Int8;
-        break;
-      default:
-        TF_LITE_REPORT_ERROR(error_reporter,
-                             "Unhandled fully-connected weights format.");
-        return kTfLiteError;
-    }
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseGather(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  auto params = safe_allocator.Allocate<TfLiteGatherParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-  params->axis = 0;
-  params->batch_dims = 0;
-  if (const auto* gather_params = op->builtin_options_as_GatherOptions()) {
-    params->axis = gather_params->axis();
-    params->batch_dims = gather_params->batch_dims();
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseGatherNd(const Operator*, ErrorReporter*,
-                           BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseGreater(const Operator*, ErrorReporter*,
-                          BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseGreaterEqual(const Operator*, ErrorReporter*,
-                               BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseHardSwish(const Operator*, ErrorReporter*,
-                            BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseIf(const Operator* op, ErrorReporter* error_reporter,
-                     BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteIfParams, SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteIfParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const IfOptions* schema_params = op->builtin_options_as_IfOptions();
-
-  if (schema_params != nullptr) {
-    params->then_subgraph_index = schema_params->then_subgraph_index();
-    params->else_subgraph_index = schema_params->else_subgraph_index();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseL2Normalization(const Operator* op,
-                                  ErrorReporter* error_reporter,
-                                  BuiltinDataAllocator* allocator,
-                                  void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteL2NormParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteL2NormParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const L2NormOptions* schema_params = op->builtin_options_as_L2NormOptions();
-
-  if (schema_params != nullptr) {
-    params->activation =
-        ConvertActivation(schema_params->fused_activation_function());
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseLeakyRelu(const Operator* op, ErrorReporter* error_reporter,
-                            BuiltinDataAllocator* allocator,
-                            void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  auto params = safe_allocator.Allocate<TfLiteLeakyReluParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-  if (const auto* leaky_relu_params =
-          op->builtin_options_as_LeakyReluOptions()) {
-    params->alpha = leaky_relu_params->alpha();
-  }
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseLess(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                       void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseLessEqual(const Operator*, ErrorReporter*,
-                            BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseLog(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                      void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseLogicalAnd(const Operator*, ErrorReporter*,
-                             BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseLogicalNot(const Operator*, ErrorReporter*,
-                             BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseLogicalOr(const Operator*, ErrorReporter*,
-                            BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseLogistic(const Operator*, ErrorReporter*,
-                           BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseLogSoftmax(const Operator*, ErrorReporter*,
-                             BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseLSTM(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  auto params = safe_allocator.Allocate<TfLiteLSTMParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-  if (const auto* lstm_params = op->builtin_options_as_LSTMOptions()) {
-    params->activation =
-        ConvertActivation(lstm_params->fused_activation_function());
-    params->cell_clip = lstm_params->cell_clip();
-    params->proj_clip = lstm_params->proj_clip();
-    switch (lstm_params->kernel_type()) {
-      case LSTMKernelType_FULL:
-        params->kernel_type = kTfLiteLSTMFullKernel;
-        break;
-      case LSTMKernelType_BASIC:
-        params->kernel_type = kTfLiteLSTMBasicKernel;
-        break;
-      default:
-        TF_LITE_REPORT_ERROR(error_reporter, "Unhandled LSTM kernel type: %d",
-                             lstm_params->kernel_type());
-        return kTfLiteError;
-    }
-    params->asymmetric_quantize_inputs =
-        lstm_params->asymmetric_quantize_inputs();
-  } else {
-    TF_LITE_REPORT_ERROR(error_reporter, "No valid LSTM builtin options exist");
-    return kTfLiteError;
-  }
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseMaximum(const Operator*, ErrorReporter*,
-                          BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseMinimum(const Operator*, ErrorReporter*,
-                          BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseMirrorPad(const Operator* op, ErrorReporter* error_reporter,
-                            BuiltinDataAllocator* allocator,
-                            void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteMirrorPaddingParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteMirrorPaddingParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const MirrorPadOptions* schema_params =
-      op->builtin_options_as_MirrorPadOptions();
-
-  if (schema_params != nullptr) {
-    params->mode = ConvertMirrorPadding(schema_params->mode());
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseMul(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteMulParams, SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteMulParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const MulOptions* schema_params = op->builtin_options_as_MulOptions();
-
-  if (schema_params != nullptr) {
-    params->activation =
-        ConvertActivation(schema_params->fused_activation_function());
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseNeg(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                      void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseNotEqual(const Operator*, ErrorReporter*,
-                           BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParsePack(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLitePackParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLitePackParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const PackOptions* schema_params = op->builtin_options_as_PackOptions();
-
-  if (schema_params != nullptr) {
-    params->values_count = schema_params->values_count();
-    params->axis = schema_params->axis();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParsePad(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                      void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParsePadV2(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                        void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParsePool(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLitePoolParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLitePoolParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const Pool2DOptions* schema_params = op->builtin_options_as_Pool2DOptions();
-
-  if (schema_params != nullptr) {
-    params->padding = ConvertPadding(schema_params->padding());
-    params->stride_width = schema_params->stride_w();
-    params->stride_height = schema_params->stride_h();
-    params->filter_width = schema_params->filter_width();
-    params->filter_height = schema_params->filter_height();
-    params->activation =
-        ConvertActivation(schema_params->fused_activation_function());
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParsePow(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                      void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParsePrelu(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                        void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseQuantize(const Operator*, ErrorReporter*,
-                           BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseReadVariable(const Operator*, ErrorReporter*,
-                               BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseReducer(const Operator* op, ErrorReporter* error_reporter,
-                          BuiltinDataAllocator* allocator,
-                          void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-
-  std::unique_ptr<TfLiteReducerParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteReducerParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const ReducerOptions* schema_params = op->builtin_options_as_ReducerOptions();
-
-  if (schema_params != nullptr) {
-    params->keep_dims = schema_params->keep_dims();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseRelu(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                       void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseRelu6(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                        void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseReshape(const Operator* op, ErrorReporter* error_reporter,
-                          BuiltinDataAllocator* allocator,
-                          void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-
-  std::unique_ptr<TfLiteReshapeParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteReshapeParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const ReshapeOptions* schema_params = op->builtin_options_as_ReshapeOptions();
-
-  if (schema_params != nullptr) {
-    const flatbuffers::Vector<int32_t>* new_shape = schema_params->new_shape();
-    if (new_shape != nullptr) {
-      TF_LITE_ENSURE_STATUS(
-          FlatBufferIntVectorToArray(sizeof(params->shape), new_shape,
-                                     params->shape, error_reporter, "reshape"));
-      params->num_dimensions = new_shape->size();
-    } else {
-      // TODO(b/157480169) TODO(b/147203660): We should either return
-      // kTfLiteError or fill in some reasonable defaults in the params struct.
-      // We are not doing so until we better undertand the ramifications of
-      // changing the legacy behavior.
-    }
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseResizeBilinear(const Operator* op,
-                                 ErrorReporter* error_reporter,
-                                 BuiltinDataAllocator* allocator,
-                                 void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteResizeBilinearParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteResizeBilinearParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const ResizeBilinearOptions* schema_params =
-      op->builtin_options_as_ResizeBilinearOptions();
-
-  if (schema_params != nullptr) {
-    params->align_corners = schema_params->align_corners();
-    params->half_pixel_centers = schema_params->half_pixel_centers();
-  } else {
-    params->align_corners = false;
-    params->half_pixel_centers = false;
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseResizeNearestNeighbor(const Operator* op,
-                                        ErrorReporter* error_reporter,
-                                        BuiltinDataAllocator* allocator,
-                                        void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteResizeNearestNeighborParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteResizeNearestNeighborParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const ResizeNearestNeighborOptions* schema_params =
-      op->builtin_options_as_ResizeNearestNeighborOptions();
-
-  if (schema_params != nullptr) {
-    params->align_corners = schema_params->align_corners();
-    params->half_pixel_centers = schema_params->half_pixel_centers();
-  } else {
-    params->align_corners = false;
-    params->half_pixel_centers = false;
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseRound(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                        void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseRsqrt(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                        void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseSelectV2(const Operator*, ErrorReporter*,
-                           BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseShape(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data) {
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteShapeParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteShapeParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const ShapeOptions* schema_params = op->builtin_options_as_ShapeOptions();
-
-  if (schema_params != nullptr) {
-    TF_LITE_ENSURE_STATUS(ConvertTensorType(schema_params->out_type(),
-                                            &params->out_type, error_reporter));
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseSin(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                      void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseSlice(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                        void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseSoftmax(const Operator* op, ErrorReporter* error_reporter,
-                          BuiltinDataAllocator* allocator,
-                          void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteSoftmaxParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteSoftmaxParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const SoftmaxOptions* schema_params = op->builtin_options_as_SoftmaxOptions();
-
-  if (schema_params != nullptr) {
-    params->beta = schema_params->beta();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseSpaceToBatchNd(const Operator*, ErrorReporter*,
-                                 BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseSpaceToDepth(const Operator* op,
-                               ErrorReporter* error_reporter,
-                               BuiltinDataAllocator* allocator,
-                               void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteSpaceToDepthParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteSpaceToDepthParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const auto* schema_params = op->builtin_options_as_SpaceToDepthOptions();
-  if (schema_params != nullptr) {
-    params->block_size = schema_params->block_size();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseSplit(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteSplitParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteSplitParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const SplitOptions* schema_params = op->builtin_options_as_SplitOptions();
-
-  if (schema_params != nullptr) {
-    params->num_splits = schema_params->num_splits();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseSplitV(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-
-  std::unique_ptr<TfLiteSplitVParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteSplitVParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const SplitVOptions* schema_params = op->builtin_options_as_SplitVOptions();
-
-  if (schema_params != nullptr) {
-    params->num_splits = schema_params->num_splits();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseUnidirectionalSequenceLSTM(const Operator* op,
-                                             ErrorReporter* error_reporter,
-                                             BuiltinDataAllocator* allocator,
-                                             void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  auto params =
-      safe_allocator.Allocate<TfLiteUnidirectionalSequenceLSTMParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-  if (const auto* seq_lstm_params =
-          op->builtin_options_as_UnidirectionalSequenceLSTMOptions()) {
-    params->activation =
-        ConvertActivation(seq_lstm_params->fused_activation_function());
-    params->cell_clip = seq_lstm_params->cell_clip();
-    params->proj_clip = seq_lstm_params->proj_clip();
-    params->time_major = seq_lstm_params->time_major();
-    params->asymmetric_quantize_inputs =
-        seq_lstm_params->asymmetric_quantize_inputs();
-  }
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseSqueeze(const Operator* op, ErrorReporter* error_reporter,
-                          BuiltinDataAllocator* allocator,
-                          void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-
-  std::unique_ptr<TfLiteSqueezeParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteSqueezeParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const SqueezeOptions* schema_params = op->builtin_options_as_SqueezeOptions();
-
-  if (schema_params != nullptr) {
-    const auto* squeeze_dims = schema_params->squeeze_dims();
-    if (squeeze_dims != nullptr) {
-      TF_LITE_ENSURE_STATUS(FlatBufferIntVectorToArray(
-          sizeof(params->squeeze_dims), squeeze_dims, params->squeeze_dims,
-          error_reporter, "squeeze"));
-      params->num_squeeze_dims = squeeze_dims->size();
-    } else {
-      params->num_squeeze_dims = 0;
-    }
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseSqrt(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                       void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseSquare(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                         void**) {
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseSquaredDifference(const Operator*, ErrorReporter*,
-                                    BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseStridedSlice(const Operator* op,
-                               ErrorReporter* error_reporter,
-                               BuiltinDataAllocator* allocator,
-                               void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteStridedSliceParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteStridedSliceParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const StridedSliceOptions* schema_params =
-      op->builtin_options_as_StridedSliceOptions();
-
-  if (schema_params != nullptr) {
-    params->begin_mask = schema_params->begin_mask();
-    params->end_mask = schema_params->end_mask();
-    params->ellipsis_mask = schema_params->ellipsis_mask();
-    params->new_axis_mask = schema_params->new_axis_mask();
-    params->shrink_axis_mask = schema_params->shrink_axis_mask();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseSub(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteSubParams, SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteSubParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const SubOptions* schema_params = op->builtin_options_as_SubOptions();
-
-  if (schema_params != nullptr) {
-    params->activation =
-        ConvertActivation(schema_params->fused_activation_function());
-    params->pot_scale_int16 = schema_params->pot_scale_int16();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseSvdf(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteSVDFParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteSVDFParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const SVDFOptions* schema_params = op->builtin_options_as_SVDFOptions();
-  if (schema_params != nullptr) {
-    params->rank = schema_params->rank();
-    params->activation =
-        ConvertActivation(schema_params->fused_activation_function());
-    params->asymmetric_quantize_inputs =
-        schema_params->asymmetric_quantize_inputs();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseTanh(const Operator*, ErrorReporter*, BuiltinDataAllocator*,
-                       void**) {
-  return kTfLiteOk;
-}
-//
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseTranspose(const Operator*, ErrorReporter*,
-                            BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseTransposeConv(const Operator* op,
-                                ErrorReporter* error_reporter,
-                                BuiltinDataAllocator* allocator,
-                                void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteTransposeConvParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteTransposeConvParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-  const TransposeConvOptions* transpose_conv_params =
-      op->builtin_options_as_TransposeConvOptions();
-  if (transpose_conv_params != nullptr) {
-    params->padding = ConvertPadding(transpose_conv_params->padding());
-    params->stride_width = transpose_conv_params->stride_w();
-    params->stride_height = transpose_conv_params->stride_h();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseUnpack(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteUnpackParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteUnpackParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const UnpackOptions* schema_params = op->builtin_options_as_UnpackOptions();
-
-  if (schema_params != nullptr) {
-    params->num = schema_params->num();
-    params->axis = schema_params->axis();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseVarHandle(const Operator* op, ErrorReporter* error_reporter,
-                            BuiltinDataAllocator* allocator,
-                            void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteVarHandleParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteVarHandleParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const VarHandleOptions* schema_params =
-      op->builtin_options_as_VarHandleOptions();
-
-  if (schema_params != nullptr) {
-    if (schema_params->container()) {
-      params->container = schema_params->container()->c_str();
-    }
-    if (schema_params->shared_name()) {
-      params->shared_name = schema_params->shared_name()->c_str();
-    }
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseWhile(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data) {
-  CheckParsePointerParams(op, error_reporter, allocator, builtin_data);
-
-  SafeBuiltinDataAllocator safe_allocator(allocator);
-  std::unique_ptr<TfLiteWhileParams,
-                  SafeBuiltinDataAllocator::BuiltinDataDeleter>
-      params = safe_allocator.Allocate<TfLiteWhileParams>();
-  TF_LITE_ENSURE(error_reporter, params != nullptr);
-
-  const WhileOptions* schema_params = op->builtin_options_as_WhileOptions();
-
-  if (schema_params != nullptr) {
-    params->cond_subgraph_index = schema_params->cond_subgraph_index();
-    params->body_subgraph_index = schema_params->body_subgraph_index();
-  } else {
-    // TODO(b/157480169): We should either return kTfLiteError or fill in some
-    // reasonable defaults in the params struct. We are not doing so until we
-    // better undertand the ramifications of changing the legacy behavior.
-  }
-
-  *builtin_data = params.release();
-  return kTfLiteOk;
-}
-
-// We have this parse function instead of directly returning kTfLiteOk from the
-// switch-case in ParseOpData because this function is used as part of the
-// selective registration for the OpResolver implementation in micro.
-TfLiteStatus ParseZerosLike(const Operator*, ErrorReporter*,
-                            BuiltinDataAllocator*, void**) {
-  return kTfLiteOk;
-}
-
-TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type,
-                         ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data) {
-// TODO(b/145762662): It would be preferable to have the build graph for TF Lite
-// Micro not have the ParseOpData function at all. This would require splitting
-// the current file into two separate files, one of which defines the
-// ParseOpData function and the other that defines the operator specific parse
-// functions (e.g. ParseAdd).
-//
-// Such a split was attempted but was not worth the effort at the time because
-// of the following reasons:
-//  * We could either duplicate the functions and the SafeBuiltinDataAllocator
-//    class in the anonymous namespace of this file, or attempt to make a common
-//    library with these helper functions and class.
-//  * Making a common library with a separate build target was not feasible as
-//    it introduced circular dependencies due to the ErrorReporter and a common
-//    .cc and .h within the same api build target the also cause circular
-//    dependencies due to the  BuiltinDataAllocator class.
-//  * If all the builtin operators were to have their own parse functions, or we
-//    were ok with some amount of code duplication, then this split of the .cc
-//    files would be a lot more feasible.
-#ifdef TF_LITE_STATIC_MEMORY
-  TF_LITE_REPORT_ERROR(
-      error_reporter,
-      "ParseOpData is unsupported on TfLiteMicro, please use the operator "
-      "specific parse functions (e.g. ParseAdd etc.).\n");
-  return kTfLiteError;
-#else
-  return ParseOpDataTfLite(op, op_type, error_reporter, allocator,
-                           builtin_data);
-#endif
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/core/api/flatbuffer_conversions.h

@@ -1,412 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_
-#define TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_
-
-// These functions transform codes and data structures that are defined in the
-// flatbuffer serialization format into in-memory values that are used by the
-// runtime API and interpreter.
-
-#include <cstddef>
-#include <new>
-#include <type_traits>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/core/api/error_reporter.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-
-namespace tflite {
-
-// Interface class for builtin data allocations.
-class BuiltinDataAllocator {
- public:
-  virtual void* Allocate(size_t size, size_t alignment_hint) = 0;
-  virtual void Deallocate(void* data) = 0;
-
-  // Allocate a structure, but make sure it is a POD structure that doesn't
-  // require constructors to run. The reason we do this, is that Interpreter's C
-  // extension part will take ownership so destructors  will not be run during
-  // deallocation.
-  template <typename T>
-  T* AllocatePOD() {
-    // TODO(b/154346074): Change this to is_trivially_destructible when all
-    // platform targets support that properly.
-    static_assert(std::is_pod<T>::value, "Builtin data structure must be POD.");
-    void* allocated_memory = this->Allocate(sizeof(T), alignof(T));
-    return new (allocated_memory) T();
-  }
-
-  virtual ~BuiltinDataAllocator() {}
-};
-
-// Parse the appropriate data out of the op.
-//
-// This handles builtin data explicitly as there are flatbuffer schemas.
-// If it returns kTfLiteOk, it passes the data out with `builtin_data`. The
-// calling function has to pass in an allocator object, and this allocator
-// will be called to reserve space for the output data. If the calling
-// function's allocator reserves memory on the heap, then it's the calling
-// function's responsibility to free it.
-// If it returns kTfLiteError, `builtin_data` will be `nullptr`.
-TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type,
-                         ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data);
-
-// Converts the tensor data type used in the flat buffer to the representation
-// used by the runtime.
-TfLiteStatus ConvertTensorType(TensorType tensor_type, TfLiteType* type,
-                               ErrorReporter* error_reporter);
-
-TfLiteStatus ParseAbs(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseAdd(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseAddN(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseArgMax(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseArgMin(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseAssignVariable(const Operator* op,
-                                 ErrorReporter* error_reporter,
-                                 BuiltinDataAllocator* allocator,
-                                 void** builtin_data);
-
-TfLiteStatus ParseBatchMatMul(const Operator* op, ErrorReporter* error_reporter,
-                              BuiltinDataAllocator* allocator,
-                              void** builtin_data);
-
-TfLiteStatus ParseBatchToSpaceNd(const Operator* op,
-                                 ErrorReporter* error_reporter,
-                                 BuiltinDataAllocator* allocator,
-                                 void** builtin_data);
-
-TfLiteStatus ParseBroadcastArgs(const Operator* op,
-                                ErrorReporter* error_reporter,
-                                BuiltinDataAllocator* allocator,
-                                void** builtin_data);
-
-TfLiteStatus ParseBroadcastTo(const Operator* op, ErrorReporter* error_reporter,
-                              BuiltinDataAllocator* allocator,
-                              void** builtin_data);
-
-TfLiteStatus ParseCallOnce(const Operator* op, ErrorReporter* error_reporter,
-                           BuiltinDataAllocator* allocator,
-                           void** builtin_data);
-
-TfLiteStatus ParseCeil(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseCast(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseConcatenation(const Operator* op,
-                                ErrorReporter* error_reporter,
-                                BuiltinDataAllocator* allocator,
-                                void** builtin_data);
-
-TfLiteStatus ParseConv2D(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseCos(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseCumsum(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseDepthToSpace(const Operator* op,
-                               ErrorReporter* error_reporter,
-                               BuiltinDataAllocator* allocator,
-                               void** builtin_data);
-
-TfLiteStatus ParseDepthwiseConv2D(const Operator* op,
-                                  ErrorReporter* error_reporter,
-                                  BuiltinDataAllocator* allocator,
-                                  void** builtin_data);
-
-TfLiteStatus ParseDequantize(const Operator* op, ErrorReporter* error_reporter,
-                             BuiltinDataAllocator* allocator,
-                             void** builtin_data);
-
-TfLiteStatus ParseDiv(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseElu(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseEqual(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseExp(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseExpandDims(const Operator* op, ErrorReporter* error_reporter,
-                             BuiltinDataAllocator* allocator,
-                             void** builtin_data);
-
-TfLiteStatus ParseFill(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseFloor(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseFloorDiv(const Operator* op, ErrorReporter* error_reporter,
-                           BuiltinDataAllocator* allocator,
-                           void** builtin_data);
-
-TfLiteStatus ParseFloorMod(const Operator* op, ErrorReporter* error_reporter,
-                           BuiltinDataAllocator* allocator,
-                           void** builtin_data);
-
-TfLiteStatus ParseFullyConnected(const Operator* op,
-                                 ErrorReporter* error_reporter,
-                                 BuiltinDataAllocator* allocator,
-                                 void** builtin_data);
-
-TfLiteStatus ParseGather(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseGatherNd(const Operator* op, ErrorReporter* error_reporter,
-                           BuiltinDataAllocator* allocator,
-                           void** builtin_data);
-
-TfLiteStatus ParseGreater(const Operator* op, ErrorReporter* error_reporter,
-                          BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseGreaterEqual(const Operator* op,
-                               ErrorReporter* error_reporter,
-                               BuiltinDataAllocator* allocator,
-                               void** builtin_data);
-
-TfLiteStatus ParseHardSwish(const Operator* op, ErrorReporter* error_reporter,
-                            BuiltinDataAllocator* allocator,
-                            void** builtin_data);
-
-TfLiteStatus ParseIf(const Operator* op, ErrorReporter* error_reporter,
-                     BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseL2Normalization(const Operator* op,
-                                  ErrorReporter* error_reporter,
-                                  BuiltinDataAllocator* allocator,
-                                  void** builtin_data);
-
-TfLiteStatus ParseLeakyRelu(const Operator* op, ErrorReporter* error_reporter,
-                            BuiltinDataAllocator* allocator,
-                            void** builtin_data);
-
-TfLiteStatus ParseLess(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseLessEqual(const Operator* op, ErrorReporter* error_reporter,
-                            BuiltinDataAllocator* allocator,
-                            void** builtin_data);
-
-TfLiteStatus ParseLog(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseLogicalAnd(const Operator* op, ErrorReporter* error_reporter,
-                             BuiltinDataAllocator* allocator,
-                             void** builtin_data);
-
-TfLiteStatus ParseLogicalNot(const Operator* op, ErrorReporter* error_reporter,
-                             BuiltinDataAllocator* allocator,
-                             void** builtin_data);
-
-TfLiteStatus ParseLogicalOr(const Operator* op, ErrorReporter* error_reporter,
-                            BuiltinDataAllocator* allocator,
-                            void** builtin_data);
-
-TfLiteStatus ParseLogistic(const Operator* op, ErrorReporter* error_reporter,
-                           BuiltinDataAllocator* allocator,
-                           void** builtin_data);
-
-TfLiteStatus ParseLogSoftmax(const Operator* op, ErrorReporter* error_reporter,
-                             BuiltinDataAllocator* allocator,
-                             void** builtin_data);
-
-TfLiteStatus ParseLSTM(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseMaximum(const Operator* op, ErrorReporter* error_reporter,
-                          BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseMinimum(const Operator* op, ErrorReporter* error_reporter,
-                          BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseMirrorPad(const Operator* op, ErrorReporter* error_reporter,
-                            BuiltinDataAllocator* allocator,
-                            void** builtin_data);
-
-TfLiteStatus ParseMul(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseNeg(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseNotEqual(const Operator* op, ErrorReporter* error_reporter,
-                           BuiltinDataAllocator* allocator,
-                           void** builtin_data);
-
-TfLiteStatus ParsePack(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParsePad(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParsePadV2(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParsePool(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParsePow(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParsePrelu(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseQuantize(const Operator* op, ErrorReporter* error_reporter,
-                           BuiltinDataAllocator* allocator,
-                           void** builtin_data);
-
-TfLiteStatus ParseReadVariable(const Operator* op,
-                               ErrorReporter* error_reporter,
-                               BuiltinDataAllocator* allocator,
-                               void** builtin_data);
-
-TfLiteStatus ParseReducer(const Operator* op, ErrorReporter* error_reporter,
-                          BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseRelu(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseRelu6(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseReshape(const Operator* op, ErrorReporter* error_reporter,
-                          BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseResizeBilinear(const Operator* op,
-                                 ErrorReporter* error_reporter,
-                                 BuiltinDataAllocator* allocator,
-                                 void** builtin_data);
-
-TfLiteStatus ParseResizeNearestNeighbor(const Operator* op,
-                                        ErrorReporter* error_reporter,
-                                        BuiltinDataAllocator* allocator,
-                                        void** builtin_data);
-
-TfLiteStatus ParseRound(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseRsqrt(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseSelectV2(const Operator* op, ErrorReporter* error_reporter,
-                           BuiltinDataAllocator* allocator,
-                           void** builtin_data);
-
-TfLiteStatus ParseShape(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseSin(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseSlice(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseSoftmax(const Operator* op, ErrorReporter* error_reporter,
-                          BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseSpaceToBatchNd(const Operator* op,
-                                 ErrorReporter* error_reporter,
-                                 BuiltinDataAllocator* allocator,
-                                 void** builtin_data);
-
-TfLiteStatus ParseSpaceToDepth(const Operator* op,
-                               ErrorReporter* error_reporter,
-                               BuiltinDataAllocator* allocator,
-                               void** builtin_data);
-
-TfLiteStatus ParseSplit(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseSplitV(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseSqueeze(const Operator* op, ErrorReporter* error_reporter,
-                          BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseSqrt(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseSquare(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseSquaredDifference(const Operator* op,
-                                    ErrorReporter* error_reporter,
-                                    BuiltinDataAllocator* allocator,
-                                    void** builtin_data);
-
-TfLiteStatus ParseStridedSlice(const Operator* op,
-                               ErrorReporter* error_reporter,
-                               BuiltinDataAllocator* allocator,
-                               void** builtin_data);
-
-TfLiteStatus ParseSub(const Operator* op, ErrorReporter* error_reporter,
-                      BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseSvdf(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseTanh(const Operator* op, ErrorReporter* error_reporter,
-                       BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseTranspose(const Operator* op, ErrorReporter* error_reporter,
-                            BuiltinDataAllocator* allocator,
-                            void** builtin_data);
-
-TfLiteStatus ParseTransposeConv(const Operator* op,
-                                ErrorReporter* error_reporter,
-                                BuiltinDataAllocator* allocator,
-                                void** builtin_data);
-
-TfLiteStatus ParseUnpack(const Operator* op, ErrorReporter* error_reporter,
-                         BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseUnidirectionalSequenceLSTM(const Operator* op,
-                                             ErrorReporter* error_reporter,
-                                             BuiltinDataAllocator* allocator,
-                                             void** builtin_data);
-
-TfLiteStatus ParseVarHandle(const Operator* op, ErrorReporter* error_reporter,
-                            BuiltinDataAllocator* allocator,
-                            void** builtin_data);
-
-TfLiteStatus ParseWhile(const Operator* op, ErrorReporter* error_reporter,
-                        BuiltinDataAllocator* allocator, void** builtin_data);
-
-TfLiteStatus ParseZerosLike(const Operator* op, ErrorReporter* error_reporter,
-                            BuiltinDataAllocator* allocator,
-                            void** builtin_data);
-
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_CORE_API_FLATBUFFER_CONVERSIONS_H_

code/components/tflite-lib/tensorflow/lite/experimental/microfrontend/lib/kiss_fft_int16.cc

@@ -1,10 +0,0 @@
-#include <cstdint>
-
-#include "tensorflow/lite/experimental/microfrontend/lib/kiss_fft_common.h"
-
-#define FIXED_POINT 16
-namespace kissfft_fixed16 {
-#include "kiss_fft.c"
-#include "tools/kiss_fftr.c"
-}  // namespace kissfft_fixed16
-#undef FIXED_POINT

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/hard_swish.h

@@ -1,168 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_HARD_SWISH_H_
-#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_HARD_SWISH_H_
-
-#include <algorithm>
-
-#include "ruy/profiler/instrumentation.h"  // from @ruy
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-
-namespace tflite {
-namespace reference_ops {
-
-inline int16_t SaturatingLeftShift(int16_t value, int amount) {
-  int64_t result = static_cast<int64_t>(value) * (1 << amount);
-  result = std::min<int64_t>(result, std::numeric_limits<int16_t>::max());
-  result = std::max<int64_t>(result, std::numeric_limits<int16_t>::min());
-  return result;
-}
-
-// Similar to ARM instruction SQDMULH.
-// Similar to gemmlowp::SaturatingRoundingDoublingHighMul except
-// rounding to zero instead of to nearest (SQRDMULH).
-inline std::int16_t SaturatingDoublingHighMul(std::int16_t a, std::int16_t b) {
-  bool overflow = a == b && a == std::numeric_limits<std::int16_t>::min();
-  std::int32_t a_32(a);
-  std::int32_t b_32(b);
-  std::int32_t ab_32 = a_32 * b_32;
-  std::int16_t ab_x2_high16 = static_cast<std::int16_t>((ab_32) / (1 << 15));
-  return overflow ? std::numeric_limits<std::int16_t>::max() : ab_x2_high16;
-}
-
-template <typename T>
-inline void HardSwish(const RuntimeShape& input_shape, const T* input_data,
-                      const RuntimeShape& output_shape, T* output_data) {
-  ruy::profiler::ScopeLabel label("ReferenceHardSwish/Float");
-  auto matching_size = MatchingFlatSize(input_shape, output_shape);
-  const T* in_end = input_data + matching_size;
-  for (; input_data < in_end; input_data++, output_data++) {
-    const float in = *input_data;
-    *output_data =
-        in * std::min(static_cast<T>(6), std::max(static_cast<T>(0), in + 3)) /
-        6;
-  }
-}
-
-template <typename T>
-inline void HardSwish(const HardSwishParams& params,
-                      const RuntimeShape& input_shape, const T* input_data,
-                      const RuntimeShape& output_shape, T* output_data) {
-  ruy::profiler::ScopeLabel label("ReferenceHardSwish/Quantized");
-
-  const int flat_size = MatchingFlatSize(input_shape, output_shape);
-
-  for (int i = 0; i < flat_size; i++) {
-    const int16_t input_value = input_data[i] - params.input_zero_point;
-    // Left-shift as much as we can without overflow/saturation to put
-    // significant bits in the high bits of our 16-bit fixedpoint values, so
-    // that fixed-point approximate computations below are as accurate as
-    // possible.
-    const int16_t input_value_on_hires_input_scale = input_value * (1 << 7);
-    // Compute the input value on essentially the output scale, just not
-    // right-shifted yet. This is the value that we'll use in the (x >= +3)
-    // case, and that in the general case we'll multiply against the "relu-ish"
-    // fixed-point multiplier in [0, 1].
-    const int16_t input_value_on_preshift_output_scale =
-        gemmlowp::SaturatingRoundingDoublingHighMul(
-            input_value_on_hires_input_scale,
-            params.output_multiplier_fixedpoint_int16);
-    // Now compute the "relu-ish multiplier". In the (-3 <= x <= +3) case, that
-    // is just an affine rescaling of x from [-3, 3] to [0, 1]. In the general
-    // case, it is just that plus saturation at the boundaries of [-3, 3].
-    // First, we rescale from [-3, 3] to [-1, 1], saturating.
-    // That is done by rescaling the input value with a fixed-point multiplier
-    // (reluish_multiplier_fixedpoint) and bit-shift such that we represent
-    // that input value on the scale where the real value 3.0f is represented
-    // by the quantized value 32768.  (+32768 is actually not representable as
-    // int16_t, so this saturates at +32767, and that is seen empirically to be
-    // a negligible contribution to numerical error/bias).
-    //
-    // This code is careful to correctly implement any magnitude of multiplier,
-    // involving either a right shift or a left shift, with correct saturation
-    // behavior in the left-shift case. This forces this code to be more
-    // complicated, but is necessary for real applications: a partially
-    // trained quantized MobileNet v3-small model that motivated this code
-    // exhibits some large [min, max] range boundaries, of the order of
-    // magnitude of 10 or 100 depending on layers.
-    //
-    // The next few lines are basically just an ordinary
-    // MultiplyByQuantizedMultiplier, except that we are more careful here
-    // about the fine details of saturation when left-shifting, because here
-    // overflow in left-shift is a common case, not an anomaly as
-    // MultiplyByQuantizedMultiplier assumes.
-    int16_t reluish_value = input_value_on_hires_input_scale;
-    // Shift left, saturating, as much as we can while ensuring that this
-    // saturation will not contribute to the result. That is, left shift amount
-    // reduced by 1.
-    if (params.reluish_multiplier_exponent > 0) {
-      reluish_value = SaturatingLeftShift(
-          reluish_value, params.reluish_multiplier_exponent - 1);
-    }
-    // Apply the fixed-point multiplier, dividing the value by a divisor
-    // ranging in [1, 2].
-    reluish_value = gemmlowp::SaturatingRoundingDoublingHighMul(
-        reluish_value, params.reluish_multiplier_fixedpoint_int16);
-    // Apply the last bit of left-shift. Thus, in the left-shifting case, if
-    // any saturation affects the result, it is happening here --- any
-    // saturation having occurred above is overwritten here, not affecting the
-    // result.
-    if (params.reluish_multiplier_exponent > 0) {
-      reluish_value = SaturatingLeftShift(reluish_value, 1);
-    }
-    // Shift right, in the right-shifting case.
-    if (params.reluish_multiplier_exponent < 0) {
-      reluish_value = gemmlowp::RoundingDivideByPOT(
-          reluish_value, -params.reluish_multiplier_exponent);
-    }
-    // At this point we have rescaled the value into a 16bit fixedpoint
-    // reluish_value in [-1, 1].
-    // We now convert that to a 16bit fixedpoint value in [0, 1].
-    reluish_value = (reluish_value + (1 << 15)) >> 1;
-    // Use of SaturatingDoublingHighMul here is important to cancel the biases
-    // from the above SaturatingRoundingDoublingHighMul.
-    //
-    // On a partially trained MobileNet-v3-small,
-    //
-    //                                       | bias on    |  ImageNet
-    //                                       | quantized  |  Top-1
-    // Operation used here                   | values     |  accuracy (50k)
-    // --------------------------------------+------------+-----------
-    // SaturatingDoublingHighMul             | -0.0024    |  58.920
-    // SaturatingRoundingDoublingHighMul     | -0.0067    |  58.064
-    //
-    // In activations_test, this is covered by this testcase:
-    //     QuantizedActivationsOpTest.HardSwishBias
-    //
-    const int16_t preshift_output_value = SaturatingDoublingHighMul(
-        reluish_value, input_value_on_preshift_output_scale);
-    // We were so far operating on the pre-shift output scale. Now we finally
-    // apply that output shift, arriving at the final output scale.
-    int16_t output_value = gemmlowp::RoundingDivideByPOT(
-        preshift_output_value, -params.output_multiplier_exponent);
-    output_value += params.output_zero_point;
-    output_value =
-        std::min<int16_t>(output_value, std::numeric_limits<T>::max());
-    output_value =
-        std::max<int16_t>(output_value, std::numeric_limits<T>::min());
-    output_data[i] = output_value;
-  }
-}
-
-}  // namespace reference_ops
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_HARD_SWISH_H_

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/mul.h

@@ -1,214 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_
-#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_
-
-#include <algorithm>
-#include <complex>
-
-#include "tensorflow/lite/kernels/internal/common.h"
-
-namespace tflite {
-
-namespace reference_ops {
-
-// Element-wise mul that can often be used for inner loop of broadcast Mul as
-// well as the non-broadcast Mul.
-inline void MulElementwise(int size, const ArithmeticParams& params,
-                           const uint8_t* input1_data,
-                           const uint8_t* input2_data, uint8_t* output_data) {
-  for (int i = 0; i < size; ++i) {
-    const int32_t input1_val = params.input1_offset + input1_data[i];
-    const int32_t input2_val = params.input2_offset + input2_data[i];
-    const int32_t unclamped_result =
-        params.output_offset +
-        MultiplyByQuantizedMultiplier(input1_val * input2_val,
-                                      params.output_multiplier,
-                                      params.output_shift);
-    const int32_t clamped_output =
-        std::min(params.quantized_activation_max,
-                 std::max(params.quantized_activation_min, unclamped_result));
-    output_data[i] = static_cast<uint8_t>(clamped_output);
-  }
-}
-
-template <typename T>
-inline void Mul(const ArithmeticParams& params,
-                const RuntimeShape& input1_shape, const T* input1_data,
-                const RuntimeShape& input2_shape, const T* input2_data,
-                const RuntimeShape& output_shape, T* output_data) {
-  T output_activation_min;
-  T output_activation_max;
-  GetActivationParams(params, &output_activation_min, &output_activation_max);
-
-  const int flat_size =
-      MatchingExtendedShapeFlatSize(input1_shape, input2_shape, output_shape);
-  for (int i = 0; i < flat_size; ++i) {
-    output_data[i] = ActivationFunctionWithMinMax(
-        input1_data[i] * input2_data[i], output_activation_min,
-        output_activation_max);
-  }
-}
-
-inline void Mul(const ArithmeticParams& params,
-                const RuntimeShape& input1_shape,
-                const std::complex<float>* input1_data,
-                const RuntimeShape& input2_shape,
-                const std::complex<float>* input2_data,
-                const RuntimeShape& output_shape,
-                std::complex<float>* output_data) {
-  const int flat_size =
-      MatchingExtendedShapeFlatSize(input1_shape, input2_shape, output_shape);
-  for (int i = 0; i < flat_size; ++i) {
-    output_data[i] = input1_data[i] * input2_data[i];
-  }
-}
-
-inline void Mul(const ArithmeticParams& params,
-                const RuntimeShape& input1_shape, const uint8_t* input1_data,
-                const RuntimeShape& input2_shape, const uint8_t* input2_data,
-                const RuntimeShape& output_shape, uint8_t* output_data) {
-  TFLITE_DCHECK_LE(params.quantized_activation_min,
-                   params.quantized_activation_max);
-  const int flat_size =
-      MatchingExtendedShapeFlatSize(input1_shape, input2_shape, output_shape);
-
-  MulElementwise(flat_size, params, input1_data, input2_data, output_data);
-}
-
-inline void BroadcastMul4DSlow(const ArithmeticParams& params,
-                               const RuntimeShape& input1_shape,
-                               const uint8_t* input1_data,
-                               const RuntimeShape& input2_shape,
-                               const uint8_t* input2_data,
-                               const RuntimeShape& output_shape,
-                               uint8_t* output_data) {
-  NdArrayDesc<4> desc1;
-  NdArrayDesc<4> desc2;
-  NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1,
-                                      &desc2);
-  const RuntimeShape extended_output_shape =
-      RuntimeShape::ExtendedShape(4, output_shape);
-
-  for (int b = 0; b < extended_output_shape.Dims(0); ++b) {
-    for (int y = 0; y < extended_output_shape.Dims(1); ++y) {
-      for (int x = 0; x < extended_output_shape.Dims(2); ++x) {
-        for (int c = 0; c < extended_output_shape.Dims(3); ++c) {
-          const int32_t input1_val =
-              params.input1_offset +
-              input1_data[SubscriptToIndex(desc1, b, y, x, c)];
-          const int32_t input2_val =
-              params.input2_offset +
-              input2_data[SubscriptToIndex(desc2, b, y, x, c)];
-          const int32_t unclamped_result =
-              params.output_offset +
-              MultiplyByQuantizedMultiplier(input1_val * input2_val,
-                                            params.output_multiplier,
-                                            params.output_shift);
-          const int32_t clamped_output = std::min(
-              params.quantized_activation_max,
-              std::max(params.quantized_activation_min, unclamped_result));
-          output_data[Offset(extended_output_shape, b, y, x, c)] =
-              static_cast<uint8_t>(clamped_output);
-        }
-      }
-    }
-  }
-}
-
-template <typename T>
-void BroadcastMul4DSlow(const ArithmeticParams& params,
-                        const RuntimeShape& unextended_input1_shape,
-                        const T* input1_data,
-                        const RuntimeShape& unextended_input2_shape,
-                        const T* input2_data,
-                        const RuntimeShape& unextended_output_shape,
-                        T* output_data) {
-  T output_activation_min;
-  T output_activation_max;
-  GetActivationParams(params, &output_activation_min, &output_activation_max);
-
-  TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4);
-  TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4);
-  TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4);
-  const RuntimeShape output_shape =
-      RuntimeShape::ExtendedShape(4, unextended_output_shape);
-
-  NdArrayDesc<4> desc1;
-  NdArrayDesc<4> desc2;
-  NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
-                                      unextended_input2_shape, &desc1, &desc2);
-
-  // In Tensorflow, the dimensions are canonically named (batch_number, row,
-  // col, channel), with extents (batches, height, width, depth), with the
-  // trailing dimension changing most rapidly (channels has the smallest stride,
-  // typically 1 element).
-  //
-  // In generated C code, we store arrays with the dimensions reversed. The
-  // first dimension has smallest stride.
-  //
-  // We name our variables by their Tensorflow convention, but generate C code
-  // nesting loops such that the innermost loop has the smallest stride for the
-  // best cache behavior.
-  for (int b = 0; b < output_shape.Dims(0); ++b) {
-    for (int y = 0; y < output_shape.Dims(1); ++y) {
-      for (int x = 0; x < output_shape.Dims(2); ++x) {
-        for (int c = 0; c < output_shape.Dims(3); ++c) {
-          output_data[Offset(output_shape, b, y, x, c)] =
-              ActivationFunctionWithMinMax(
-                  input1_data[SubscriptToIndex(desc1, b, y, x, c)] *
-                      input2_data[SubscriptToIndex(desc2, b, y, x, c)],
-                  output_activation_min, output_activation_max);
-        }
-      }
-    }
-  }
-}
-
-inline void BroadcastMul4DSlow(const ArithmeticParams& params,
-                               const RuntimeShape& unextended_input1_shape,
-                               const std::complex<float>* input1_data,
-                               const RuntimeShape& unextended_input2_shape,
-                               const std::complex<float>* input2_data,
-                               const RuntimeShape& unextended_output_shape,
-                               std::complex<float>* output_data) {
-  TFLITE_DCHECK_LE(unextended_input1_shape.DimensionsCount(), 4);
-  TFLITE_DCHECK_LE(unextended_input2_shape.DimensionsCount(), 4);
-  TFLITE_DCHECK_LE(unextended_output_shape.DimensionsCount(), 4);
-  const RuntimeShape output_shape =
-      RuntimeShape::ExtendedShape(4, unextended_output_shape);
-
-  NdArrayDesc<4> desc1;
-  NdArrayDesc<4> desc2;
-  NdArrayDescsForElementwiseBroadcast(unextended_input1_shape,
-                                      unextended_input2_shape, &desc1, &desc2);
-
-  for (int b = 0; b < output_shape.Dims(0); ++b) {
-    for (int y = 0; y < output_shape.Dims(1); ++y) {
-      for (int x = 0; x < output_shape.Dims(2); ++x) {
-        for (int c = 0; c < output_shape.Dims(3); ++c) {
-          output_data[Offset(output_shape, b, y, x, c)] =
-              input1_data[SubscriptToIndex(desc1, b, y, x, c)] *
-              input2_data[SubscriptToIndex(desc2, b, y, x, c)];
-        }
-      }
-    }
-  }
-}
-
-}  // namespace reference_ops
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_MUL_H_

code/components/tflite-lib/tensorflow/lite/kernels/internal/reference/select.h

@@ -1,151 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SELECT_H_
-#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SELECT_H_
-
-#include <cmath>
-
-#include "ruy/profiler/instrumentation.h"  // from @ruy
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-
-namespace tflite {
-namespace reference_ops {
-
-template <typename D, typename T>
-void Select(const RuntimeShape& input_condition_shape,
-            const D* input_condition_data, const RuntimeShape& input_x_shape,
-            const T* input_x_data, const RuntimeShape& input_y_shape,
-            const T* input_y_data, const RuntimeShape& output_shape,
-            T* output_data) {
-  ruy::profiler::ScopeLabel label("Select");
-  int64_t flatsize;
-  // Allow select operator executions on mixed scalar tensors and one element
-  // tensors.
-  if (input_condition_shape.FlatSize() == 1 && input_x_shape.FlatSize() == 1 &&
-      input_y_shape.FlatSize() == 1 && output_shape.FlatSize() == 1) {
-    flatsize = 1;
-  } else {
-    flatsize = MatchingFlatSize(input_condition_shape, input_x_shape,
-                                input_y_shape, output_shape);
-  }
-  for (int64_t i = 0; i < flatsize; ++i) {
-    output_data[i] =
-        input_condition_data[i] ? input_x_data[i] : input_y_data[i];
-  }
-}
-
-template <typename D, typename T>
-void RankOneSelect(const RuntimeShape& input_condition_shape,
-                   const D* input_condition_data,
-                   const RuntimeShape& input_x_shape, const T* input_x_data,
-                   const RuntimeShape& input_y_shape, const T* input_y_data,
-                   const RuntimeShape& output_shape, T* output_data) {
-  ruy::profiler::ScopeLabel label("Select/RankOneSelect");
-  const int64_t outer_size = input_condition_shape.FlatSize();
-  int64_t inner_size;
-  if (input_condition_shape.DimensionsCount() == 0) {
-    inner_size = MatchingFlatSize(input_x_shape, input_y_shape, output_shape);
-  } else {
-    TFLITE_DCHECK_EQ(
-        MatchingDim(input_x_shape, 0, input_y_shape, 0, output_shape, 0),
-        outer_size);
-    inner_size =
-        MatchingFlatSizeSkipDim(input_x_shape, 0, input_y_shape, output_shape);
-  }
-
-  int64_t offset = 0;
-  for (int64_t i = 0; i < outer_size; i++) {
-    const T* input_data = input_condition_data[i] ? input_x_data : input_y_data;
-    memcpy(output_data + offset, input_data + offset, inner_size * sizeof(T));
-    offset += inner_size;
-  }
-}
-
-template <typename D, typename T>
-void BroadcastSelect5DSlow(const RuntimeShape& input_condition_shape,
-                           const D* input_condition_data,
-                           const RuntimeShape& input_x_shape,
-                           const T* input_x_data,
-                           const RuntimeShape& input_y_shape,
-                           const T* input_y_data,
-                           const RuntimeShape& output_shape, T* output_data) {
-  ruy::profiler::ScopeLabel label("Select/BroadcastSelectSlow");
-  TFLITE_DCHECK_LE(input_condition_shape.DimensionsCount(), 5);
-  TFLITE_DCHECK_LE(input_x_shape.DimensionsCount(), 5);
-  TFLITE_DCHECK_LE(input_y_shape.DimensionsCount(), 5);
-  TFLITE_DCHECK_LE(output_shape.DimensionsCount(), 5);
-
-  NdArrayDesc<5> desc_condition;
-  NdArrayDesc<5> desc_x;
-  NdArrayDesc<5> desc_y;
-  NdArrayDesc<5> desc_output;
-  const RuntimeShape extended_output_shape =
-      RuntimeShape::ExtendedShape(5, output_shape);
-  CopyDimsToDesc(extended_output_shape, &desc_output);
-  NdArrayDescsForElementwiseBroadcast(input_condition_shape, input_x_shape,
-                                      input_y_shape, &desc_condition, &desc_x,
-                                      &desc_y);
-
-  // In Tensorflow, the dimensions are canonically named (batch_number, row,
-  // col, channel), with extents (batches, height, width, depth), with the
-  // trailing dimension changing most rapidly (channels has the smallest
-  // stride, typically 1 element).
-  //
-  // In generated C code, we store arrays with the dimensions reversed. The
-  // first dimension has smallest stride.
-  //
-  // We name our variables by their Tensorflow convention, but generate C code
-  // nesting loops such that the innermost loop has the smallest stride for
-  // the best cache behavior.
-  for (int n = 0; n < desc_output.extents[0]; ++n) {
-    int out_idx_n = desc_output.extents[1] * n;
-    int cond_idx_n = desc_condition.strides[0] * n;
-    int in_idx1_n = desc_x.strides[0] * n;
-    int in_idx2_n = desc_y.strides[0] * n;
-    for (int b = 0; b < desc_output.extents[1]; ++b) {
-      int out_idx_b = (out_idx_n + b) * desc_output.extents[2];
-      int cond_idx_b = cond_idx_n + desc_condition.strides[1] * b;
-      int in_idx1_b = in_idx1_n + desc_x.strides[1] * b;
-      int in_idx2_b = in_idx2_n + desc_y.strides[1] * b;
-      for (int y = 0; y < desc_output.extents[2]; ++y) {
-        int out_idx_y = (out_idx_b + y) * desc_output.extents[3];
-        int cond_idx_y = cond_idx_b + desc_condition.strides[2] * y;
-        int in_idx1_y = in_idx1_b + desc_x.strides[2] * y;
-        int in_idx2_y = in_idx2_b + desc_y.strides[2] * y;
-        for (int x = 0; x < desc_output.extents[3]; ++x) {
-          int out_idx = (out_idx_y + x) * desc_output.extents[4];
-          int cond_idx = cond_idx_y + desc_condition.strides[3] * x;
-          int in_idx1 = in_idx1_y + desc_x.strides[3] * x;
-          int in_idx2 = in_idx2_y + desc_y.strides[3] * x;
-          for (int c = 0; c < desc_output.extents[4]; ++c) {
-            output_data[out_idx] = input_condition_data[cond_idx]
-                                       ? input_x_data[in_idx1]
-                                       : input_y_data[in_idx2];
-            out_idx++;
-            cond_idx += desc_condition.strides[4];
-            in_idx1 += desc_x.strides[4];
-            in_idx2 += desc_y.strides[4];
-          }
-        }
-      }
-    }
-  }
-}
-
-}  // namespace reference_ops
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SELECT_H_

code/components/tflite-lib/tensorflow/lite/micro/all_ops_resolver.cc

@@ -1,122 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/all_ops_resolver.h"
-
-#include "tensorflow/lite/micro/kernels/micro_ops.h"
-
-namespace tflite {
-
-AllOpsResolver::AllOpsResolver() {
-  // Please keep this list of Builtin Operators in alphabetical order.
-  AddAbs();
-  AddAdd();
-  AddAddN();
-  AddArgMax();
-  AddArgMin();
-  AddAssignVariable();
-  AddAveragePool2D();
-  AddBatchToSpaceNd();
-  AddBroadcastArgs();
-  AddBroadcastTo();
-  AddCallOnce();
-  AddCast();
-  AddCeil();
-  AddCircularBuffer();
-  AddConcatenation();
-  AddConv2D();
-  AddCos();
-  AddCumSum();
-  AddDepthToSpace();
-  AddDepthwiseConv2D();
-  AddDequantize();
-  AddDetectionPostprocess();
-  AddDiv();
-  AddElu();
-  AddEqual();
-  AddEthosU();
-  AddExp();
-  AddExpandDims();
-  AddFill();
-  AddFloor();
-  AddFloorDiv();
-  AddFloorMod();
-  AddFullyConnected();
-  AddGather();
-  AddGatherNd();
-  AddGreater();
-  AddGreaterEqual();
-  AddHardSwish();
-  AddIf();
-  AddL2Normalization();
-  AddL2Pool2D();
-  AddLeakyRelu();
-  AddLess();
-  AddLessEqual();
-  AddLog();
-  AddLogicalAnd();
-  AddLogicalNot();
-  AddLogicalOr();
-  AddLogistic();
-  AddMaxPool2D();
-  AddMaximum();
-  AddMean();
-  AddMinimum();
-  AddMirrorPad();
-  AddMul();
-  AddNeg();
-  AddNotEqual();
-  AddPack();
-  AddPad();
-  AddPadV2();
-  AddPrelu();
-  AddQuantize();
-  AddReadVariable();
-  AddReduceMax();
-  AddRelu();
-  AddRelu6();
-  AddReshape();
-  AddResizeBilinear();
-  AddResizeNearestNeighbor();
-  AddRound();
-  AddRsqrt();
-  AddSelectV2();
-  AddShape();
-  AddSin();
-  AddSlice();
-  AddSoftmax();
-  AddSpaceToBatchNd();
-  AddSpaceToDepth();
-  AddSplit();
-  AddSplitV();
-  AddSqrt();
-  AddSquare();
-  AddSquaredDifference();
-  AddSqueeze();
-  AddStridedSlice();
-  AddSub();
-  AddSum();
-  AddSvdf();
-  AddTanh();
-  AddTranspose();
-  AddTransposeConv();
-  AddUnidirectionalSequenceLSTM();
-  AddUnpack();
-  AddVarHandle();
-  AddWhile();
-  AddZerosLike();
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/add.h

@@ -1,77 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_KERNELS_ADD_H_
-#define TENSORFLOW_LITE_MICRO_KERNELS_ADD_H_
-
-#include <cstdint>
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-
-namespace tflite {
-
-extern const int kAddInputTensor1;
-extern const int kAddInputTensor2;
-extern const int kAddOutputTensor;
-
-struct OpDataAdd {
-  bool requires_broadcast;
-
-  // These fields are used in both the general 8-bit -> 8bit quantized path,
-  // and the special 16-bit -> 16bit quantized path
-  int input1_shift;
-  int input2_shift;
-  int32_t output_activation_min;
-  int32_t output_activation_max;
-
-  // These fields are used only in the general 8-bit -> 8bit quantized path
-  int32_t input1_multiplier;
-  int32_t input2_multiplier;
-  int32_t output_multiplier;
-  int output_shift;
-  int left_shift;
-  int32_t input1_offset;
-  int32_t input2_offset;
-  int32_t output_offset;
-
-  // Used only for float evals:
-  float output_activation_min_f32;
-  float output_activation_max_f32;
-};
-
-TfLiteStatus CalculateOpDataAdd(TfLiteContext* context, TfLiteAddParams* params,
-                                const TfLiteTensor* input1,
-                                const TfLiteTensor* input2,
-                                TfLiteTensor* output, OpDataAdd* data);
-
-TfLiteStatus AddPrepare(TfLiteContext* context, TfLiteNode* node);
-
-// Generic must define registration function.
-TfLiteRegistration Register_ADD();
-
-#if defined(CMSIS_NN)
-TfLiteRegistration Register_ADD_INT8();
-
-TfLiteRegistration Register_ADD_INT16();
-#else
-// Fallback registration
-inline TfLiteRegistration Register_ADD_INT8() { return Register_ADD(); }
-
-inline TfLiteRegistration Register_ADD_INT16() { return Register_ADD(); }
-#endif
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_MICRO_KERNELS_ADD_H_

code/components/tflite-lib/tensorflow/lite/micro/kernels/add_n.cc

@@ -1,214 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/add_n.h"
-
-#include <cstdint>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-constexpr int kInputTensor0 = 0;
-constexpr int kOutputTensor = 0;
-
-constexpr int kAddNIntegerShift = 20;
-
-// only used with INT8 tensors
-struct OpData {
-  int32_t output_activation_min;
-  int32_t output_activation_max;
-  int32_t input_offset;
-  int32_t output_offset;
-  int32_t input_multiplier;
-  int32_t output_multiplier;
-  int input_shift;
-  int output_shift;
-  int left_shift;
-  int scratch_index;
-};
-
-TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node) {
-  int num_inputs = NumInputs(node);
-  TF_LITE_ENSURE(context, num_inputs >= 2);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  MicroContext* micro_context = GetMicroContext(context);
-  TfLiteTensor* input_tensor_first =
-      micro_context->AllocateTempInputTensor(node, kInputTensor0);
-  TF_LITE_ENSURE(context, input_tensor_first != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  // Check that all tensors have the same shape and type.
-  TF_LITE_ENSURE_TYPES_EQ(context, output->type, input_tensor_first->type);
-  for (int i = kInputTensor0 + 1; i < num_inputs; ++i) {
-    TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, i);
-    TF_LITE_ENSURE(context, input != nullptr);
-    TF_LITE_ENSURE(context, HaveSameShapes(input_tensor_first, input));
-    TF_LITE_ENSURE_TYPES_EQ(context, input_tensor_first->type, input->type);
-
-    // Check that all INT8 input tensors have the same zero-point and scale.
-    if (input_tensor_first->type == kTfLiteInt8) {
-      TF_LITE_ENSURE(context, input_tensor_first->params.zero_point ==
-                                  input->params.zero_point);
-      TF_LITE_ENSURE(context,
-                     input_tensor_first->params.scale == input->params.scale);
-    }
-
-    micro_context->DeallocateTempTfLiteTensor(input);
-  }
-
-  if (output->type == kTfLiteFloat32) {
-    // Allocate scratch buffer space for pointer to each tensor's data
-    // and store the scratch buffer index in the node's user_data
-    int scratch_index;
-    size_t scratch_size = sizeof(float*) * num_inputs;
-    TF_LITE_ENSURE_OK(context, context->RequestScratchBufferInArena(
-                                   context, scratch_size, &scratch_index));
-    node->user_data =
-        reinterpret_cast<decltype(node->user_data)>(scratch_index);
-  } else if (output->type == kTfLiteInt8) {
-    node->user_data =
-        context->AllocatePersistentBuffer(context, sizeof(OpData));
-    OpData* data = static_cast<OpData*>(node->user_data);
-
-    // Allocate scratch buffer space for pointer to each tensor's data
-    // and store the scratch buffer index in OpData
-    size_t scratch_size = sizeof(int8_t*) * num_inputs;
-    TF_LITE_ENSURE_OK(
-        context, context->RequestScratchBufferInArena(context, scratch_size,
-                                                      &data->scratch_index));
-
-    // 8bit -> 8bit general quantized path, with general rescalings
-    data->input_offset = -input_tensor_first->params.zero_point;
-    data->output_offset = output->params.zero_point;
-    data->left_shift = kAddNIntegerShift;
-    const double twice_max_input_scale =
-        2 * static_cast<double>(input_tensor_first->params.scale);
-    const double real_input_multiplier =
-        static_cast<double>(input_tensor_first->params.scale) /
-        twice_max_input_scale;
-    const double real_output_multiplier =
-        twice_max_input_scale /
-        ((1 << data->left_shift) * static_cast<double>(output->params.scale));
-
-    QuantizeMultiplierSmallerThanOneExp(
-        real_input_multiplier, &data->input_multiplier, &data->input_shift);
-
-    QuantizeMultiplierSmallerThanOneExp(
-        real_output_multiplier, &data->output_multiplier, &data->output_shift);
-
-    TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
-        context, kTfLiteActNone, output, &data->output_activation_min,
-        &data->output_activation_max));
-  } else {
-    MicroPrintf("ADD_N only supports FLOAT32 and INT8, got %s.",
-                TfLiteTypeGetName(output->type));
-    return kTfLiteError;
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(input_tensor_first);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  return CalculateOpData(context, node);
-}
-
-template <typename T>
-inline const T** CopyInputsToScratchBuffer(TfLiteContext* context,
-                                           TfLiteNode* node,
-                                           const int scratch_index) {
-  int num_inputs = NumInputs(node);
-  void* scratch_buffer = context->GetScratchBuffer(context, scratch_index);
-  const T** all_inputs = static_cast<decltype(all_inputs)>(scratch_buffer);
-  for (int i = 0; i < num_inputs; i++) {
-    const TfLiteEvalTensor* next_input =
-        tflite::micro::GetEvalInput(context, node, kInputTensor0 + i);
-    all_inputs[i] = tflite::micro::GetTensorData<T>(next_input);
-  }
-
-  return all_inputs;
-}
-
-template <typename T>
-void EvalAddN(TfLiteContext* context, TfLiteNode* node,
-              TfLiteEvalTensor* output) {
-  int num_inputs = NumInputs(node);
-
-  int scratch_index =
-      static_cast<int>(reinterpret_cast<intptr_t>(node->user_data));
-  const T** all_inputs =
-      CopyInputsToScratchBuffer<T>(context, node, scratch_index);
-
-  reference_ops::AddN<T>(tflite::micro::GetTensorShape(output), num_inputs,
-                         all_inputs, tflite::micro::GetTensorData<T>(output));
-}
-
-template <typename T>
-void EvalAddNQuantized(TfLiteContext* context, TfLiteNode* node,
-                       TfLiteEvalTensor* output) {
-  int num_inputs = NumInputs(node);
-
-  OpData* data = static_cast<OpData*>(node->user_data);
-  const T** all_inputs =
-      CopyInputsToScratchBuffer<T>(context, node, data->scratch_index);
-
-  ArithmeticParams params;
-  params.left_shift = data->left_shift;
-  params.input1_offset = data->input_offset;
-  params.input1_multiplier = data->input_multiplier;
-  params.input1_shift = data->input_shift;
-  params.output_offset = data->output_offset;
-  params.output_multiplier = data->output_multiplier;
-  params.output_shift = data->output_shift;
-  SetActivationParams(data->output_activation_min, data->output_activation_max,
-                      &params);
-
-  reference_ops::AddN(params, tflite::micro::GetTensorShape(output), num_inputs,
-                      all_inputs, tflite::micro::GetTensorData<T>(output));
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  if (output->type == kTfLiteFloat32) {
-    EvalAddN<float>(context, node, output);
-  } else if (output->type == kTfLiteInt8) {
-    EvalAddNQuantized<int8_t>(context, node, output);
-  } else {
-    MicroPrintf("ADD_N only supports FLOAT32 and INT8, got %s.",
-                TfLiteTypeGetName(output->type));
-    return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_ADD_N() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/arg_min_max.cc

@@ -1,115 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/arg_min_max.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/micro_utils.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace arg_min_max {
-
-constexpr int kInputTensor = 0;
-constexpr int kAxis = 1;
-constexpr int kOutputTensor = 0;
-
-template <typename T1, typename T2, typename T3>
-inline void ArgMinMaxHelper(const RuntimeShape& input1_shape,
-                            const T1* input1_data, const T3* input2_data,
-                            const RuntimeShape& output_shape, T2* output_data,
-                            bool is_arg_max) {
-  if (is_arg_max) {
-    reference_ops::ArgMinMax(input1_shape, input1_data, input2_data,
-                             output_shape, output_data, micro::Greater());
-  } else {
-    reference_ops::ArgMinMax(input1_shape, input1_data, input2_data,
-                             output_shape, output_data, micro::Less());
-  }
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node, bool is_arg_max) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  const TfLiteEvalTensor* axis =
-      tflite::micro::GetEvalInput(context, node, kAxis);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-#define TF_LITE_ARG_MIN_MAX(data_type, axis_type, output_type)       \
-  ArgMinMaxHelper(tflite::micro::GetTensorShape(input),              \
-                  tflite::micro::GetTensorData<data_type>(input),    \
-                  tflite::micro::GetTensorData<axis_type>(axis),     \
-                  tflite::micro::GetTensorShape(output),             \
-                  tflite::micro::GetTensorData<output_type>(output), \
-                  is_arg_max)
-  if (axis->type == kTfLiteInt32) {
-    if (output->type == kTfLiteInt32) {
-      switch (input->type) {
-        case kTfLiteFloat32:
-          TF_LITE_ARG_MIN_MAX(float, int32_t, int32_t);
-          break;
-        case kTfLiteInt8:
-          TF_LITE_ARG_MIN_MAX(int8_t, int32_t, int32_t);
-          break;
-        default:
-          MicroPrintf(
-              "Only float32, uint8_t and int8_t are "
-              "supported currently, got %s.",
-              TfLiteTypeGetName(input->type));
-          return kTfLiteError;
-      }
-    } else {
-      MicroPrintf("Only int32_t are supported currently, got %s.",
-                  TfLiteTypeGetName(output->type));
-      return kTfLiteError;
-    }
-  } else {
-    MicroPrintf("Only int32_t are supported currently, got %s.",
-                TfLiteTypeGetName(axis->type));
-    return kTfLiteError;
-  }
-
-#undef TF_LITE_ARG_MIN_MAX
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus ArgMinEval(TfLiteContext* context, TfLiteNode* node) {
-  return Eval(context, node, false);
-}
-
-TfLiteStatus ArgMaxEval(TfLiteContext* context, TfLiteNode* node) {
-  return Eval(context, node, true);
-}
-
-}  // namespace arg_min_max
-
-TfLiteRegistration Register_ARG_MAX() {
-  return tflite::micro::RegisterOp(nullptr, nullptr, arg_min_max::ArgMaxEval);
-}
-
-TfLiteRegistration Register_ARG_MIN() {
-  return tflite::micro::RegisterOp(nullptr, nullptr, arg_min_max::ArgMinEval);
-}
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/batch_to_space_nd.cc

@@ -1,111 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/batch_to_space_nd.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-
-namespace tflite {
-
-namespace {
-
-constexpr int kInputTensor = 0;
-constexpr int kBlockShapeTensor = 1;
-constexpr int kCropsTensor = 2;
-constexpr int kOutputTensor = 0;
-
-// Currently, only 3D NHC and 4D NHWC input/output op_context are supported.
-// In case of 3D input, it will be extended to 3D NHWC by adding W=1.
-// The 4D array need to have exactly 2 spatial dimensions.
-// TODO(b/149952582): Support arbitrary dimension in SpaceToBatchND.
-const int kInputOutputMinDimensionNum = 3;
-const int kInputOutputMaxDimensionNum = 4;
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, input != nullptr && output != nullptr);
-
-  TF_LITE_ENSURE(context, NumDimensions(input) >= kInputOutputMinDimensionNum);
-  TF_LITE_ENSURE(context, NumDimensions(output) >= kInputOutputMinDimensionNum);
-  TF_LITE_ENSURE(context, NumDimensions(input) <= kInputOutputMaxDimensionNum);
-  TF_LITE_ENSURE(context, NumDimensions(output) <= kInputOutputMaxDimensionNum);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  const TfLiteEvalTensor* block_shape =
-      tflite::micro::GetEvalInput(context, node, kBlockShapeTensor);
-  const TfLiteEvalTensor* crops =
-      tflite::micro::GetEvalInput(context, node, kCropsTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  switch (input->type) {  // Already know in/out types are same.
-    case kTfLiteFloat32:
-      reference_ops::BatchToSpaceND(
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<float>(input),
-          tflite::micro::GetTensorShape(block_shape),
-          tflite::micro::GetTensorData<int32_t>(block_shape),
-          tflite::micro::GetTensorShape(crops),
-          tflite::micro::GetTensorData<int32_t>(crops),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<float>(output));
-      break;
-    case kTfLiteInt8:
-      reference_ops::BatchToSpaceND(
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(block_shape),
-          tflite::micro::GetTensorData<int32_t>(block_shape),
-          tflite::micro::GetTensorShape(crops),
-          tflite::micro::GetTensorData<int32_t>(crops),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int8_t>(output));
-      break;
-    default:
-      MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
-                  input->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace.
-
-TfLiteRegistration Register_BATCH_TO_SPACE_ND() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/circular_buffer.cc

@@ -1,116 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/kernels/circular_buffer.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/flatbuffer_utils.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-/*
- * The circular buffer custom operator is used to implement strided streaming
- * convolutions on TFLite Micro.  Each time this operator is invoked, it checks
- * whether or not to run, based on a predetermined stride in time.  If the op
- * runs, it inserts the input into the end of the output buffer and shifts the
- * output values towards the start of the buffer.  It discards the oldest value
- * in the output buffer.
- *
- * Input: [<input N+1]
- * Before shifting:
- * Output: [<input 1>, <input 2>, <input ...>, <input N>]
- *
- * After shifting:
- * Output: [<input 2>, <input 3>, <input ...>, <input N+1>]
- *
- * We make some assumptions in this custom operator:
- * - Input shape must be [1, 1, 1, depth]
- * - Output shape must be [1, num_slots, 1, depth]
- * - Input and output types must match.
- * - Input and output quantization params must be identical.
- */
-namespace tflite {
-
-void* CircularBufferInit(TfLiteContext* context, const char* buffer,
-                         size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  OpDataCircularBuffer* op_data = static_cast<OpDataCircularBuffer*>(
-      context->AllocatePersistentBuffer(context, sizeof(OpDataCircularBuffer)));
-
-  if (buffer != nullptr && length > 0) {
-    const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
-    tflite::FlexbufferWrapper wrapper(buffer_t, length);
-    op_data->cycles_max = wrapper.ElementAsInt32(kCircularBufferCyclesMaxIndex);
-  } else {
-    op_data->cycles_max = 0;
-  }
-
-  return op_data;
-}
-
-// Shifts buffer over by the output depth, and write new input to end of buffer.
-// num_slots is the number of samples stored in the output buffer.
-// depth is the size of each sample.
-void EvalInt8(const int8_t* input, int num_slots, int depth, int8_t* output) {
-  memmove(output, &output[depth], (num_slots - 1) * depth);
-  memcpy(&output[(num_slots - 1) * depth], input, depth);
-}
-
-TfLiteStatus CircularBufferEval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kCircularBufferInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kCircularBufferOutputTensor);
-
-  TFLITE_DCHECK(node->user_data != nullptr);
-  OpDataCircularBuffer* data =
-      reinterpret_cast<OpDataCircularBuffer*>(node->user_data);
-
-  int num_slots = output->dims->data[1];
-  int depth = output->dims->data[2] * output->dims->data[3];
-
-  if (input->type == kTfLiteInt8) {
-    EvalInt8(tflite::micro::GetTensorData<int8_t>(input), num_slots, depth,
-             tflite::micro::GetTensorData<int8_t>(output));
-  } else {
-    MicroPrintf("Type %s (%d) not supported.",
-                       TfLiteTypeGetName(input->type), input->type);
-    return kTfLiteError;
-  }
-
-  if (--data->cycles_until_run != 0) {
-    // Signal the interpreter to end current run if the delay before op invoke
-    // has not been reached.
-    // TODO(b/149795762): Add kTfLiteAbort to TfLiteStatus enum.
-    return static_cast<TfLiteStatus>(kTfLiteAbort);
-  }
-
-  data->cycles_until_run = data->cycles_max;
-
-  return kTfLiteOk;
-}
-
-TfLiteRegistration* Register_CIRCULAR_BUFFER() {
-  static TfLiteRegistration r = tflite::micro::RegisterOp(
-      CircularBufferInit, CircularBufferPrepare, CircularBufferEval);
-  return &r;
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/comparisons.cc

@@ -1,617 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/kernels/internal/reference/comparisons.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace comparisons {
-namespace {
-
-struct OpData {
-  ComparisonParams params;
-};
-
-constexpr int kInputTensor1 = 0;
-constexpr int kInputTensor2 = 1;
-constexpr int kOutputTensor = 0;
-
-TfLiteStatus EqualEval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const OpData* data = static_cast<const OpData*>(node->user_data);
-
-  const TfLiteEvalTensor* input1 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor1);
-  const TfLiteEvalTensor* input2 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor2);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1);
-  RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2);
-  RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
-  bool* output_data = tflite::micro::GetTensorData<bool>(output);
-
-  bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
-  switch (input1->type) {
-    case kTfLiteBool:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<bool>(input1), input2_shape,
-                tflite::micro::GetTensorData<bool>(input2), output_shape,
-                output_data)
-          : reference_ops::EqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<bool>(input1), input2_shape,
-                tflite::micro::GetTensorData<bool>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteFloat32:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<float>(input1), input2_shape,
-                tflite::micro::GetTensorData<float>(input2), output_shape,
-                output_data)
-          : reference_ops::EqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<float>(input1), input2_shape,
-                tflite::micro::GetTensorData<float>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt32:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int32_t>(input2), output_shape,
-                output_data)
-          : reference_ops::EqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int32_t>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt64:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int64_t>(input2), output_shape,
-                output_data)
-          : reference_ops::EqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int64_t>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt8:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowEqualWithScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int8_t>(input2), output_shape,
-                output_data)
-          : reference_ops::EqualWithScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int8_t>(input2), output_shape,
-                output_data);
-      break;
-    default:
-      MicroPrintf("Type %s (%d) not supported.",
-                  TfLiteTypeGetName(input1->type), input1->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-// TODO(renjieliu): Refactor the logic to avoid duplications.
-TfLiteStatus NotEqualEval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const OpData* data = static_cast<const OpData*>(node->user_data);
-
-  const TfLiteEvalTensor* input1 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor1);
-  const TfLiteEvalTensor* input2 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor2);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1);
-  RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2);
-  RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
-  bool* output_data = tflite::micro::GetTensorData<bool>(output);
-
-  bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
-  switch (input1->type) {
-    case kTfLiteBool:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowNotEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<bool>(input1), input2_shape,
-                tflite::micro::GetTensorData<bool>(input2), output_shape,
-                output_data)
-          : reference_ops::NotEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<bool>(input1), input2_shape,
-                tflite::micro::GetTensorData<bool>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteFloat32:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowNotEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<float>(input1), input2_shape,
-                tflite::micro::GetTensorData<float>(input2), output_shape,
-                output_data)
-          : reference_ops::NotEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<float>(input1), input2_shape,
-                tflite::micro::GetTensorData<float>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt32:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowNotEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int32_t>(input2), output_shape,
-                output_data)
-          : reference_ops::NotEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int32_t>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt64:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowNotEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int64_t>(input2), output_shape,
-                output_data)
-          : reference_ops::NotEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int64_t>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt8:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowNotEqualWithScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int8_t>(input2), output_shape,
-                output_data)
-          : reference_ops::NotEqualWithScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int8_t>(input2), output_shape,
-                output_data);
-      break;
-    default:
-      MicroPrintf("Type %s (%d) not supported.",
-                  TfLiteTypeGetName(input1->type), input1->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus GreaterEval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const OpData* data = static_cast<const OpData*>(node->user_data);
-
-  const TfLiteEvalTensor* input1 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor1);
-  const TfLiteEvalTensor* input2 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor2);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1);
-  RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2);
-  RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
-  bool* output_data = tflite::micro::GetTensorData<bool>(output);
-
-  bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
-  switch (input1->type) {
-    case kTfLiteFloat32:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowGreaterNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<float>(input1), input2_shape,
-                tflite::micro::GetTensorData<float>(input2), output_shape,
-                output_data)
-          : reference_ops::GreaterNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<float>(input1), input2_shape,
-                tflite::micro::GetTensorData<float>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt32:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowGreaterNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int32_t>(input2), output_shape,
-                output_data)
-          : reference_ops::GreaterNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int32_t>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt64:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowGreaterNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int64_t>(input2), output_shape,
-                output_data)
-          : reference_ops::GreaterNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int64_t>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt8:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowGreaterWithScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int8_t>(input2), output_shape,
-                output_data)
-          : reference_ops::GreaterWithScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int8_t>(input2), output_shape,
-                output_data);
-      break;
-    default:
-      MicroPrintf("Type %s (%d) not supported.",
-                  TfLiteTypeGetName(input1->type), input1->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus GreaterEqualEval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const OpData* data = static_cast<const OpData*>(node->user_data);
-
-  const TfLiteEvalTensor* input1 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor1);
-  const TfLiteEvalTensor* input2 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor2);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1);
-  RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2);
-  RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
-  bool* output_data = tflite::micro::GetTensorData<bool>(output);
-
-  bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
-  switch (input1->type) {
-    case kTfLiteFloat32:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowGreaterEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<float>(input1), input2_shape,
-                tflite::micro::GetTensorData<float>(input2), output_shape,
-                output_data)
-          : reference_ops::GreaterEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<float>(input1), input2_shape,
-                tflite::micro::GetTensorData<float>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt32:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowGreaterEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int32_t>(input2), output_shape,
-                output_data)
-          : reference_ops::GreaterEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int32_t>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt64:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowGreaterEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int64_t>(input2), output_shape,
-                output_data)
-          : reference_ops::GreaterEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int64_t>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt8:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowGreaterEqualWithScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int8_t>(input2), output_shape,
-                output_data)
-          : reference_ops::GreaterEqualWithScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int8_t>(input2), output_shape,
-                output_data);
-      break;
-    default:
-      MicroPrintf("Type %s (%d) not supported.",
-                  TfLiteTypeGetName(input1->type), input1->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus LessEval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const OpData* data = static_cast<const OpData*>(node->user_data);
-
-  const TfLiteEvalTensor* input1 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor1);
-  const TfLiteEvalTensor* input2 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor2);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1);
-  RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2);
-  RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
-  bool* output_data = tflite::micro::GetTensorData<bool>(output);
-
-  bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
-  switch (input1->type) {
-    case kTfLiteFloat32:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowLessNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<float>(input1), input2_shape,
-                tflite::micro::GetTensorData<float>(input2), output_shape,
-                output_data)
-          : reference_ops::LessNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<float>(input1), input2_shape,
-                tflite::micro::GetTensorData<float>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt32:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowLessNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int32_t>(input2), output_shape,
-                output_data)
-          : reference_ops::LessNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int32_t>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt64:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowLessNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int64_t>(input2), output_shape,
-                output_data)
-          : reference_ops::LessNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int64_t>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt8:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowLessWithScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int8_t>(input2), output_shape,
-                output_data)
-          : reference_ops::LessWithScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int8_t>(input2), output_shape,
-                output_data);
-      break;
-    default:
-      MicroPrintf("Type %s (%d) not supported.",
-                  TfLiteTypeGetName(input1->type), input1->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus LessEqualEval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const OpData* data = static_cast<const OpData*>(node->user_data);
-
-  const TfLiteEvalTensor* input1 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor1);
-  const TfLiteEvalTensor* input2 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor2);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  RuntimeShape input1_shape = tflite::micro::GetTensorShape(input1);
-  RuntimeShape input2_shape = tflite::micro::GetTensorShape(input2);
-  RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
-  bool* output_data = tflite::micro::GetTensorData<bool>(output);
-
-  bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
-  switch (input1->type) {
-    case kTfLiteFloat32:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowLessEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<float>(input1), input2_shape,
-                tflite::micro::GetTensorData<float>(input2), output_shape,
-                output_data)
-          : reference_ops::LessEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<float>(input1), input2_shape,
-                tflite::micro::GetTensorData<float>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt32:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowLessEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int32_t>(input2), output_shape,
-                output_data)
-          : reference_ops::LessEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int32_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int32_t>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt64:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowLessEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int64_t>(input2), output_shape,
-                output_data)
-          : reference_ops::LessEqualNoScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int64_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int64_t>(input2), output_shape,
-                output_data);
-      break;
-    case kTfLiteInt8:
-      requires_broadcast
-          ? reference_ops::Broadcast4DSlowLessEqualWithScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int8_t>(input2), output_shape,
-                output_data)
-          : reference_ops::LessEqualWithScaling(
-                data->params, input1_shape,
-                tflite::micro::GetTensorData<int8_t>(input1), input2_shape,
-                tflite::micro::GetTensorData<int8_t>(input2), output_shape,
-                output_data);
-      break;
-    default:
-      MicroPrintf("Type %s (%d) not supported.",
-                  TfLiteTypeGetName(input1->type), input1->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(OpData));
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  OpData* data = static_cast<OpData*>(node->user_data);
-
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TfLiteTensor* input1 =
-      micro_context->AllocateTempInputTensor(node, kInputTensor1);
-  TF_LITE_ENSURE(context, input1 != nullptr);
-  TfLiteTensor* input2 =
-      micro_context->AllocateTempInputTensor(node, kInputTensor2);
-  TF_LITE_ENSURE(context, input2 != nullptr);
-
-  if (input1->type == kTfLiteInt8) {
-    auto input1_offset = -input1->params.zero_point;
-    auto input2_offset = -input2->params.zero_point;
-    const int kLeftShift = 8;
-
-    int32_t input1_multiplier;
-    int input1_shift;
-    QuantizeMultiplierSmallerThanOneExp(
-        static_cast<double>(input1->params.scale), &input1_multiplier,
-        &input1_shift);
-    int32_t input2_multiplier;
-    int input2_shift;
-    QuantizeMultiplierSmallerThanOneExp(
-        static_cast<double>(input2->params.scale), &input2_multiplier,
-        &input2_shift);
-
-    data->params.left_shift = kLeftShift;
-    data->params.input1_offset = input1_offset;
-    data->params.input1_multiplier = input1_multiplier;
-    data->params.input1_shift = input1_shift;
-    data->params.input2_offset = input2_offset;
-    data->params.input2_multiplier = input2_multiplier;
-    data->params.input2_shift = input2_shift;
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(input1);
-  micro_context->DeallocateTempTfLiteTensor(input2);
-
-  return kTfLiteOk;
-}
-
-}  // namespace comparisons
-
-TfLiteRegistration Register_EQUAL() {
-  return tflite::micro::RegisterOp(comparisons::Init, comparisons::Prepare,
-                                   comparisons::EqualEval);
-}
-
-TfLiteRegistration Register_NOT_EQUAL() {
-  return tflite::micro::RegisterOp(comparisons::Init, comparisons::Prepare,
-                                   comparisons::NotEqualEval);
-}
-
-TfLiteRegistration Register_GREATER() {
-  return tflite::micro::RegisterOp(comparisons::Init, comparisons::Prepare,
-                                   comparisons::GreaterEval);
-}
-
-TfLiteRegistration Register_GREATER_EQUAL() {
-  return tflite::micro::RegisterOp(comparisons::Init, comparisons::Prepare,
-                                   comparisons::GreaterEqualEval);
-}
-
-TfLiteRegistration Register_LESS() {
-  return tflite::micro::RegisterOp(comparisons::Init, comparisons::Prepare,
-                                   comparisons::LessEval);
-}
-
-TfLiteRegistration Register_LESS_EQUAL() {
-  return tflite::micro::RegisterOp(comparisons::Init, comparisons::Prepare,
-                                   comparisons::LessEqualEval);
-}
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/concatenation.cc

@@ -1,262 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/kernels/internal/reference/concatenation.h"
-
-#include <cstdint>
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/portable_tensor.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace concatenation {
-
-constexpr int kMaxInputNum = 10;  // Maximum number of input tensors
-constexpr int kOutputTensor = 0;
-
-struct OpData {
-  ConcatenationParams params;
-};
-
-// Handles negative axis index, coerces to positive index value.
-inline int CalculatePositiveAxis(int axis, const TfLiteTensor* output_tensor) {
-  if (axis >= 0) {
-    return axis;
-  } else {
-    return NumDimensions(output_tensor) + axis;
-  }
-}
-
-// The following functions are helpers to get tensor data in the format that the
-// reference op implementation expects. They provide the same functionality as
-// class VectorOfTensors and class VectorOfQuantizedTensors in TFLite.
-
-// Gets shapes from a list of tensors.
-inline void GetAllInputTensorShapes(const TfLiteContext* context,
-                                    const TfLiteNode* node,
-                                    RuntimeShape all_shapes[kMaxInputNum]) {
-  TFLITE_DCHECK(context != nullptr);
-  TFLITE_DCHECK(node != nullptr);
-  for (int i = 0; i < node->inputs->size; ++i) {
-    const TfLiteEvalTensor* t = tflite::micro::GetEvalInput(context, node, i);
-    RuntimeShape shape = tflite::micro::GetTensorShape(t);
-    all_shapes[i].ReplaceWith(shape.DimensionsCount(), shape.DimsData());
-  }
-}
-
-// Get shape pointers from a list of shapes.
-inline void GetShapesPointers(const RuntimeShape* shapes, size_t num,
-                              const RuntimeShape* pointers[]) {
-  for (size_t i = 0; i < num; ++i) {
-    pointers[i] = &shapes[i];
-  }
-}
-
-// Gets data pointers from a list of tensors.
-template <typename T>
-inline void GetAllInputTensorData(const TfLiteContext* context,
-                                  const TfLiteNode* node,
-                                  T* all_data[kMaxInputNum]) {
-  TFLITE_DCHECK(context != nullptr);
-  TFLITE_DCHECK(node != nullptr);
-  for (int i = 0; i < node->inputs->size; ++i) {
-    const TfLiteEvalTensor* t = tflite::micro::GetEvalInput(context, node, i);
-    all_data[i] = tflite::micro::GetTensorData<T>(t);
-  }
-}
-
-template <typename data_type>
-void EvalUnquantized(TfLiteContext* context, TfLiteNode* node) {
-  // Collect the shapes and data pointer of input tensors
-  RuntimeShape inputs_shape[kMaxInputNum];
-  const RuntimeShape* inputs_shape_ptr[kMaxInputNum];
-  const data_type* inputs_data[kMaxInputNum];
-  GetAllInputTensorShapes(context, node, inputs_shape);
-  GetShapesPointers(inputs_shape, node->inputs->size, inputs_shape_ptr);
-  GetAllInputTensorData(context, node, inputs_data);
-
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const OpData* data = static_cast<const OpData*>(node->user_data);
-
-  reference_ops::Concatenation(data->params, inputs_shape_ptr, inputs_data,
-                               tflite::micro::GetTensorShape(output),
-                               tflite::micro::GetTensorData<data_type>(output));
-}
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(OpData));
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  // This function only checks the types. Additional shape validations are
-  // performed in the reference implementation called during Eval().
-  const TfLiteConcatenationParams* params =
-      reinterpret_cast<TfLiteConcatenationParams*>(node->builtin_data);
-
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TfLiteTensor* input_tensor = micro_context->AllocateTempInputTensor(node, 0);
-  TF_LITE_ENSURE(context, input_tensor != nullptr);
-  TfLiteType input_type = input_tensor->type;
-  TfLiteTensor* output_tensor =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output_tensor != nullptr);
-  TfLiteType output_type = output_tensor->type;
-
-  micro_context->DeallocateTempTfLiteTensor(input_tensor);
-  micro_context->DeallocateTempTfLiteTensor(output_tensor);
-
-  // Check activation and input type
-  TF_LITE_ENSURE_EQ(context, params->activation, kTfLiteActNone);
-  TF_LITE_ENSURE(context,
-                 input_type == kTfLiteFloat32 || input_type == kTfLiteInt8 ||
-                     input_type == kTfLiteInt16 || input_type == kTfLiteInt32 ||
-                     input_type == kTfLiteInt64 || input_type == kTfLiteBool);
-
-  // Output type must match input type
-  TF_LITE_ENSURE_EQ(context, output_type, input_type);
-
-  // This implementation does not support large number of input tensors
-  const int num_inputs = NumInputs(node);
-  TF_LITE_ENSURE(context, num_inputs <= kMaxInputNum);
-
-  // Shapes with dimensions >4 are not yet supported with static allocation.
-  for (int i = 0; i < num_inputs; ++i) {
-    TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, i);
-    TF_LITE_ENSURE(context, input != nullptr);
-    int num_dimensions = NumDimensions(input);
-
-    if (num_dimensions > RuntimeShape::kMaxSmallSize) {
-      MicroPrintf(
-          "Op Concatenation does not currently support num dimensions > %d "
-          "Tensor has %d dimensions.",
-          RuntimeShape::kMaxSmallSize, num_dimensions);
-      return kTfLiteError;
-    }
-    micro_context->DeallocateTempTfLiteTensor(input);
-  }
-
-  // Calculate OpData.
-  TFLITE_DCHECK(node->user_data != nullptr);
-  OpData* data = static_cast<OpData*>(node->user_data);
-
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  switch (output_type) {  // Already know in/outtypes are same.
-    case kTfLiteBool:
-    case kTfLiteFloat32:
-    case kTfLiteInt16:
-    case kTfLiteInt32:
-    case kTfLiteInt64: {
-      data->params.axis = CalculatePositiveAxis(params->axis, output);
-      data->params.inputs_count = node->inputs->size;
-      break;
-    }
-    case kTfLiteInt8: {
-      data->params.axis = CalculatePositiveAxis(params->axis, output);
-      data->params.inputs_count = node->inputs->size;
-
-      float* input_scales =
-          reinterpret_cast<float*>(context->AllocatePersistentBuffer(
-              context, node->inputs->size * sizeof(float)));
-
-      int32_t* input_zero_points =
-          reinterpret_cast<int32_t*>(context->AllocatePersistentBuffer(
-              context, node->inputs->size * sizeof(int32_t)));
-
-      // Allocate persistent scale and zeropoint buffers.
-      // Store input scale and zero point values in OpParams:
-      for (int i = 0; i < node->inputs->size; ++i) {
-        TfLiteTensor* t = micro_context->AllocateTempInputTensor(node, i);
-        TF_LITE_ENSURE(context, t != nullptr);
-        input_scales[i] = t->params.scale;
-        input_zero_points[i] = t->params.zero_point;
-        micro_context->DeallocateTempTfLiteTensor(t);
-      }
-
-      data->params.input_scale = input_scales;
-      data->params.input_zeropoint = input_zero_points;
-      data->params.output_zeropoint = output->params.zero_point;
-      data->params.output_scale = output->params.scale;
-      break;
-    }
-    default:
-      MicroPrintf("Op Concatenation does not currently support Type '%s'.",
-                  TfLiteTypeGetName(output_type));
-      return kTfLiteError;
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* output_tensor =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  TF_LITE_ENSURE(context, output_tensor != nullptr);
-  TfLiteType output_type = output_tensor->type;
-
-  switch (output_type) {  // Already know in/outtypes are same.
-    case kTfLiteFloat32:
-      EvalUnquantized<float>(context, node);
-      break;
-    case kTfLiteInt32:
-      EvalUnquantized<int32_t>(context, node);
-      break;
-    case kTfLiteInt8:
-      EvalUnquantized<int8_t>(context, node);
-      break;
-    case kTfLiteInt64:
-      EvalUnquantized<int64_t>(context, node);
-      break;
-    case kTfLiteInt16:
-      EvalUnquantized<int16_t>(context, node);
-      break;
-    case kTfLiteBool:
-      EvalUnquantized<bool>(context, node);
-      break;
-
-    default:
-      MicroPrintf("Op Concatenation does not currently support Type '%s'.",
-                  TfLiteTypeGetName(output_type));
-      return kTfLiteError;
-  }
-
-  return kTfLiteOk;
-}
-
-}  // namespace concatenation
-
-TfLiteRegistration Register_CONCATENATION() {
-  return tflite::micro::RegisterOp(concatenation::Init, concatenation::Prepare,
-                                   concatenation::Eval);
-}
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/cumsum.cc

@@ -1,174 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/cumsum.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-constexpr int kInputTensor = 0;
-constexpr int kAxisTensor = 1;
-constexpr int kOutputTensor = 0;
-
-constexpr int kCumSumIntegerShift = 20;
-
-// only used with INT8 tensors
-struct OpData {
-  int32_t output_activation_min;
-  int32_t output_activation_max;
-  int32_t input_offset;
-  int32_t output_offset;
-  int32_t input_multiplier;
-  int32_t output_multiplier;
-  int input_shift;
-  int output_shift;
-  int left_shift;
-};
-
-TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node) {
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TfLiteTensor* axis =
-      micro_context->AllocateTempInputTensor(node, kAxisTensor);
-
-  TF_LITE_ENSURE(context,
-                 input->type == kTfLiteFloat32 || input->type == kTfLiteInt8);
-  TF_LITE_ENSURE_EQ(context, axis->type, kTfLiteInt32);
-
-  TF_LITE_ENSURE_EQ(context, NumElements(axis), 1);
-
-  TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
-
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-
-  TF_LITE_ENSURE_EQ(context, input->type, output->type);
-  TF_LITE_ENSURE(context, HaveSameShapes(input, output));
-
-  if (output->type == kTfLiteInt8) {
-    node->user_data =
-        context->AllocatePersistentBuffer(context, sizeof(OpData));
-    OpData* data = static_cast<OpData*>(node->user_data);
-
-    // 8bit -> 8bit general quantized path, with general rescalings
-    data->input_offset = -input->params.zero_point;
-    data->output_offset = output->params.zero_point;
-    data->left_shift = kCumSumIntegerShift;
-    const double twice_max_input_scale =
-        2 * static_cast<double>(input->params.scale);
-    const double real_input_multiplier =
-        static_cast<double>(input->params.scale) / twice_max_input_scale;
-    const double real_output_multiplier =
-        twice_max_input_scale /
-        ((1 << data->left_shift) * static_cast<double>(output->params.scale));
-
-    QuantizeMultiplierSmallerThanOneExp(
-        real_input_multiplier, &data->input_multiplier, &data->input_shift);
-
-    QuantizeMultiplierSmallerThanOneExp(
-        real_output_multiplier, &data->output_multiplier, &data->output_shift);
-
-    TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
-        context, kTfLiteActNone, output, &data->output_activation_min,
-        &data->output_activation_max));
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(axis);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  return CalculateOpData(context, node);
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  const TfLiteEvalTensor* axis_tensor =
-      tflite::micro::GetEvalInput(context, node, kAxisTensor);
-
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  auto* cs_params = static_cast<TfLiteCumsumParams*>(node->builtin_data);
-  auto input_shape = tflite::micro::GetTensorShape(input);
-
-  int32_t axis = *tflite::micro::GetTensorData<int32_t>(axis_tensor);
-  if (axis < 0) axis += input_shape.DimensionsCount();
-
-  if (axis < 0 || axis >= input_shape.DimensionsCount()) {
-    MicroPrintf("CUMSUM Invalid axis: %d", axis);
-    return kTfLiteError;
-  }
-
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      reference_ops::CumSum(tflite::micro::GetTensorData<float>(input),
-                            input_shape, axis, cs_params->exclusive,
-                            cs_params->reverse,
-                            tflite::micro::GetTensorData<float>(output));
-      return kTfLiteOk;
-    } break;
-
-    case kTfLiteInt8: {
-      auto* data = static_cast<OpData*>(node->user_data);
-      ArithmeticParams params;
-      params.left_shift = data->left_shift;
-      params.input1_offset = data->input_offset;
-      params.input1_multiplier = data->input_multiplier;
-      params.input1_shift = data->input_shift;
-      params.output_offset = data->output_offset;
-      params.output_multiplier = data->output_multiplier;
-      params.output_shift = data->output_shift;
-      SetActivationParams(data->output_activation_min,
-                          data->output_activation_max, &params);
-      reference_ops::CumSum(params, tflite::micro::GetTensorData<int8_t>(input),
-                            input_shape, axis, cs_params->exclusive,
-                            cs_params->reverse,
-                            tflite::micro::GetTensorData<int8_t>(output));
-      return kTfLiteOk;
-    } break;
-
-    default: {
-      MicroPrintf("CUMSUM only supports FLOAT32 and INT8, got %s.",
-                  TfLiteTypeGetName(output->type));
-      return kTfLiteError;
-    }
-  }
-
-  return kTfLiteError;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_CUMSUM() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/depth_to_space.cc

@@ -1,141 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/kernels/internal/reference/depth_to_space.h"
-
-#include <stdint.h>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-constexpr int kInputTensor = 0;
-constexpr int kOutputTensor = 0;
-
-// input/output tensor shape rank associations
-constexpr int kBatchRank = 0;
-constexpr int kHeightRank = 1;
-constexpr int kWidthRank = 2;
-constexpr int kDepthRank = 3;
-
-TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node) {
-  auto* params =
-      reinterpret_cast<TfLiteDepthToSpaceParams*>(node->builtin_data);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
-
-  auto data_type = output->type;
-  TF_LITE_ENSURE(context,
-                 data_type == kTfLiteFloat32 || data_type == kTfLiteInt8);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-
-  const int block_size = params->block_size;
-  TF_LITE_ENSURE(context, block_size > 0);
-  const int input_height = input->dims->data[kHeightRank];
-  const int input_width = input->dims->data[kWidthRank];
-  const int input_channels = input->dims->data[kDepthRank];
-  int output_height = input_height * block_size;
-  int output_width = input_width * block_size;
-  int output_channels = input_channels / block_size / block_size;
-
-  TF_LITE_ENSURE_EQ(context, input_height, output_height / block_size);
-  TF_LITE_ENSURE_EQ(context, input_width, output_width / block_size);
-  TF_LITE_ENSURE_EQ(context, input_channels,
-                    output_channels * block_size * block_size);
-
-  // We must update the output tensor dimensions.
-  // The dims storage is expected to be the same area in memory
-  // for both TfLiteTensor and TfLiteEvalTensor.  This is important
-  // because TfLiteTensor in the MicroInterpreter is a temporary
-  // allocation.  For the KernelRunner interpreter, TfLiteEvalTensor
-  // is a temporary allocation.  We must therefore relocate the dims
-  // from the FlatBuffer to the persistant storage arena.
-  TfLiteEvalTensor* output_eval =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  TF_LITE_ENSURE_OK(context, tflite::micro::CreateWritableTensorDimsWithCopy(
-                                 context, output, output_eval));
-  output->dims->data[kBatchRank] = input->dims->data[kBatchRank];
-  output->dims->data[kHeightRank] = output_height;
-  output->dims->data[kWidthRank] = output_width;
-  output->dims->data[kDepthRank] = output_channels;
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  return CalculateOpData(context, node);
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  auto* params =
-      reinterpret_cast<TfLiteDepthToSpaceParams*>(node->builtin_data);
-
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  tflite::DepthToSpaceParams op_params;
-  op_params.block_size = static_cast<int32_t>(params->block_size);
-
-  switch (input->type) {  // Already know in/out types are same.
-    case kTfLiteFloat32:
-      reference_ops::DepthToSpace(op_params,
-                                  tflite::micro::GetTensorShape(input),
-                                  tflite::micro::GetTensorData<float>(input),
-                                  tflite::micro::GetTensorShape(output),
-                                  tflite::micro::GetTensorData<float>(output));
-      break;
-    case kTfLiteInt8:
-      reference_ops::DepthToSpace(op_params,
-                                  tflite::micro::GetTensorShape(input),
-                                  tflite::micro::GetTensorData<int8_t>(input),
-                                  tflite::micro::GetTensorShape(output),
-                                  tflite::micro::GetTensorData<int8_t>(output));
-      break;
-    default:
-      MicroPrintf("DEPTH_TO_SPACE only supports FLOAT32 and INT8, got %s.",
-                  TfLiteTypeGetName(output->type));
-      return kTfLiteError;
-  }
-
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_DEPTH_TO_SPACE() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/depthwise_conv.cc

@@ -1,98 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/kernels/depthwise_conv.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h"
-#include "tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/padding.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(OpDataConv));
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-
-  auto& params =
-      *(reinterpret_cast<TfLiteDepthwiseConvParams*>(node->builtin_data));
-  const OpDataConv& data = *(static_cast<const OpDataConv*>(node->user_data));
-
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kDepthwiseConvOutputTensor);
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kDepthwiseConvInputTensor);
-  const TfLiteEvalTensor* filter =
-      tflite::micro::GetEvalInput(context, node, kDepthwiseConvWeightsTensor);
-  const TfLiteEvalTensor* bias =
-      (NumInputs(node) == 3)
-          ? tflite::micro::GetEvalInput(context, node, kDepthwiseConvBiasTensor)
-          : nullptr;
-
-  switch (input->type) {  // Already know in/out types are same.
-    case kTfLiteFloat32: {
-      tflite::reference_ops::DepthwiseConv(
-          DepthwiseConvParamsFloat(params, data),
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<float>(input),
-          tflite::micro::GetTensorShape(filter),
-          tflite::micro::GetTensorData<float>(filter),
-          tflite::micro::GetTensorShape(bias),
-          tflite::micro::GetOptionalTensorData<float>(bias),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<float>(output));
-      break;
-    }
-    case kTfLiteInt8: {
-      reference_integer_ops::DepthwiseConvPerChannel(
-          DepthwiseConvParamsQuantized(params, data),
-          data.per_channel_output_multiplier, data.per_channel_output_shift,
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(filter),
-          tflite::micro::GetTensorData<int8_t>(filter),
-          tflite::micro::GetTensorShape(bias),
-          tflite::micro::GetOptionalTensorData<int32_t>(bias),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int8_t>(output));
-      break;
-    }
-    default:
-      MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
-                  input->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_DEPTHWISE_CONV_2D() {
-  return tflite::micro::RegisterOp(Init, DepthwiseConvPrepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/div.cc

@@ -1,207 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/div.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-constexpr int kInputTensor1 = 0;
-constexpr int kInputTensor2 = 1;
-constexpr int kOutputTensor = 0;
-
-struct OpDataDiv {
-  // Parameters used in the quantized paths where the output is 8bit
-  int32_t input1_zero_point;
-  int32_t input2_zero_point;
-  int32_t output_zero_point;
-  int32_t output_activation_min;
-  int32_t output_activation_max;
-
-  // Parameters used in all quantized paths
-  int32_t output_multiplier;
-  int output_shift;
-};
-
-TfLiteStatus CalculateOpDataDiv(TfLiteContext* context, TfLiteTensor* input1,
-                                TfLiteTensor* input2, TfLiteTensor* output,
-                                TfLiteDivParams* params, OpDataDiv* data) {
-  TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
-  TF_LITE_ENSURE_TYPES_EQ(context, input1->type, output->type);
-
-  if (output->type == kTfLiteInt8) {
-    TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized(
-        context, params->activation, output, &data->output_activation_min,
-        &data->output_activation_max));
-    const double real_multiplier = static_cast<double>(
-        input1->params.scale / (input2->params.scale * output->params.scale));
-    QuantizeMultiplier(real_multiplier, &data->output_multiplier,
-                       &data->output_shift);
-    data->input1_zero_point = input1->params.zero_point;
-    data->input2_zero_point = input2->params.zero_point;
-    data->output_zero_point = output->params.zero_point;
-  }
-
-  return kTfLiteOk;
-}
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(OpDataDiv));
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-
-  MicroContext* micro_context = GetMicroContext(context);
-  TfLiteTensor* input1 =
-      micro_context->AllocateTempInputTensor(node, kInputTensor1);
-  TF_LITE_ENSURE(context, input1 != nullptr);
-  TfLiteTensor* input2 =
-      micro_context->AllocateTempInputTensor(node, kInputTensor2);
-  TF_LITE_ENSURE(context, input2 != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  OpDataDiv* data = static_cast<OpDataDiv*>(node->user_data);
-  auto* params = reinterpret_cast<TfLiteDivParams*>(node->builtin_data);
-
-  TF_LITE_ENSURE_STATUS(
-      CalculateOpDataDiv(context, input1, input2, output, params, data));
-
-  micro_context->DeallocateTempTfLiteTensor(input1);
-  micro_context->DeallocateTempTfLiteTensor(input2);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-void EvalDiv(TfLiteContext* context, TfLiteNode* node, TfLiteDivParams* params,
-             const OpDataDiv* data, const TfLiteEvalTensor* input1,
-             const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) {
-  tflite::ArithmeticParams op_params = {};
-
-#define TF_LITE_DIV(type, opname, data_type)                           \
-  data_type output_activation_min, output_activation_max;              \
-  CalculateActivationRange(params->activation, &output_activation_min, \
-                           &output_activation_max);                    \
-  SetActivationParams(output_activation_min, output_activation_max,    \
-                      &op_params);                                     \
-  type::opname(op_params, tflite::micro::GetTensorShape(input1),       \
-               tflite::micro::GetTensorData<data_type>(input1),        \
-               tflite::micro::GetTensorShape(input2),                  \
-               tflite::micro::GetTensorData<data_type>(input2),        \
-               tflite::micro::GetTensorShape(output),                  \
-               tflite::micro::GetTensorData<data_type>(output))
-
-  bool requires_broadcast = reference_ops::ProcessBroadcastShapes(
-      tflite::micro::GetTensorShape(input1),
-      tflite::micro::GetTensorShape(input2), &op_params);
-
-  if (requires_broadcast) {
-    TF_LITE_DIV(reference_ops, BroadcastDivSlow, float);
-  } else {
-    TF_LITE_DIV(reference_ops, Div, float);
-  }
-#undef TF_LITE_DIV
-}
-
-TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node,
-                           TfLiteDivParams* params, const OpDataDiv* data,
-                           const TfLiteEvalTensor* input1,
-                           const TfLiteEvalTensor* input2,
-                           TfLiteEvalTensor* output) {
-  tflite::ArithmeticParams op_params = {};
-
-#define TF_LITE_DIV(type, opname, dtype)                         \
-  type::opname(op_params, tflite::micro::GetTensorShape(input1), \
-               tflite::micro::GetTensorData<dtype>(input1),      \
-               tflite::micro::GetTensorShape(input2),            \
-               tflite::micro::GetTensorData<dtype>(input2),      \
-               tflite::micro::GetTensorShape(output),            \
-               tflite::micro::GetTensorData<dtype>(output))
-
-  if (input1->type == kTfLiteInt8 && input2->type == kTfLiteInt8 &&
-      output->type == kTfLiteInt8) {
-    SetActivationParams(data->output_activation_min,
-                        data->output_activation_max, &op_params);
-    op_params.input1_offset = -data->input1_zero_point;
-    op_params.input2_offset = -data->input2_zero_point;
-    op_params.output_offset = data->output_zero_point;
-    op_params.output_multiplier = data->output_multiplier;
-    op_params.output_shift = data->output_shift;
-
-    bool requires_broadcast = reference_ops::ProcessBroadcastShapes(
-        tflite::micro::GetTensorShape(input1),
-        tflite::micro::GetTensorShape(input2), &op_params);
-
-    if (requires_broadcast) {
-      TF_LITE_DIV(reference_ops, BroadcastDivSlow, int8_t);
-    } else {
-      TF_LITE_DIV(reference_ops, Div, int8_t);
-    }
-#undef TF_LITE_DIV
-  } else {
-    MicroPrintf("Unsupported combination of input and output types in DIV.");
-    return kTfLiteError;
-  }
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-  auto* params = static_cast<TfLiteDivParams*>(node->builtin_data);
-  TFLITE_DCHECK(node->user_data != nullptr);
-  auto* data = static_cast<OpDataDiv*>(node->user_data);
-
-  const TfLiteEvalTensor* input1 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor1);
-  const TfLiteEvalTensor* input2 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor2);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  if (output->type == kTfLiteFloat32) {
-    EvalDiv(context, node, params, data, input1, input2, output);
-  } else if (output->type == kTfLiteInt8) {
-    TF_LITE_ENSURE_OK(context, EvalQuantized(context, node, params, data,
-                                             input1, input2, output));
-  } else {
-    MicroPrintf(
-        "DIV only supports FLOAT32, quantized INT8 "
-        "now, got type %s (%d).",
-        TfLiteTypeGetName(output->type), output->type);
-    return kTfLiteError;
-  }
-
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_DIV() {
-  return tflite::micro::RegisterOp(Init, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/elementwise.cc

@@ -1,429 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include <cmath>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace elementwise {
-namespace {
-
-constexpr int kAbsNameId = 0;
-constexpr int kRsrqtNameId = 1;
-
-const int kElementwiseInputTensor = 0;
-const int kElementwiseOutputTensor = 0;
-
-struct OpDataAbsRsqrt {
-  int32_t multiplier;
-  int shift;
-  int input_offset;
-  int output_offset;
-  bool needs_rescale;
-  TfLiteQuantizationType input_quantization_type;
-  TfLiteType input_type;
-};
-
-bool IsNumericSupportedType(const TfLiteType type) {
-  return type == kTfLiteFloat32;
-}
-
-bool IsLogicalSupportedType(const TfLiteType type) {
-  return type == kTfLiteBool;
-}
-
-bool IsAbsSupportedType(const TfLiteType type) {
-  return type == kTfLiteFloat32 || type == kTfLiteInt8 || type == kTfLiteInt16;
-}
-
-bool IsRsqrtSupportedType(const TfLiteType type) {
-  return type == kTfLiteFloat32 || type == kTfLiteInt8;
-}
-
-inline void SetAbsOutputMultiplier(const float input_scale,
-                                   const float output_scale,
-                                   int32_t* multiplier, int* shift) {
-  QuantizeMultiplier(static_cast<double>(input_scale / output_scale),
-                     multiplier, shift);
-}
-
-inline void SetRsqrtOutputMultiplier(const float input_scale,
-                                     const float output_scale,
-                                     int32_t* multiplier, int* shift) {
-  const double scale =
-      1. / static_cast<double>((std::sqrt(input_scale) * output_scale));
-  QuantizeMultiplier(scale, multiplier, shift);
-}
-
-typedef bool (*IsSupportedType)(TfLiteType);
-template <IsSupportedType>
-TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kElementwiseInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kElementwiseOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-  if (!IsSupportedType(input->type)) {
-    MicroPrintf("Input data type %s (%d) is not supported.",
-                TfLiteTypeGetName(input->type), input->type);
-    return kTfLiteError;
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-typedef bool (*IsSupportedType)(TfLiteType);
-template <IsSupportedType, const int op_nameid>
-TfLiteStatus PrepareAbsRsqrt(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0);
-  TF_LITE_ENSURE(context, output != nullptr);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-  if (!IsSupportedType(input->type)) {
-    MicroPrintf("Input data type %s (%d) is not supported.",
-                TfLiteTypeGetName(input->type), input->type);
-    return kTfLiteError;
-  }
-
-  auto* op_data = static_cast<OpDataAbsRsqrt*>(node->user_data);
-  op_data->input_type = input->type;
-
-  // For int16 type input, we support both quantized and non-quantized
-  // evaluation.
-  if (op_nameid == kAbsNameId) {
-    op_data->input_quantization_type = input->quantization.type;
-  }
-
-  if (input->type == kTfLiteInt8 ||
-      (input->type == kTfLiteInt16 &&
-       input->quantization.type != kTfLiteNoQuantization)) {
-    TF_LITE_ENSURE_EQ(context, input->quantization.type,
-                      kTfLiteAffineQuantization);
-    TF_LITE_ENSURE_EQ(context, output->quantization.type,
-                      kTfLiteAffineQuantization);
-    const auto* input_params =
-        reinterpret_cast<TfLiteAffineQuantization*>(input->quantization.params);
-    const auto* output_params = reinterpret_cast<TfLiteAffineQuantization*>(
-        output->quantization.params);
-    TF_LITE_ENSURE(context, input_params != nullptr);
-    TF_LITE_ENSURE(context, input_params->scale != nullptr);
-    TF_LITE_ENSURE(context, input_params->scale->size > 0);
-    TF_LITE_ENSURE(context, input_params->zero_point->size > 0);
-    TF_LITE_ENSURE(context, output_params != nullptr);
-    TF_LITE_ENSURE(context, output_params->scale != nullptr);
-    TF_LITE_ENSURE(context, output_params->scale->size > 0);
-    TF_LITE_ENSURE(context, output_params->zero_point->size > 0);
-    op_data->input_offset = input_params->zero_point->data[0];
-    op_data->output_offset = output_params->zero_point->data[0];
-    if (input->type == kTfLiteInt16) {
-      TF_LITE_ENSURE_EQ(context, op_data->input_offset, 0);
-      TF_LITE_ENSURE_EQ(context, op_data->output_offset, 0);
-    }
-    const float input_scale = input_params->scale->data[0];
-    const float output_scale = output_params->scale->data[0];
-    op_data->needs_rescale = input_scale != output_scale;
-    if (op_nameid == kAbsNameId && op_data->needs_rescale) {
-      SetAbsOutputMultiplier(input_scale, output_scale, &op_data->multiplier,
-                             &op_data->shift);
-    } else if (op_nameid == kRsrqtNameId) {
-      SetRsqrtOutputMultiplier(input_scale, output_scale, &op_data->multiplier,
-                               &op_data->shift);
-    }
-  }
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-template <typename T>
-inline TfLiteStatus EvalImplQuantized(
-    TfLiteContext* context, TfLiteNode* node,
-    T func(TfLiteContext*, TfLiteNode*, T),
-    TfLiteStatus validate_input_func(TfLiteContext*, TfLiteNode*, T),
-    TfLiteType expected_type) {
-  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
-  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, expected_type);
-  const size_t num_elements = ElementCount(*input->dims);
-  const T* in_data = tflite::micro::GetTensorData<T>(input);
-  T* out_data = tflite::micro::GetTensorData<T>(output);
-  for (size_t i = 0; i < num_elements; ++i) {
-    if (validate_input_func) {
-      TF_LITE_ENSURE_OK(context,
-                        validate_input_func(context, node, in_data[i]));
-    }
-    out_data[i] = func(context, node, in_data[i]);
-  }
-  return kTfLiteOk;
-}
-
-template <typename T>
-inline T AbsHelper(T i) {
-  return std::abs(i);
-}
-
-template <typename T>
-inline TfLiteStatus EvalImpl(TfLiteContext* context, TfLiteNode* node,
-                             T func(T), TfLiteStatus validate_input_func(T),
-                             TfLiteType expected_type) {
-  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
-  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, expected_type);
-  const size_t num_elements = ElementCount(*input->dims);
-  const T* in_data = tflite::micro::GetTensorData<T>(input);
-  T* out_data = tflite::micro::GetTensorData<T>(output);
-  for (size_t i = 0; i < num_elements; ++i) {
-    if (validate_input_func) {
-      TF_LITE_ENSURE_OK(context, validate_input_func(in_data[i]));
-    }
-    out_data[i] = func(in_data[i]);
-  }
-  return kTfLiteOk;
-}
-
-inline TfLiteStatus EvalNumeric(TfLiteContext* context, TfLiteNode* node,
-                                float float_func(float)) {
-  return EvalImpl<float>(context, node, float_func,
-                         /*validate_input_func=*/nullptr, kTfLiteFloat32);
-}
-
-inline TfLiteStatus EvalLogical(TfLiteContext* context, TfLiteNode* node,
-
-                                bool bool_func(bool)) {
-  return EvalImpl<bool>(context, node, bool_func,
-                        /*validate_input_func=*/nullptr, kTfLiteBool);
-}
-
-void* ElementWiseAbsRsqrtInit(TfLiteContext* context, const char* buffer,
-                              size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(OpDataAbsRsqrt));
-}
-
-template <typename T>
-inline T AbsEvalQuantized(TfLiteContext* context, TfLiteNode* node, T i) {
-  const auto* op_data = static_cast<const OpDataAbsRsqrt*>(node->user_data);
-  const int kMin = std::numeric_limits<T>::min();
-  const int kMax = std::numeric_limits<T>::max();
-
-  const int32_t value = std::abs(i - op_data->input_offset);
-  if (!op_data->needs_rescale) {
-    return static_cast<T>(
-        std::min(std::max(static_cast<long int>(value + op_data->output_offset),
-                          static_cast<long int>(kMin)),
-                 static_cast<long int>(kMax)));
-  }
-
-  const int32_t output = tflite::MultiplyByQuantizedMultiplier(
-                             value, op_data->multiplier, op_data->shift) +
-                         op_data->output_offset;
-  return static_cast<T>(std::min(
-      std::max(static_cast<long int>(output), static_cast<long int>(kMin)),
-      static_cast<long int>(kMax)));
-}
-
-template <typename T>
-inline T RsqrtEvalQuantized(TfLiteContext* context, TfLiteNode* node, T i) {
-  const auto* op_data = static_cast<const OpDataAbsRsqrt*>(node->user_data);
-  const int kMin = std::numeric_limits<T>::min();
-  const int kMax = std::numeric_limits<T>::max();
-
-  const int32_t value = (i - op_data->input_offset);
-  const int32_t kShift = 20;  // Shift to keep value integer.
-  if (value == 0) {
-    // Assume that any value close to 0 represents the max output value.
-    return static_cast<T>(kMax);
-  }
-  int32_t inv_sqrt_multiplier;
-  int inv_sqrt_shift;
-  GetInvSqrtQuantizedMultiplierExp(value, kReverseShift, &inv_sqrt_multiplier,
-                                   &inv_sqrt_shift);
-  const int32_t data = tflite::MultiplyByQuantizedMultiplier(
-      static_cast<int32_t>(1), inv_sqrt_multiplier, inv_sqrt_shift + kShift);
-  const int32_t output =
-      tflite::MultiplyByQuantizedMultiplier(data, op_data->multiplier,
-                                            op_data->shift - kShift) +
-      op_data->output_offset;
-  return static_cast<T>(std::min(
-      std::max(static_cast<long int>(output), static_cast<long int>(kMin)),
-      static_cast<long int>(kMax)));
-}
-
-template <typename T>
-TfLiteStatus validate_input_func(TfLiteContext* context, TfLiteNode* node,
-                                 T i) {
-  const auto* op_data = static_cast<const OpDataAbsRsqrt*>(node->user_data);
-
-  TF_LITE_ENSURE_MSG(context, i >= op_data->input_offset,
-                     "Rsqrt is only defined for positive values");
-  return static_cast<TfLiteStatus>(kTfLiteOk);
-}
-
-TfLiteStatus AbsEval(TfLiteContext* context, TfLiteNode* node) {
-  OpDataAbsRsqrt* op_data = reinterpret_cast<OpDataAbsRsqrt*>(node->user_data);
-  TfLiteType type = op_data->input_type;
-  TfLiteQuantizationType input_quantization_type =
-      op_data->input_quantization_type;
-  TfLiteStatus eval_result;
-
-  switch (type) {
-    case kTfLiteFloat32:
-      eval_result = EvalNumeric(context, node, std::abs);
-      break;
-    case kTfLiteInt8:
-      eval_result =
-          EvalImplQuantized<int8_t>(context, node, AbsEvalQuantized,
-                                    /*validate_input_func=*/nullptr, type);
-      break;
-    case kTfLiteInt16:
-      eval_result =
-          input_quantization_type == kTfLiteNoQuantization
-              ? EvalImpl<int16_t>(context, node, AbsHelper,
-                                  /*validate_input_func=*/nullptr, type)
-              : EvalImplQuantized<int16_t>(context, node, AbsEvalQuantized,
-                                           /*validate_input_func=*/nullptr,
-                                           type);
-      break;
-    default:
-      MicroPrintf("Current data type %s is not supported.",
-                  TfLiteTypeGetName(type));
-      return kTfLiteError;
-      break;
-  }
-  return eval_result;
-}
-
-TfLiteStatus SinEval(TfLiteContext* context, TfLiteNode* node) {
-  return EvalNumeric(context, node, std::sin);
-}
-
-TfLiteStatus CosEval(TfLiteContext* context, TfLiteNode* node) {
-  return EvalNumeric(context, node, std::cos);
-}
-
-TfLiteStatus LogEval(TfLiteContext* context, TfLiteNode* node) {
-  return EvalNumeric(context, node, std::log);
-}
-
-TfLiteStatus SqrtEval(TfLiteContext* context, TfLiteNode* node) {
-  return EvalNumeric(context, node, std::sqrt);
-}
-
-TfLiteStatus RsqrtEval(TfLiteContext* context, TfLiteNode* node) {
-  const auto* op_data = static_cast<const OpDataAbsRsqrt*>(node->user_data);
-  TfLiteType type = op_data->input_type;
-  switch (type) {
-    case kTfLiteFloat32:
-      return EvalImpl<float>(
-          context, node, [](float f) { return 1.f / std::sqrt(f); },
-          /*validate_input_func=*/nullptr, type);
-    case kTfLiteInt8:
-      return EvalImplQuantized<int8_t>(context, node,
-                                       elementwise::RsqrtEvalQuantized,
-                                       elementwise::validate_input_func, type);
-
-    default:
-      MicroPrintf("Current data type %s is not supported.",
-                  TfLiteTypeGetName(type));
-      return kTfLiteError;
-  }
-}
-
-TfLiteStatus SquareEval(TfLiteContext* context, TfLiteNode* node) {
-  return EvalNumeric(context, node, [](float f) { return f * f; });
-}
-
-TfLiteStatus LogicalNotEval(TfLiteContext* context, TfLiteNode* node) {
-  return EvalLogical(context, node, [](bool v) { return !v; });
-}
-
-}  // namespace
-}  // namespace elementwise
-
-TfLiteRegistration Register_ABS() {
-  return tflite::micro::RegisterOp(
-      elementwise::ElementWiseAbsRsqrtInit,
-      elementwise::PrepareAbsRsqrt<elementwise::IsAbsSupportedType,
-                                   elementwise::kAbsNameId>,
-      elementwise::AbsEval);
-}
-
-TfLiteRegistration Register_SIN() {
-  return tflite::micro::RegisterOp(
-      nullptr, elementwise::GenericPrepare<elementwise::IsNumericSupportedType>,
-      elementwise::SinEval);
-}
-
-TfLiteRegistration Register_COS() {
-  return tflite::micro::RegisterOp(
-      nullptr, elementwise::GenericPrepare<elementwise::IsNumericSupportedType>,
-      elementwise::CosEval);
-}
-
-TfLiteRegistration Register_LOG() {
-  return tflite::micro::RegisterOp(
-      nullptr, elementwise::GenericPrepare<elementwise::IsNumericSupportedType>,
-      elementwise::LogEval);
-}
-
-TfLiteRegistration Register_SQRT() {
-  return tflite::micro::RegisterOp(
-      nullptr, elementwise::GenericPrepare<elementwise::IsNumericSupportedType>,
-      elementwise::SqrtEval);
-}
-
-TfLiteRegistration Register_RSQRT() {
-  return tflite::micro::RegisterOp(
-      elementwise::ElementWiseAbsRsqrtInit,
-      elementwise::PrepareAbsRsqrt<elementwise::IsRsqrtSupportedType,
-                                   elementwise::kRsrqtNameId>,
-      elementwise::RsqrtEval);
-}
-
-TfLiteRegistration Register_SQUARE() {
-  return tflite::micro::RegisterOp(
-      nullptr, elementwise::GenericPrepare<elementwise::IsNumericSupportedType>,
-      elementwise::SquareEval);
-}
-
-TfLiteRegistration Register_LOGICAL_NOT() {
-  return tflite::micro::RegisterOp(
-      nullptr, elementwise::GenericPrepare<elementwise::IsLogicalSupportedType>,
-      elementwise::LogicalNotEval);
-}
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/elu.cc

@@ -1,150 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/elu.h"
-
-#include <algorithm>
-#include <limits>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/cppmath.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-// Input/output tensor index.
-constexpr int kInputTensor = 0;
-constexpr int kOutputTensor = 0;
-
-// OLD-TODO(b/142762739): We should figure out a multi-threading plan for most
-// of the activation ops below.
-
-struct OpData {
-  int8_t table[256];
-};
-
-using TransformFunc = float (*)(float);
-
-template <typename T>
-void PopulateLookupTable(const TfLiteTensor* input, const TfLiteTensor* output,
-                         const TransformFunc transform, OpData* data) {
-  if (sizeof(T) != 1) {
-    MicroPrintf("Lookup table valid only for 8bit");
-    TFLITE_ABORT;
-  }
-
-  const float inverse_scale = 1 / output->params.scale;
-  int32_t maxval = std::numeric_limits<T>::max();
-  int32_t minval = std::numeric_limits<T>::min();
-  for (int32_t val = minval; val <= maxval; ++val) {
-    const float dequantized =
-        input->params.scale * (val - input->params.zero_point);
-    const float transformed = transform(dequantized);
-    const float rescaled = TfLiteRound(transformed * inverse_scale);
-    const int32_t quantized =
-        static_cast<int32_t>(rescaled + output->params.zero_point);
-    data->table[static_cast<uint8_t>(static_cast<T>(val))] =
-        static_cast<T>(std::max(std::min(maxval, quantized), minval));
-  }
-}
-
-// OLD-TODO(b/143696793): move this to optimized_ops.
-void EvalUsingLookupTable(const OpData* data, const TfLiteEvalTensor* input,
-                          TfLiteEvalTensor* output) {
-  const int size = MatchingFlatSize(tflite::micro::GetTensorShape(input),
-                                    tflite::micro::GetTensorShape(output));
-  int8_t* output_data = tflite::micro::GetTensorData<int8_t>(output);
-  const int8_t* input_data = tflite::micro::GetTensorData<int8_t>(input);
-
-  for (int i = 0; i < size; ++i) {
-    output_data[i] = data->table[static_cast<uint8_t>(input_data[i])];
-  }
-}
-
-TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-
-  // Use LUT to handle quantized elu path.
-  if (input->type == kTfLiteInt8) {
-    OpData* data = static_cast<OpData*>(node->user_data);
-    TransformFunc transform = [](float value) {
-      return value < 0.0f ? std::exp(value) - 1.0f : value;
-    };
-    PopulateLookupTable<int8_t>(input, output, transform, data);
-  }
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-void* EluInit(TfLiteContext* context, const char* buffer, size_t length) {
-  // This is a builtin op, so we don't use the contents in 'buffer', if any.
-  // Instead, we allocate a new object to carry information from Prepare() to
-  // Eval().
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(OpData));
-}
-
-TfLiteStatus EluPrepare(TfLiteContext* context, TfLiteNode* node) {
-  return CalculateOpData(context, node);
-}
-
-TfLiteStatus EluEval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      reference_ops::Elu(tflite::micro::GetTensorShape(input),
-                         tflite::micro::GetTensorData<float>(input),
-                         tflite::micro::GetTensorShape(output),
-                         tflite::micro::GetTensorData<float>(output));
-      return kTfLiteOk;
-    }
-    case kTfLiteInt8: {
-      const OpData* data = static_cast<OpData*>(node->user_data);
-      EvalUsingLookupTable(data, input, output);
-      return kTfLiteOk;
-    }
-    default:
-      MicroPrintf("ELU only supports float32 and int8 currently, got %s.",
-                  TfLiteTypeGetName(input->type));
-      return kTfLiteError;
-  }
-}
-
-}  // namespace
-
-TfLiteRegistration Register_ELU() {
-  return tflite::micro::RegisterOp(EluInit, EluPrepare, EluEval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/conv.cc

@@ -1,343 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/kernels/conv.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/reference/conv.h"
-#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/padding.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-#include <esp_timer.h>
-
-#if ESP_NN
-#include <esp_nn.h>
-#endif
-
-
-long long conv_total_time = 0;
-
-namespace tflite {
-namespace {
-
-struct NodeData {
-  OpDataConv op_data;
-#if ESP_NN
-  int buffer_idx;
-#endif
-};
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(NodeData));
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-
-  NodeData* data = static_cast<NodeData*>(node->user_data);
-  const auto& params =
-      *(static_cast<const TfLiteConvParams*>(node->builtin_data));
-
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kConvInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* filter =
-      micro_context->AllocateTempInputTensor(node, kConvWeightsTensor);
-  TF_LITE_ENSURE(context, filter != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kConvOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  const int input_width = input->dims->data[2];
-  const int input_height = input->dims->data[1];
-  const int filter_width = filter->dims->data[2];
-  const int filter_height = filter->dims->data[1];
-  const int output_width = output->dims->data[2];
-  const int output_height = output->dims->data[1];
-
-  // Dynamically allocate per-channel quantization parameters.
-  const int num_channels = filter->dims->data[kConvQuantizedDimension];
-  data->op_data.per_channel_output_multiplier =
-      static_cast<int32_t*>(context->AllocatePersistentBuffer(
-          context, num_channels * sizeof(int32_t)));
-  data->op_data.per_channel_output_shift =
-      static_cast<int32_t*>(context->AllocatePersistentBuffer(
-          context, num_channels * sizeof(int32_t)));
-
-  // All per-channel quantized tensors need valid zero point and scale arrays.
-  if (input->type == kTfLiteInt8) {
-    TF_LITE_ENSURE_EQ(context, filter->quantization.type,
-                      kTfLiteAffineQuantization);
-
-    const auto* affine_quantization =
-        static_cast<TfLiteAffineQuantization*>(filter->quantization.params);
-    TFLITE_DCHECK(affine_quantization != nullptr);
-    TFLITE_DCHECK(affine_quantization->scale != nullptr);
-    TFLITE_DCHECK(affine_quantization->zero_point != nullptr);
-
-    TF_LITE_ENSURE(context,
-                   affine_quantization->scale->size == 1 ||
-                       affine_quantization->scale->size ==
-                           filter->dims->data[kConvQuantizedDimension]);
-    TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size,
-                      affine_quantization->zero_point->size);
-  }
-
-  TF_LITE_ENSURE_STATUS(CalculateOpDataConv(
-      context, node, params, input_width, input_height, filter_width,
-      filter_height, output_width, output_height, input->type, &data->op_data));
-
-#if ESP_NN
-  if (input->type == kTfLiteInt8) {
-    data_dims_t input_dims =  {
-                                .width = input_width, .height = input_height,
-                                .channels = input->dims->data[3], 1
-                              };
-    data_dims_t output_dims = {
-                                .width = output_width, .height = output_height,
-                                .channels = output->dims->data[3], 1
-                              };
-    data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0};
-    conv_params_t conv_params = {
-                                  .in_offset = 0, .out_offset = 0,
-                                  .stride = {params.stride_width, params.stride_height},
-                                  .padding = {data->op_data.padding.width, data->op_data.padding.height},
-                                  .dilation = {0, 0}, .activation = {-128, 127}
-                                };
-
-    int scratch_buf_size = esp_nn_get_conv_scratch_size(
-        &input_dims, &filter_dims, &output_dims, &conv_params);
-    if (scratch_buf_size > 0) {
-      TF_LITE_ENSURE_STATUS(context->RequestScratchBufferInArena(
-        context, scratch_buf_size, &data->buffer_idx));
-    } else {
-      data->buffer_idx = -1;
-    }
-  }
-#endif
-
-  micro_context->DeallocateTempTfLiteTensor(output);
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(filter);
-
-  return kTfLiteOk;
-}
-
-#if ESP_NN
-// Fixed-point per-channel-quantization convolution Int8 function wrapper.
-inline void EvalQuantizedPerChannel(
-    TfLiteContext* context, TfLiteNode* node, const TfLiteConvParams& params,
-    const NodeData& data, const TfLiteEvalTensor* input,
-    const TfLiteEvalTensor* filter, const TfLiteEvalTensor* bias,
-    TfLiteEvalTensor* output) {
-  const int dilation_width_factor = params.dilation_width_factor;
-  const int dilation_height_factor = params.dilation_height_factor;
-
-  if (dilation_width_factor == 1 && dilation_height_factor == 1) {
-    // Get parameters.
-    RuntimeShape filter_shape = tflite::micro::GetTensorShape(filter);
-    RuntimeShape input_shape = tflite::micro::GetTensorShape(input);
-    RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
-    RuntimeShape bias_shape = tflite::micro::GetTensorShape(bias);
-
-    const int8_t *input_data = tflite::micro::GetTensorData<int8_t>(input);
-    int8_t *output_data = tflite::micro::GetTensorData<int8_t>(output);
-
-    const int32_t input_offset = -data.op_data.input_zero_point;
-    const int32_t output_offset = data.op_data.output_zero_point;
-    const int stride_width = params.stride_width;
-    const int stride_height = params.stride_height;
-    const int pad_width = data.op_data.padding.width;
-    const int pad_height = data.op_data.padding.height;
-
-    const int input_height = input_shape.Dims(1);
-    const int input_width = input_shape.Dims(2);
-    const int filter_height = filter_shape.Dims(1);
-    const int filter_width = filter_shape.Dims(2);
-    const int output_height = output_shape.Dims(1);
-    const int output_width = output_shape.Dims(2);
-
-    // Set min and max value of the output.
-    const int32_t activation_min = data.op_data.output_activation_min;
-    const int32_t activation_max = data.op_data.output_activation_max;
-
-    // Consistency check.
-    TFLITE_DCHECK_LE(activation_min, activation_max);
-    TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
-    TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
-    TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
-    const int batch_size = MatchingDim(input_shape, 0, output_shape, 0);
-    const int input_depth = MatchingDim(input_shape, 3, filter_shape, 3);
-    const int output_depth = MatchingDim(filter_shape, 0, output_shape, 3);
-
-    if (tflite::micro::GetTensorData<int8_t>(bias)) {
-      TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
-    }
-
-    void *scratch_buf = NULL;
-    if (data.buffer_idx > -1) {
-      scratch_buf = context->GetScratchBuffer(context, data.buffer_idx);
-    }
-    esp_nn_set_conv_scratch_buf(scratch_buf);
-
-    const int input_size = input_width * input_height * input_depth;
-    const int output_size = output_width * output_height * output_depth;
-
-    data_dims_t input_dims =  {
-                                .width = input_width, .height = input_height,
-                                .channels = input_depth, 1
-                              };
-    data_dims_t output_dims = {
-                                .width = output_width, .height = output_height,
-                                .channels = output_depth, 1
-                              };
-    data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0};
-    conv_params_t conv_params = {
-                                  .in_offset = input_offset, .out_offset = output_offset,
-                                  .stride = {stride_width, stride_height},
-                                  .padding = {pad_width, pad_height},
-                                  .dilation = {0, 0},
-                                  .activation = {activation_min, activation_max}
-                                };
-    quant_data_t quant_data = {
-                                .shift = data.op_data.per_channel_output_shift,
-                                .mult = data.op_data.per_channel_output_multiplier
-                              };
-
-    for (int i_batch = 0; i_batch < batch_size; i_batch++) {
-      esp_nn_conv_s8(&input_dims, input_data + i_batch * input_size,
-                     &filter_dims, tflite::micro::GetTensorData<int8_t>(filter),
-                     tflite::micro::GetTensorData<int32_t>(bias),
-                     &output_dims, output_data + i_batch * output_size,
-                     &conv_params, &quant_data);
-    }
-  } else {
-    reference_integer_ops::ConvPerChannel(
-        ConvParamsQuantized(params, data.op_data),
-        data.op_data.per_channel_output_multiplier,
-        data.op_data.per_channel_output_shift,
-        tflite::micro::GetTensorShape(input),
-        tflite::micro::GetTensorData<int8_t>(input),
-        tflite::micro::GetTensorShape(filter),
-        tflite::micro::GetTensorData<int8_t>(filter),
-        tflite::micro::GetTensorShape(bias),
-        tflite::micro::GetTensorData<int32_t>(bias),
-        tflite::micro::GetTensorShape(output),
-        tflite::micro::GetTensorData<int8_t>(output));
-  }
-}
-#endif
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kConvInputTensor);
-  const TfLiteEvalTensor* filter =
-      tflite::micro::GetEvalInput(context, node, kConvWeightsTensor);
-  const TfLiteEvalTensor* bias =
-      (NumInputs(node) == 3)
-          ? tflite::micro::GetEvalInput(context, node, kConvBiasTensor)
-          : nullptr;
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kConvOutputTensor);
-
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-  const auto& params =
-      *(reinterpret_cast<TfLiteConvParams*>(node->builtin_data));
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const auto& data = *(static_cast<const NodeData*>(node->user_data));
-
-  TF_LITE_ENSURE_EQ(context, input->type, output->type);
-  TF_LITE_ENSURE_MSG(context, input->type == filter->type,
-                     "Hybrid models are not supported on TFLite Micro.");
-
-  long long start_time = esp_timer_get_time();
-  switch (input->type) {  // Already know in/out types are same.
-    case kTfLiteFloat32: {
-      tflite::reference_ops::Conv(
-          ConvParamsFloat(params, data.op_data),
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<float>(input),
-          tflite::micro::GetTensorShape(filter),
-          tflite::micro::GetTensorData<float>(filter),
-          tflite::micro::GetTensorShape(bias),
-          tflite::micro::GetTensorData<float>(bias),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<float>(output),
-          tflite::micro::GetTensorShape(nullptr), nullptr);
-      break;
-    }
-    case kTfLiteInt8: {
-#if ESP_NN
-      EvalQuantizedPerChannel(context, node, params, data, input, filter,
-                              bias, output);
-#else
-      reference_integer_ops::ConvPerChannel(
-          ConvParamsQuantized(params, data.op_data),
-          data.op_data.per_channel_output_multiplier,
-          data.op_data.per_channel_output_shift,
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(filter),
-          tflite::micro::GetTensorData<int8_t>(filter),
-          tflite::micro::GetTensorShape(bias),
-          tflite::micro::GetTensorData<int32_t>(bias),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int8_t>(output));
-#endif
-      break;
-    }
-    case kTfLiteUInt8: {
-      //EvalQuantized
-      reference_ops::Conv(ConvParamsQuantized(params, data.op_data),
-                          tflite::micro::GetTensorShape(input),
-                          tflite::micro::GetTensorData<uint8_t>(input),
-                          tflite::micro::GetTensorShape(filter),
-                          tflite::micro::GetTensorData<uint8_t>(filter),
-                          tflite::micro::GetTensorShape(bias),
-                          tflite::micro::GetTensorData<int32_t>(bias),
-                          tflite::micro::GetTensorShape(output),
-                          tflite::micro::GetTensorData<uint8_t>(output),
-                          tflite::micro::GetTensorShape(nullptr), nullptr,
-                          nullptr);
-      break;
-    }
-    default:
-      TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
-                         TfLiteTypeGetName(input->type), input->type);
-      return kTfLiteError;
-  }
-  long long time_this_instance = esp_timer_get_time() - start_time;
-  conv_total_time += time_this_instance;
-  //printf("time this instance: %llu\n", time_this_instance / 1000);
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_CONV_2D() {
-  return tflite::micro::RegisterOp(Init, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/depthwise_conv.cc

@@ -1,345 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/kernels/depthwise_conv.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/reference/depthwiseconv_float.h"
-#include "tensorflow/lite/kernels/internal/reference/depthwiseconv_uint8.h"
-#include "tensorflow/lite/kernels/internal/reference/integer_ops/depthwise_conv.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/padding.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-#include <esp_timer.h>
-
-#if ESP_NN
-#include <esp_nn.h>
-#endif
-
-long long dc_total_time = 0;
-
-namespace tflite {
-namespace {
-
-struct NodeData {
-  OpDataConv op_data;
-#if ESP_NN
-  int buffer_idx;
-#endif
-};
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(NodeData));
-}
-
-#if ESP_NN
-inline void EvalQuantizedPerChannel(TfLiteContext* context, TfLiteNode* node,
-                                    const TfLiteDepthwiseConvParams& params,
-                                    const NodeData& data,
-                                    const TfLiteEvalTensor* input,
-                                    const TfLiteEvalTensor* filter,
-                                    const TfLiteEvalTensor* bias,
-                                    TfLiteEvalTensor* output) {
-  const int dilation_width_factor = params.dilation_width_factor;
-  const int dilation_height_factor = params.dilation_height_factor;
-
-  if (dilation_width_factor == 1 && dilation_height_factor == 1) {
-    // Get parameters.
-    RuntimeShape input_shape = tflite::micro::GetTensorShape(input);
-    RuntimeShape filter_shape = tflite::micro::GetTensorShape(filter);
-    RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
-    RuntimeShape bias_shape = tflite::micro::GetTensorShape(bias);
-
-    TFLITE_DCHECK_EQ(input_shape.DimensionsCount(), 4);
-    TFLITE_DCHECK_EQ(filter_shape.DimensionsCount(), 4);
-    TFLITE_DCHECK_EQ(output_shape.DimensionsCount(), 4);
-
-    const int8_t *input_data = tflite::micro::GetTensorData<int8_t>(input);
-    int8_t *output_data = tflite::micro::GetTensorData<int8_t>(output);
-
-    const int depth_multiplier = params.depth_multiplier;
-    const int32_t input_offset = -data.op_data.input_zero_point;
-    const int32_t output_offset = data.op_data.output_zero_point;
-    const int stride_width = params.stride_width;
-    const int stride_height = params.stride_height;
-    const int pad_width = data.op_data.padding.width;
-    const int pad_height = data.op_data.padding.height;
-
-    const int input_height = input_shape.Dims(1);
-    const int input_width = input_shape.Dims(2);
-    const int input_depth = input_shape.Dims(3);
-    const int filter_height = filter_shape.Dims(1);
-    const int filter_width = filter_shape.Dims(2);
-    const int output_height = output_shape.Dims(1);
-    const int output_width = output_shape.Dims(2);
-
-    // Set min and max value of the output.
-    const int32_t activation_min = data.op_data.output_activation_min;
-    const int32_t activation_max = data.op_data.output_activation_max;
-
-    // Consistency check.
-    TFLITE_DCHECK_LE(activation_min, activation_max);
-    const int batch_size = MatchingDim(input_shape, 0, output_shape, 0);
-    const int output_depth = MatchingDim(filter_shape, 3, output_shape, 3);
-
-    TFLITE_DCHECK_EQ(output_depth, input_depth * depth_multiplier);
-    if (tflite::micro::GetTensorData<int8_t>(bias)) {
-      TFLITE_DCHECK_EQ(bias_shape.FlatSize(), output_depth);
-    }
-
-    const int input_size = input_width * input_height * input_depth;
-    const int output_size = output_width * output_height * output_depth;
-    void *scratch_buf = NULL;
-    if (data.buffer_idx > -1) {
-      scratch_buf = context->GetScratchBuffer(context, data.buffer_idx);
-    }
-
-    esp_nn_set_depthwise_conv_scratch_buf(scratch_buf);
-
-    data_dims_t input_dims =  {
-                                .width = input_width, .height = input_height,
-                                .channels = input_depth, 1
-                              };
-    data_dims_t output_dims = {
-                                .width = output_width, .height = output_height,
-                                .channels = output_depth, 1
-                              };
-    data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0};
-    dw_conv_params_t conv_params =  {
-                                      .in_offset = input_offset, .out_offset = output_offset,
-                                      .ch_mult = depth_multiplier,
-                                      .stride = {stride_width, stride_height},
-                                      .padding = {pad_width, pad_height}, .dilation = {0, 0},
-                                      .activation = {activation_min, activation_max}
-                                    };
-    quant_data_t quant_data = {
-                                .shift = data.op_data.per_channel_output_shift,
-                                .mult = data.op_data.per_channel_output_multiplier
-                              };
-
-    for (int i_batch = 0; i_batch < batch_size; i_batch++) {
-      esp_nn_depthwise_conv_s8(&input_dims, input_data + i_batch * input_size,
-                               &filter_dims, tflite::micro::GetTensorData<int8_t>(filter),
-                               tflite::micro::GetTensorData<int32_t>(bias),
-                               &output_dims, output_data + i_batch * output_size,
-                               &conv_params, &quant_data);
-    }
-  } else {
-    reference_integer_ops::DepthwiseConvPerChannel(
-        DepthwiseConvParamsQuantized(params, data.op_data),
-        data.op_data.per_channel_output_multiplier,
-        data.op_data.per_channel_output_shift,
-        tflite::micro::GetTensorShape(input),
-        tflite::micro::GetTensorData<int8_t>(input),
-        tflite::micro::GetTensorShape(filter),
-        tflite::micro::GetTensorData<int8_t>(filter),
-        tflite::micro::GetTensorShape(bias),
-        tflite::micro::GetTensorData<int32_t>(bias),
-        tflite::micro::GetTensorShape(output),
-        tflite::micro::GetTensorData<int8_t>(output));
-  }
-}
-#endif
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-
-  NodeData* data = static_cast<NodeData*>(node->user_data);
-  const TfLiteDepthwiseConvParams& params =
-      *(static_cast<const TfLiteDepthwiseConvParams*>(node->builtin_data));
-
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kConvInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* filter =
-      micro_context->AllocateTempInputTensor(node, kConvWeightsTensor);
-  TF_LITE_ENSURE(context, filter != nullptr);
-  TfLiteTensor* bias =
-      micro_context->AllocateTempInputTensor(node, kConvBiasTensor);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kConvOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  const int input_width = input->dims->data[2];
-  const int input_height = input->dims->data[1];
-  const int filter_width = filter->dims->data[2];
-  const int filter_height = filter->dims->data[1];
-  const int output_width = output->dims->data[2];
-  const int output_height = output->dims->data[1];
-
-  // Dynamically allocate per-channel quantization parameters.
-  const int num_channels = filter->dims->data[kDepthwiseConvQuantizedDimension];
-  data->op_data.per_channel_output_multiplier =
-      static_cast<int32_t*>(context->AllocatePersistentBuffer(
-          context, num_channels * sizeof(int32_t)));
-  data->op_data.per_channel_output_shift =
-      static_cast<int32_t*>(context->AllocatePersistentBuffer(
-          context, num_channels * sizeof(int32_t)));
-
-  // All per-channel quantized tensors need valid zero point and scale arrays.
-  if (input->type == kTfLiteInt8) {
-    TF_LITE_ENSURE_EQ(context, filter->quantization.type,
-                      kTfLiteAffineQuantization);
-
-    const auto* affine_quantization =
-        static_cast<TfLiteAffineQuantization*>(filter->quantization.params);
-    TFLITE_DCHECK(affine_quantization != nullptr);
-    TFLITE_DCHECK(affine_quantization->scale != nullptr);
-    TFLITE_DCHECK(affine_quantization->zero_point != nullptr);
-
-    TF_LITE_ENSURE(
-        context, affine_quantization->scale->size == 1 ||
-                     affine_quantization->scale->size ==
-                         filter->dims->data[kDepthwiseConvQuantizedDimension]);
-
-    TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size,
-                      affine_quantization->zero_point->size);
-  }
-
-  TF_LITE_ENSURE_STATUS(CalculateOpDataDepthwiseConv(
-      context, node, params, input_width, input_height, filter_width,
-      filter_height, output_width, output_height, input->type, &data->op_data));
-
-#if ESP_NN
-  if (input->type == kTfLiteInt8) {
-    data_dims_t input_dims =  {
-                                .width = input_width, .height = input_height,
-                                .channels = input->dims->data[3], 1
-                              };
-    data_dims_t output_dims = {
-                                .width = output_width, .height = output_height,
-                                .channels = output->dims->data[3], 1
-                              };
-    data_dims_t filter_dims = {.width = filter_width, .height = filter_height, 0, 0};
-    dw_conv_params_t conv_params =  {
-                                      .in_offset = 0, .out_offset = 0,
-                                      .ch_mult = params.depth_multiplier,
-                                      .stride = {params.stride_width, params.stride_height},
-                                      .padding = {data->op_data.padding.width, data->op_data.padding.height},
-                                      .dilation = {0, 0}, .activation = {-128, 127}
-                                    };
-
-    int scratch_buf_size = esp_nn_get_depthwise_conv_scratch_size(
-        &input_dims, &filter_dims, &output_dims, &conv_params);
-    if (scratch_buf_size > 0) {
-      TF_LITE_ENSURE_STATUS(context->RequestScratchBufferInArena(
-        context, scratch_buf_size, &data->buffer_idx));
-    } else {
-      data->buffer_idx = -1;
-    }
-  }
-#endif
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(filter);
-  micro_context->DeallocateTempTfLiteTensor(bias);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-
-  auto& params =
-      *(reinterpret_cast<TfLiteDepthwiseConvParams*>(node->builtin_data));
-  const NodeData& data = *(static_cast<const NodeData*>(node->user_data));
-
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kDepthwiseConvOutputTensor);
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kDepthwiseConvInputTensor);
-  const TfLiteEvalTensor* filter =
-      tflite::micro::GetEvalInput(context, node, kDepthwiseConvWeightsTensor);
-  const TfLiteEvalTensor* bias =
-      (NumInputs(node) == 3)
-          ? tflite::micro::GetEvalInput(context, node, kDepthwiseConvBiasTensor)
-          : nullptr;
-
-  long long start_time = esp_timer_get_time();
-  switch (input->type) {  // Already know in/out types are same.
-    case kTfLiteFloat32:
-      tflite::reference_ops::DepthwiseConv(
-          DepthwiseConvParamsFloat(params, data.op_data),
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<float>(input),
-          tflite::micro::GetTensorShape(filter),
-          tflite::micro::GetTensorData<float>(filter),
-          tflite::micro::GetTensorShape(bias),
-          tflite::micro::GetTensorData<float>(bias),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<float>(output));
-      break;
-    case kTfLiteInt8:
-#if ESP_NN
-      EvalQuantizedPerChannel(context, node, params, data, input, filter, bias,
-                              output);
-#else
-      reference_integer_ops::DepthwiseConvPerChannel(
-          DepthwiseConvParamsQuantized(params, data.op_data),
-          data.op_data.per_channel_output_multiplier,
-          data.op_data.per_channel_output_shift,
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(filter),
-          tflite::micro::GetTensorData<int8_t>(filter),
-          tflite::micro::GetTensorShape(bias),
-          tflite::micro::GetTensorData<int32_t>(bias),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int8_t>(output));
-#endif
-      break;
-    case kTfLiteUInt8:
-      //EvalQuantized(context, node, params, &data, input, filter, bias, output);
-      reference_ops::DepthwiseConv(
-          DepthwiseConvParamsQuantized(params, data.op_data),
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<uint8_t>(input),
-          tflite::micro::GetTensorShape(filter),
-          tflite::micro::GetTensorData<uint8_t>(filter),
-          tflite::micro::GetTensorShape(bias),
-          tflite::micro::GetTensorData<int32_t>(bias),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<uint8_t>(output));
-      break;
-    default:
-      TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
-                         TfLiteTypeGetName(input->type), input->type);
-      return kTfLiteError;
-  }
-  long long time_this_instance = esp_timer_get_time() - start_time;
-  dc_total_time += time_this_instance;
-  // printf("time this instance: %llu\n", time_this_instance / 1000);
-
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_DEPTHWISE_CONV_2D() {
-  return tflite::micro::RegisterOp(Init, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/esp_nn/softmax.cc

@@ -1,207 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/kernels/softmax.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/reference/softmax.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-#include <esp_timer.h>
-
-#if ESP_NN
-#include <esp_nn.h>
-#endif
-
-long long softmax_total_time = 0;
-
-namespace tflite {
-namespace {
-// Softmax parameter data that persists in user_data
-const int kInt16LUTArraySize = 513;
-
-struct NodeData {
-  SoftmaxParams op_data;
-#if ESP_NN
-  int buffer_idx;
-#endif
-};
-
-static void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(NodeData));
-}
-
-void SoftmaxQuantized(TfLiteContext* context, const TfLiteEvalTensor* input,
-                      TfLiteEvalTensor* output, const NodeData* data) {
-  if (input->type == kTfLiteInt8) {
-    if (output->type == kTfLiteInt16) {
-      tflite::reference_ops::Softmax(
-          data->op_data, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int16_t>(output));
-    } else {
-#if ESP_NN
-      const int32_t input_beta_multiplier = data->op_data.input_multiplier;
-      const int32_t input_beta_left_shift = data->op_data.input_left_shift;
-      const int diff_min = data->op_data.diff_min;
-      const RuntimeShape input_shape = tflite::micro::GetTensorShape(input);
-      const RuntimeShape output_shape = tflite::micro::GetTensorShape(output);
-      const int trailing_dim = input_shape.DimensionsCount() - 1;
-      const int outer_size =
-          MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape);
-      const int depth =
-          MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);
-      const int8_t *in_ptr = tflite::micro::GetTensorData<int8_t>(input);
-      int8_t *out_ptr = tflite::micro::GetTensorData<int8_t>(output);
-      void *scratch_buf = NULL;
-      if (data->buffer_idx > -1) {
-        scratch_buf = context->GetScratchBuffer(context, data->buffer_idx);
-      }
-      esp_nn_set_softmax_scratch_buf(scratch_buf);
-      esp_nn_softmax_s8(in_ptr, outer_size, depth, input_beta_multiplier,
-                        input_beta_left_shift, diff_min, out_ptr);
-#else
-      tflite::reference_ops::Softmax(
-          data->op_data, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int8_t>(output));
-#endif
-    }
-  } else {
-    tflite::reference_ops::SoftmaxInt16(
-        data->op_data, tflite::micro::GetTensorShape(input),
-        tflite::micro::GetTensorData<int16_t>(input),
-        tflite::micro::GetTensorShape(output),
-        tflite::micro::GetTensorData<int16_t>(output));
-  }
-}
-
-static TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
-  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
-
-  TFLITE_DCHECK(node->user_data != nullptr);
-  NodeData data = *static_cast<NodeData*>(node->user_data);
-
-  long long start_time = esp_timer_get_time();
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      tflite::reference_ops::Softmax(
-          data.op_data, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<float>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<float>(output));
-    }
-    break;
-    case kTfLiteInt8:
-    case kTfLiteInt16: {
-      SoftmaxQuantized(context, input, output, &data);
-    }
-    break;
-    default:
-      TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
-                         TfLiteTypeGetName(input->type), input->type);
-      return kTfLiteError;
-  }
-  softmax_total_time += esp_timer_get_time() - start_time;
-  return kTfLiteOk;
-}
-
-static TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
-  TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  TF_LITE_ENSURE(context, node->user_data != nullptr);
-  NodeData* data = static_cast<NodeData*>(node->user_data);
-  // Only allocate LUTs for KTfLiteInt16 data type
-  if (input->type == kTfLiteInt16) {
-    void* raw_exp_lut = context->AllocatePersistentBuffer(
-        context, sizeof(int16_t) * kInt16LUTArraySize);
-    TF_LITE_ENSURE(context, raw_exp_lut != nullptr);
-    data->op_data.exp_lut = reinterpret_cast<int16_t*>(raw_exp_lut);
-    void* one_over_one_plus_x_lut = context->AllocatePersistentBuffer(
-        context, sizeof(int16_t) * kInt16LUTArraySize);
-    TF_LITE_ENSURE(context, one_over_one_plus_x_lut != nullptr);
-    data->op_data.one_over_one_plus_x_lut =
-        reinterpret_cast<int16_t*>(one_over_one_plus_x_lut);
-  }
-
-  if (output->type == kTfLiteInt16) {
-    TF_LITE_ENSURE(context,
-                   input->type == kTfLiteInt8 || input->type == kTfLiteInt16);
-  } else {
-    TF_LITE_ENSURE_EQ(context, input->type, output->type);
-  }
-
-  // Populate LUT if required
-  if (input->type == kTfLiteInt16) {
-    TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
-    // exp LUT only used on negative values
-    // we consider exp(-10.0) is insignificant to accumulation
-    gen_lut<float, int16_t, int16_t>(
-        [](float value) { return std::exp(value); }, -10.0f, 0.0f, -1.0f, 1.0f,
-        data->op_data.exp_lut);
-    gen_lut<float, int16_t, int16_t>(
-        [](float value) { return 1.0f / (1.0f + value); }, 0.0f, 1.0f, -1.0f,
-        1.0f, data->op_data.one_over_one_plus_x_lut);
-    data->op_data.zero_point = output->params.zero_point;
-    data->op_data.scale = output->params.scale;
-  }
-
-  auto* params = static_cast<TfLiteSoftmaxParams*>(node->builtin_data);
-  auto ret_val =
-      CalculateSoftmaxParams(context, input, output, params, &data->op_data);
-
-#if ESP_NN
-  if (output->type == kTfLiteInt8 && input->type == kTfLiteInt8) {
-    const int32_t input_width = input->dims->data[1];
-    const int32_t input_height = input->dims->data[2];
-    int scratch_buf_size = esp_nn_get_softmax_scratch_size(input_width,
-                                                           input_height);
-    if (scratch_buf_size > 0) {
-      TF_LITE_ENSURE_STATUS(context->RequestScratchBufferInArena(
-        context, scratch_buf_size, &data->buffer_idx));
-    }
-  }
-#endif
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return ret_val;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_SOFTMAX() {
-  return tflite::micro::RegisterOp(Init, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/exp.cc

@@ -1,79 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/exp.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-
-namespace tflite {
-namespace {
-
-constexpr int kInputTensor = 0;
-constexpr int kOutputTensor = 0;
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32);
-  TF_LITE_ENSURE_TYPES_EQ(context, output->type, input->type);
-  TF_LITE_ENSURE_EQ(context, output->bytes, input->bytes);
-  TF_LITE_ENSURE_EQ(context, output->dims->size, input->dims->size);
-  for (int i = 0; i < output->dims->size; ++i) {
-    TF_LITE_ENSURE_EQ(context, output->dims->data[i], input->dims->data[i]);
-  }
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  int flat_size = MatchingFlatSize(tflite::micro::GetTensorShape(input),
-                                   tflite::micro::GetTensorShape(output));
-
-  if (input->type == kTfLiteFloat32) {
-    reference_ops::Exp(tflite::micro::GetTensorData<float>(input),
-                       static_cast<size_t>(flat_size),
-                       tflite::micro::GetTensorData<float>(output));
-  } else {
-    MicroPrintf("Type %s (%d) currently not supported by Exp.",
-                TfLiteTypeGetName(input->type), input->type);
-    return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-}  // namespace
-
-TfLiteRegistration Register_EXP() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/expand_dims.cc

@@ -1,148 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-
-namespace tflite {
-namespace {
-
-constexpr int kInputTensor = 0;
-constexpr int kAxisTensor = 1;
-constexpr int kOutputTensor = 0;
-
-TfLiteStatus GetAxisValueFromTensor(TfLiteContext* context,
-                                    const TfLiteTensor* axis,
-                                    int32_t* axis_value) {
-  const int axis_dims = (tflite::GetTensorShape(axis)).DimensionsCount();
-  if (axis_dims > 1) {
-    MicroPrintf("Axis has only one element for Expand_Dims.", axis_dims);
-    return kTfLiteError;
-  }
-
-  if (kTfLiteInt32 == (axis->type)) {
-    const int32_t* axis_ptr = tflite::GetTensorData<int32_t>(axis);
-    *axis_value = axis_ptr[0];
-    return kTfLiteOk;
-  } else {
-    MicroPrintf("Axis type %s (%d) not supported by Expand_Dims.",
-                TfLiteTypeGetName(axis->type), axis->type);
-    return kTfLiteError;
-  }
-}
-
-// Verifies that the output tensor's dimension shape is equivalent to inserting
-// a dimension of length 1 at the dimension index axis of input's shape as
-// defined in https://www.tensorflow.org/api_docs/python/tf/expand_dims.
-TfLiteStatus VerifyTensorDim(TfLiteContext* context, const TfLiteTensor* input,
-                             const TfLiteTensor* axis_tensor,
-                             const TfLiteTensor* output) {
-  int32_t axis_value = 0;
-  TF_LITE_ENSURE_OK(context,
-                    GetAxisValueFromTensor(context, axis_tensor, &axis_value));
-
-  tflite::RuntimeShape input_shape = tflite::GetTensorShape(input);
-  if (axis_value < 0) {
-    axis_value = input_shape.DimensionsCount() + 1 + axis_value;
-  }
-  TF_LITE_ENSURE(context, axis_value <= input_shape.DimensionsCount());
-
-  // TFLM only supports fixed dimension tensor and assumes that the output shape
-  // is fully specified in the model. As such, TFLM directly use the pointer to
-  // the dimension array in the model buffer.
-  tflite::RuntimeShape output_shape = tflite::GetTensorShape(output);
-
-  TF_LITE_ENSURE(context, output_shape.DimensionsCount() ==
-                              input_shape.DimensionsCount() + 1);
-  for (int i = 0; i < output_shape.DimensionsCount(); ++i) {
-    if (i < axis_value) {
-      TF_LITE_ENSURE(context, output_shape.Dims(i) == input_shape.Dims(i));
-    } else if (i == axis_value) {
-      TF_LITE_ENSURE(context, output_shape.Dims(i) == 1);
-    } else {
-      TF_LITE_ENSURE(context, output_shape.Dims(i) == input_shape.Dims(i - 1));
-    }
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* axis =
-      micro_context->AllocateTempInputTensor(node, kAxisTensor);
-  TF_LITE_ENSURE(context, axis != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-  output->type = input->type;
-  if (IsDynamicTensor(axis)) {
-    MicroPrintf("DynamicTensor is not yet supported by Expand_Dims.");
-    return kTfLiteError;
-  }
-  TF_LITE_ENSURE_OK(context, VerifyTensorDim(context, input, axis, output));
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(axis);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-template <typename T>
-void memCopyN(T* out, const T* in, const int num_elements) {
-  for (int i = 0; i < num_elements; ++i) {
-    out[i] = in[i];
-  }
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  const int flat_size = ElementCount(*input->dims);
-
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      memCopyN(tflite::micro::GetTensorData<float>(output),
-               tflite::micro::GetTensorData<float>(input), flat_size);
-    } break;
-    case kTfLiteInt8: {
-      memCopyN(tflite::micro::GetTensorData<int8_t>(output),
-               tflite::micro::GetTensorData<int8_t>(input), flat_size);
-    } break;
-    default:
-      MicroPrintf(
-          "Expand_Dims only currently supports int8 and float32, got %d.",
-          input->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-}  // namespace
-
-TfLiteRegistration Register_EXPAND_DIMS() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/fill.cc

@@ -1,139 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/fill.h"
-
-#include <stdint.h>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-
-namespace {
-
-template <typename T>
-TfLiteStatus EnsureEqImpl(TfLiteContext* context, const TfLiteIntArray* array,
-                          const TfLiteTensor* tensor) {
-  for (int i = 0; i < array->size; ++i) {
-    TF_LITE_ENSURE_EQ(context, array->data[i], GetTensorData<T>(tensor)[i]);
-  }
-  return kTfLiteOk;
-}
-
-// Ensure the equality of an int array and a tensor, which must be
-// one-dimensional and of an integer type.
-TfLiteStatus EnsureEq(TfLiteContext* context, const TfLiteIntArray* array,
-                      const TfLiteTensor* tensor) {
-  TF_LITE_ENSURE_EQ(context, NumDimensions(tensor), 1);
-  const auto tensor_len = tensor->dims->data[0];
-  TF_LITE_ENSURE_EQ(context, array->size, tensor_len);
-
-  switch (tensor->type) {
-    case kTfLiteInt8:
-      return EnsureEqImpl<int8_t>(context, array, tensor);
-    case kTfLiteInt16:
-      return EnsureEqImpl<int16_t>(context, array, tensor);
-    case kTfLiteInt32:
-      return EnsureEqImpl<int32_t>(context, array, tensor);
-    case kTfLiteInt64:
-      return EnsureEqImpl<int64_t>(context, array, tensor);
-    default:
-      MicroPrintf("cannot compare int array to tensor of type %d.",
-                  tensor->type);
-      return kTfLiteError;
-  }
-}
-
-constexpr int kDimsTensor = 0;
-constexpr int kValueTensor = 1;
-constexpr int kOutputTensor = 0;
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  // Ensure inputs and outputs exist.
-  TfLiteTensor* dims =
-      micro_context->AllocateTempInputTensor(node, kDimsTensor);
-  TF_LITE_ENSURE(context, dims != nullptr);
-  TfLiteTensor* value =
-      micro_context->AllocateTempInputTensor(node, kValueTensor);
-  TF_LITE_ENSURE(context, value != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  // The value tensor must be a scalar.
-  TF_LITE_ENSURE_EQ(context, NumDimensions(value), 0);
-
-  // The value type and output type must match.
-  TF_LITE_ENSURE_EQ(context, value->type, output->type);
-
-  // The dimension of the output tensor is known in model already.
-  TFLITE_DCHECK(output->dims != nullptr);
-
-  if (dims->data.data != nullptr) {
-    // When the dims tensor is specified in model already (i.e. is not an
-    // activation tensor), the dims tensor must match the output tensor shape.
-    // As a byproduct, ensures the dims tensor is of an integer type.
-    TF_LITE_ENSURE_OK(context, EnsureEq(context, output->dims, dims));
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(dims);
-  micro_context->DeallocateTempTfLiteTensor(value);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-template <typename T>
-void FillImpl(const TfLiteEvalTensor* value, TfLiteEvalTensor* output) {
-  reference_ops::Fill(
-      micro::GetTensorShape(value), micro::GetTensorData<T>(value),
-      micro::GetTensorShape(output), micro::GetTensorData<T>(output));
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* value =
-      micro::GetEvalInput(context, node, kValueTensor);
-  TfLiteEvalTensor* output = micro::GetEvalOutput(context, node, kOutputTensor);
-
-  switch (value->type) {
-    case kTfLiteFloat32:
-      FillImpl<float>(value, output);
-      break;
-    case kTfLiteInt32:
-      FillImpl<int32_t>(value, output);
-      break;
-    case kTfLiteInt8:
-      FillImpl<int8_t>(value, output);
-      break;
-    default:
-      MicroPrintf("Fill only currently supports float32 for input 1, got %d.",
-                  TfLiteTypeGetName(value->type));
-      return kTfLiteError;
-  }
-
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_FILL() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/floor_div.cc

@@ -1,129 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/floor_div.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/reference/binary_function.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-
-namespace tflite {
-namespace {
-
-// Input/output tensor index.
-constexpr int kInputTensor1 = 0;
-constexpr int kInputTensor2 = 1;
-constexpr int kOutputTensor = 0;
-
-TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  TfLiteTensor* input1 =
-      micro_context->AllocateTempInputTensor(node, kInputTensor1);
-  TF_LITE_ENSURE(context, input1 != nullptr);
-  TfLiteTensor* input2 =
-      micro_context->AllocateTempInputTensor(node, kInputTensor2);
-  TF_LITE_ENSURE(context, input2 != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
-  TF_LITE_ENSURE_TYPES_EQ(context, input1->type, output->type);
-
-  micro_context->DeallocateTempTfLiteTensor(input1);
-  micro_context->DeallocateTempTfLiteTensor(input2);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  return nullptr;
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  return CalculateOpData(context, node);
-}
-
-template <typename T>
-TfLiteStatus EvalFloorDiv(TfLiteContext* context,
-                          const TfLiteEvalTensor* input1,
-                          const TfLiteEvalTensor* input2,
-                          TfLiteEvalTensor* output) {
-  const T* denominator_data = tflite::micro::GetTensorData<T>(input2);
-
-  // Validate the denominator.
-  for (int i = 0; i < tflite::ElementCount(*input2->dims); ++i) {
-    if (std::equal_to<T>()(denominator_data[i], 0)) {
-      MicroPrintf("Division by 0");
-      return kTfLiteError;
-    }
-  }
-
-  bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
-
-  if (requires_broadcast) {
-    reference_ops::BroadcastBinaryFunction4DSlow<T, T, T>(
-        tflite::micro::GetTensorShape(input1),
-        tflite::micro::GetTensorData<T>(input1),
-        tflite::micro::GetTensorShape(input2), denominator_data,
-        tflite::micro::GetTensorShape(output),
-        tflite::micro::GetTensorData<T>(output), reference_ops::FloorDiv<T>);
-  } else {
-    reference_ops::BinaryFunction<T, T, T>(
-        tflite::micro::GetTensorShape(input1),
-        tflite::micro::GetTensorData<T>(input1),
-        tflite::micro::GetTensorShape(input2), denominator_data,
-        tflite::micro::GetTensorShape(output),
-        tflite::micro::GetTensorData<T>(output), reference_ops::FloorDiv<T>);
-  }
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input1 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor1);
-  const TfLiteEvalTensor* input2 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor2);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  switch (input1->type) {
-    case kTfLiteFloat32: {
-      return EvalFloorDiv<float>(context, input1, input2, output);
-    }
-    default: {
-      MicroPrintf("Type '%s' is not supported by FLOOR_DIV.",
-                  TfLiteTypeGetName(input1->type));
-      return kTfLiteError;
-    }
-  }
-}
-
-}  // namespace
-
-TfLiteRegistration Register_FLOOR_DIV() {
-  return tflite::micro::RegisterOp(Init, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/floor_mod.cc

@@ -1,127 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/floor_mod.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/reference/binary_function.h"
-#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-
-// OLD-TODO(b/117523611): We should factor out a binary_op and put binary ops
-// there.
-namespace tflite {
-namespace {
-
-// Input/output tensor index.
-constexpr int kInputTensor1 = 0;
-constexpr int kInputTensor2 = 1;
-constexpr int kOutputTensor = 0;
-
-// OLD-TODO(b/117912880): Support quantization.
-
-TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  TfLiteTensor* input1 =
-      micro_context->AllocateTempInputTensor(node, kInputTensor1);
-  TF_LITE_ENSURE(context, input1 != nullptr);
-  TfLiteTensor* input2 =
-      micro_context->AllocateTempInputTensor(node, kInputTensor2);
-  TF_LITE_ENSURE(context, input2 != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  TF_LITE_ENSURE_TYPES_EQ(context, input1->type, input2->type);
-  TF_LITE_ENSURE_TYPES_EQ(context, input1->type, output->type);
-
-  micro_context->DeallocateTempTfLiteTensor(input1);
-  micro_context->DeallocateTempTfLiteTensor(input2);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  return nullptr;
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  return CalculateOpData(context, node);
-}
-
-template <typename T>
-TfLiteStatus EvalFloorMod(TfLiteContext* context, bool requires_broadcast,
-                          const TfLiteEvalTensor* input1,
-                          const TfLiteEvalTensor* input2,
-                          TfLiteEvalTensor* output) {
-  const T* denominator_data = tflite::micro::GetTensorData<T>(input2);
-
-  if (requires_broadcast) {
-    reference_ops::BroadcastBinaryFunction4DSlow<T, T, T>(
-        tflite::micro::GetTensorShape(input1),
-        tflite::micro::GetTensorData<T>(input1),
-        tflite::micro::GetTensorShape(input2), denominator_data,
-        tflite::micro::GetTensorShape(output),
-        tflite::micro::GetTensorData<T>(output), reference_ops::FloorMod<T>);
-  } else {
-    reference_ops::BinaryFunction<T, T, T>(
-        tflite::micro::GetTensorShape(input1),
-        tflite::micro::GetTensorData<T>(input1),
-        tflite::micro::GetTensorShape(input2), denominator_data,
-        tflite::micro::GetTensorShape(output),
-        tflite::micro::GetTensorData<T>(output), reference_ops::FloorMod<T>);
-  }
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input1 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor1);
-  const TfLiteEvalTensor* input2 =
-      tflite::micro::GetEvalInput(context, node, kInputTensor2);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  bool requires_broadcast = !tflite::micro::HaveSameShapes(input1, input2);
-
-  switch (input1->type) {
-    case kTfLiteFloat32: {
-      return EvalFloorMod<float>(context, requires_broadcast, input1, input2,
-                                 output);
-    }
-    default: {
-      MicroPrintf("Type '%s' is not supported by FLOOR_MOD.",
-                  TfLiteTypeGetName(input1->type));
-      return kTfLiteError;
-    }
-  }
-}
-
-}  // namespace
-
-TfLiteRegistration Register_FLOOR_MOD() {
-  return tflite::micro::RegisterOp(Init, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/fully_connected.cc

@@ -1,158 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/kernels/fully_connected.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/reference/fully_connected.h"
-#include "tensorflow/lite/kernels/internal/reference/integer_ops/fully_connected.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context,
-                                           sizeof(OpDataFullyConnected));
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TFLITE_DCHECK(node->user_data != nullptr);
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-
-  auto* data = static_cast<OpDataFullyConnected*>(node->user_data);
-  const auto params =
-      static_cast<const TfLiteFullyConnectedParams*>(node->builtin_data);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kFullyConnectedInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* filter = micro_context->AllocateTempInputTensor(
-      node, kFullyConnectedWeightsTensor);
-  TF_LITE_ENSURE(context, filter != nullptr);
-  TfLiteTensor* bias =
-      micro_context->AllocateTempInputTensor(node, kFullyConnectedBiasTensor);
-  TfLiteTensor* output = micro_context->AllocateTempOutputTensor(
-      node, kFullyConnectedOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-
-  TF_LITE_ENSURE_OK(context, CalculateOpDataFullyConnected(
-                                 context, params->activation, input->type,
-                                 input, filter, bias, output, data));
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(filter);
-  if (bias != nullptr) {
-    micro_context->DeallocateTempTfLiteTensor(bias);
-  }
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-  const auto* params =
-      static_cast<const TfLiteFullyConnectedParams*>(node->builtin_data);
-
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kFullyConnectedInputTensor);
-  const TfLiteEvalTensor* filter =
-      tflite::micro::GetEvalInput(context, node, kFullyConnectedWeightsTensor);
-  const TfLiteEvalTensor* bias =
-      tflite::micro::GetEvalInput(context, node, kFullyConnectedBiasTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kFullyConnectedOutputTensor);
-
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const auto& data =
-      *(static_cast<const OpDataFullyConnected*>(node->user_data));
-
-  // Checks in Prepare ensure input, output and filter types are all the same.
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      const float* bias_data =
-          nullptr != bias ? tflite::micro::GetTensorData<float>(bias) : nullptr;
-
-      tflite::reference_ops::FullyConnected(
-          FullyConnectedParamsFloat(params->activation),
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<float>(input),
-          tflite::micro::GetTensorShape(filter),
-          tflite::micro::GetTensorData<float>(filter),
-          tflite::micro::GetTensorShape(bias), bias_data,
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<float>(output));
-      break;
-    }
-
-    case kTfLiteInt8: {
-      const int32_t* bias_data =
-          nullptr != bias ? tflite::micro::GetTensorData<int32_t>(bias)
-                          : nullptr;
-
-      tflite::reference_integer_ops::FullyConnected(
-          FullyConnectedParamsQuantized(data),
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(filter),
-          tflite::micro::GetTensorData<int8_t>(filter),
-          tflite::micro::GetTensorShape(bias), bias_data,
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int8_t>(output));
-      break;
-    }
-
-    case kTfLiteInt16: {
-      const int64_t* bias_data =
-          nullptr != bias ? tflite::micro::GetTensorData<int64_t>(bias)
-                          : nullptr;
-
-      tflite::reference_integer_ops::FullyConnected(
-          FullyConnectedParamsQuantized(data),
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int16_t>(input),
-          tflite::micro::GetTensorShape(filter),
-          tflite::micro::GetTensorData<int8_t>(filter),
-          tflite::micro::GetTensorShape(bias), bias_data,
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int16_t>(output));
-      break;
-    }
-
-    default: {
-      MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
-                  input->type);
-      return kTfLiteError;
-    }
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_FULLY_CONNECTED() {
-  return tflite::micro::RegisterOp(Init, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/gather.cc

@@ -1,223 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-
-namespace tflite {
-namespace {
-
-constexpr int kInputTensor = 0;
-constexpr int kInputPositions = 1;
-constexpr int kOutputTensor = 0;
-
-template <typename InputT, typename CoordsT = int32_t>
-TfLiteStatus Gather(const TfLiteGatherParams* params,
-                    const TfLiteEvalTensor* input,
-                    const TfLiteEvalTensor* coords, TfLiteEvalTensor* output) {
-  const InputT* input_data = tflite::micro::GetTensorData<InputT>(input);
-  const CoordsT* coords_data = tflite::micro::GetTensorData<CoordsT>(coords);
-  InputT* output_data = tflite::micro::GetTensorData<InputT>(output);
-  const TfLiteIntArray* input_dims = input->dims;
-  const int input_dims_size = input_dims->size;
-  int axis = params->axis;
-  if (axis < 0) {
-    axis += input_dims_size;
-  }
-  TFLITE_DCHECK_GE(axis, 0);
-  TFLITE_DCHECK_LT(axis, input_dims_size);
-
-  int batch_dims = params->batch_dims;
-  // batch_dims should be in range: [-rank(coords), rank(coords)].
-  // Negative batch_dims is added with rank of coords.
-  const TfLiteIntArray* coords_dims = coords->dims;
-  const int coords_dims_size = coords_dims->size;
-  if (batch_dims < 0) {
-    batch_dims += coords_dims_size;
-  }
-  TFLITE_DCHECK_GE(batch_dims, 0);
-  TFLITE_DCHECK_LT(batch_dims, input_dims_size);
-  TFLITE_DCHECK_LE(batch_dims, coords_dims_size);
-  TFLITE_DCHECK_GE(axis, batch_dims);
-  for (int i = 0; i < batch_dims; ++i) {
-    TFLITE_DCHECK_EQ(input_dims->data[i], coords_dims->data[i]);
-  }
-
-  const int axis_size = input_dims->data[axis];
-
-  int batch_size = 1;
-  for (int i = 0; i < batch_dims; ++i) {
-    batch_size *= input_dims->data[i];
-  }
-  int outer_size = 1;
-  for (int i = batch_dims; i < axis; ++i) {
-    outer_size *= input_dims->data[i];
-  }
-  int inner_size = 1;
-  for (int i = axis + 1; i < input_dims_size; ++i) {
-    inner_size *= input_dims->data[i];
-  }
-  int coord_size = 1;
-  for (int i = batch_dims; i < coords_dims_size; ++i) {
-    coord_size *= coords_dims->data[i];
-  }
-
-  for (int batch = 0; batch < batch_size; ++batch) {
-    for (int outer = 0; outer < outer_size; ++outer) {
-      for (int coord = 0; coord < coord_size; ++coord) {
-        TFLITE_DCHECK_GE(coords_data[coord], 0);
-        TFLITE_DCHECK_LT(coords_data[coord], axis_size);
-        std::memcpy(output_data +
-                        (((batch * outer_size) + outer) * coord_size + coord) *
-                            inner_size,
-                    input_data + (((batch * outer_size) + outer) * axis_size +
-                                  coords_data[batch * coord_size + coord]) *
-                                     inner_size,
-                    sizeof(InputT) * inner_size);
-      }
-    }
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  const auto* params =
-      reinterpret_cast<const TfLiteGatherParams*>(node->builtin_data);
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* coords =
-      micro_context->AllocateTempInputTensor(node, kInputPositions);
-  TF_LITE_ENSURE(context, coords != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  switch (coords->type) {
-    case kTfLiteInt32:
-      break;
-    default:
-      MicroPrintf("Positions of type '%s' are not supported by gather.",
-                  TfLiteTypeGetName(coords->type));
-      return kTfLiteError;
-      break;
-  }
-
-  // Assign to output the input type.
-  output->type = input->type;
-
-  // Check conditions for different types.
-  switch (input->type) {
-    case kTfLiteFloat32:
-    case kTfLiteInt8:
-      break;
-    default:
-      MicroPrintf("Type '%s' is not supported by gather.",
-                  TfLiteTypeGetName(input->type));
-      return kTfLiteError;
-      break;
-  }
-
-  int axis = params->axis;
-  if (axis < 0) {
-    axis += NumDimensions(input);
-  }
-  TF_LITE_ENSURE(context, 0 <= axis && axis < NumDimensions(input));
-
-  int batch_dims = params->batch_dims;
-  // batch_dims should be in range: [-rank(coords), rank(coords)].
-  // Negative batch_dims is added with rank of coords.
-  if (batch_dims < 0) {
-    batch_dims += NumDimensions(coords);
-  }
-  TF_LITE_ENSURE(context, batch_dims <= axis);
-  TF_LITE_ENSURE(context, 0 <= batch_dims && batch_dims < NumDimensions(input));
-  TF_LITE_ENSURE(context, batch_dims <= NumDimensions(coords));
-  for (int i = 0; i < batch_dims; ++i) {
-    TF_LITE_ENSURE_EQ(context, input->dims->data[i], coords->dims->data[i]);
-  }
-
-  // GATHER updates the output tensor dimensions, but TfLiteTensor in the
-  // MicroInterpreter is a temporary allocation. We must therefore relocate the
-  // dims from the FlatBuffer to the persistant storage arena.
-  TfLiteEvalTensor* output_eval =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  TF_LITE_ENSURE_OK(context, tflite::micro::CreateWritableTensorDimsWithCopy(
-                                 context, output, output_eval));
-
-  TfLiteIntArray* output_shape = output->dims;
-  output_shape->size =
-      NumDimensions(input) + NumDimensions(coords) - 1 - batch_dims;
-  int output_index = 0;
-  for (int i = 0; i < axis; ++i) {
-    output_shape->data[output_index++] = input->dims->data[i];
-  }
-  for (int i = batch_dims; i < coords->dims->size; ++i) {
-    output_shape->data[output_index++] = coords->dims->data[i];
-  }
-  for (int i = axis + 1; i < input->dims->size; ++i) {
-    output_shape->data[output_index++] = input->dims->data[i];
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(coords);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const auto* params =
-      reinterpret_cast<const TfLiteGatherParams*>(node->builtin_data);
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  const TfLiteEvalTensor* coords =
-      tflite::micro::GetEvalInput(context, node, kInputPositions);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  if (coords->type == kTfLiteInt32) {
-    switch (input->type) {
-      case kTfLiteFloat32:
-        return Gather<float, int32_t>(params, input, coords, output);
-        break;
-      case kTfLiteInt8:
-        return Gather<int8_t, int32_t>(params, input, coords, output);
-        break;
-      default:
-        MicroPrintf("Type '%s' is not supported by gather.",
-                    TfLiteTypeGetName(input->type));
-        return kTfLiteError;
-        break;
-    }
-  }
-  return kTfLiteOk;
-}
-}  // namespace
-
-TfLiteRegistration Register_GATHER() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/gather_nd.cc

@@ -1,204 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-
-namespace tflite {
-namespace {
-
-constexpr int kParams = 0;
-constexpr int kIndices = 1;
-constexpr int kOutputTensor = 0;
-constexpr int MAX_INDICES_ND = 5;
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  TfLiteTensor* params = micro_context->AllocateTempInputTensor(node, kParams);
-  TF_LITE_ENSURE(context, params != nullptr);
-  TfLiteTensor* indices =
-      micro_context->AllocateTempInputTensor(node, kIndices);
-  TF_LITE_ENSURE(context, indices != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  switch (params->type) {
-    case kTfLiteFloat32:
-    case kTfLiteInt8:
-      break;
-    default:
-      MicroPrintf("Params of type '%s' are not supported by gather_nd.",
-                  TfLiteTypeGetName(params->type));
-      return kTfLiteError;
-      break;
-  }
-  switch (indices->type) {
-    case kTfLiteInt32:
-      break;
-    default:
-      MicroPrintf("Indices of type '%s' are not supported by gather_nd.",
-                  TfLiteTypeGetName(indices->type));
-      return kTfLiteError;
-  }
-
-  const int params_rank = NumDimensions(params);
-  const int indices_rank = NumDimensions(indices);
-  const int indices_nd = SizeOfDimension(indices, indices_rank - 1);
-  if (params_rank < 1) {
-    MicroPrintf("Params must be at least a vector.");
-    return kTfLiteError;
-  }
-  if (indices_rank < 1) {
-    MicroPrintf("Indices must be at least a vector.");
-    return kTfLiteError;
-  }
-  if (indices_nd > params_rank) {
-    MicroPrintf("Index innermost dimension length must be <= params rank.");
-    return kTfLiteError;
-  }
-  if (indices_nd > MAX_INDICES_ND) {
-    MicroPrintf("Index innermost dimension length must not exceed %d.",
-                MAX_INDICES_ND);
-    return kTfLiteError;
-  }
-
-  // Assign to output the input type.
-  output->type = params->type;
-
-  // TFLM gather_nd does not create the output tensor, but it needs to ensure
-  // that the output shape is correct. The result shape is
-  // indices.shape[:-1] + params.shape[indices.shape[-1]:]
-  TfLiteIntArray* output_shape = output->dims;
-  int output_index = 0;
-  for (int i = 0; i < indices_rank - 1; ++i) {
-    output_shape->data[output_index++] = indices->dims->data[i];
-  }
-  for (int i = indices_nd; i < params_rank; ++i) {
-    output_shape->data[output_index++] = params->dims->data[i];
-  }
-  output_shape->size = output_index;
-
-  micro_context->DeallocateTempTfLiteTensor(params);
-  micro_context->DeallocateTempTfLiteTensor(indices);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-template <typename ParamsT, typename IndicesT>
-TfLiteStatus GatherNd(const TfLiteEvalTensor* params,
-                      const TfLiteEvalTensor* indices,
-                      TfLiteEvalTensor* output) {
-  const int indices_dims = indices->dims->size;
-  const int indices_nd = indices->dims->data[indices_dims - 1];
-  const int params_dims = params->dims->size;
-  const IndicesT* index_data = tflite::micro::GetTensorData<IndicesT>(indices);
-  const ParamsT* param_data = tflite::micro::GetTensorData<ParamsT>(params);
-  ParamsT* output_data = tflite::micro::GetTensorData<ParamsT>(output);
-
-  int n_slices = 1;
-  for (int i = 0; i < indices_dims - 1; ++i) {
-    n_slices *= indices->dims->data[i];
-  }
-
-  // If indices[-1] == params.rank, fetch single elements.
-  // If indices[-1] < params.rank, fetch slices.
-  int slice_size = 1;
-  for (int i = indices_nd; i < params_dims; ++i) {
-    slice_size *= params->dims->data[i];
-  }
-
-  int params_flat_size = ElementCount(*params->dims);
-  int remain_flat_size = params_flat_size;
-
-  // Number of elements per dimension
-  int dims_to_count[MAX_INDICES_ND];
-  for (int i = 0; i < indices_nd; ++i) {
-    dims_to_count[i] = remain_flat_size / params->dims->data[i];
-    remain_flat_size = dims_to_count[i];
-  }
-
-  for (int i = 0; i < n_slices; ++i) {
-    int from_pos = 0;
-    for (int j = 0; j < indices_nd; ++j) {
-      int offset = i * indices_nd + j;
-      IndicesT index = index_data[offset];
-      from_pos += index * dims_to_count[j];
-    }
-    if (from_pos < 0 || from_pos + slice_size > params_flat_size) {
-      return kTfLiteError;
-    }
-    std::memcpy(output_data + i * slice_size, param_data + from_pos,
-                sizeof(ParamsT) * slice_size);
-  }
-  return kTfLiteOk;
-}
-
-template <typename IndicesT>
-TfLiteStatus EvalGatherNd(TfLiteContext* context,
-                          const TfLiteEvalTensor* params,
-                          const TfLiteEvalTensor* indices,
-                          TfLiteEvalTensor* output) {
-  TfLiteStatus status = kTfLiteError;
-  switch (params->type) {
-    case kTfLiteFloat32:
-      status = GatherNd<float, IndicesT>(params, indices, output);
-      break;
-    case kTfLiteInt8:
-      status = GatherNd<int8_t, IndicesT>(params, indices, output);
-      break;
-    default:
-      MicroPrintf("Params type '%s' are not supported by gather_nd.",
-                  TfLiteTypeGetName(params->type));
-      return kTfLiteError;
-  }
-  if (status != kTfLiteOk) {
-    MicroPrintf("gather_nd index out of bounds");
-  }
-  return status;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* params =
-      tflite::micro::GetEvalInput(context, node, kParams);
-  const TfLiteEvalTensor* indices =
-      tflite::micro::GetEvalInput(context, node, kIndices);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  switch (indices->type) {
-    case kTfLiteInt32:
-      return EvalGatherNd<int32_t>(context, params, indices, output);
-      break;
-    default:
-      MicroPrintf("Indices of type '%s' are not supported by gather_nd.",
-                  TfLiteTypeGetName(indices->type));
-      return kTfLiteError;
-  }
-}
-}  // namespace
-
-TfLiteRegistration Register_GATHER_ND() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_runner.cc

@@ -1,122 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/kernels/kernel_runner.h"
-
-#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h"
-#include "tensorflow/lite/micro/micro_arena_constants.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/micro/test_helpers.h"
-
-namespace tflite {
-namespace micro {
-
-// TODO(b/161841696): Consider moving away from global arena buffers:
-constexpr int KernelRunner::kKernelRunnerBufferSize_;
-uint8_t KernelRunner::kKernelRunnerBuffer_[];
-
-void ClearBufferApi(TfLiteContext* context_) {
-  context_->GetScratchBuffer = nullptr;
-  context_->GetExternalContext = nullptr;
-  context_->AllocatePersistentBuffer = nullptr;
-  context_->RequestScratchBufferInArena = nullptr;
-}
-
-KernelRunner::KernelRunner(const TfLiteRegistration& registration,
-                           TfLiteTensor* tensors, int tensors_size,
-                           TfLiteIntArray* inputs, TfLiteIntArray* outputs,
-                           void* builtin_data, TfLiteIntArray* intermediates)
-    : registration_(registration),
-      allocator_(SingleArenaBufferAllocator::Create(GetMicroErrorReporter(),
-                                                    kKernelRunnerBuffer_,
-                                                    kKernelRunnerBufferSize_)),
-      mock_micro_graph_(allocator_),
-      fake_micro_context_(tensors, allocator_, &mock_micro_graph_) {
-  // Prepare TfLiteContext:
-  context_.impl_ = static_cast<void*>(&fake_micro_context_);
-  context_.ReportError = MicroContextReportOpError;
-  context_.recommended_num_threads = 1;
-  context_.GetTensor = MicroContextGetTensor;
-  context_.GetEvalTensor = MicroContextGetEvalTensor;
-  tflite::micro::ClearBufferApi(&context_);
-  context_.AllocatePersistentBuffer = MicroContextAllocatePersistentBuffer;
-
-  context_.recommended_num_threads = 0;
-
-  // Prepare TfLiteNode:
-  node_.inputs = inputs;
-  node_.outputs = outputs;
-  node_.builtin_data = builtin_data;
-  node_.intermediates = intermediates;
-}
-
-bool KernelRunner::ValidateTempBufferDeallocated() {
-  return fake_micro_context_.IsAllTempTfLiteTensorDeallocated();
-}
-
-TfLiteStatus KernelRunner::InitAndPrepare(const char* init_data,
-                                          size_t length) {
-  if (registration_.init) {
-    tflite::micro::ClearBufferApi(&context_);
-    context_.AllocatePersistentBuffer = MicroContextAllocatePersistentBuffer;
-    node_.user_data = registration_.init(&context_, init_data, length);
-  }
-
-  TF_LITE_ENSURE(&context_, ValidateTempBufferDeallocated());
-
-  if (registration_.prepare) {
-    tflite ::micro::ClearBufferApi(&context_);
-    context_.AllocatePersistentBuffer = MicroContextAllocatePersistentBuffer;
-    context_.RequestScratchBufferInArena =
-        MicroContextRequestScratchBufferInArena;
-    context_.GetExternalContext = MicroContextGetExternalContext;
-    TF_LITE_ENSURE_STATUS(registration_.prepare(&context_, &node_));
-  }
-
-  TF_LITE_ENSURE(&context_, ValidateTempBufferDeallocated());
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus KernelRunner::Invoke() {
-  tflite::micro::ClearBufferApi(&context_);
-  context_.GetScratchBuffer = MicroContextGetScratchBuffer;
-
-  if (registration_.invoke == nullptr) {
-    MicroPrintf("TfLiteRegistration missing invoke function pointer!");
-    return kTfLiteError;
-  }
-
-  TF_LITE_ENSURE_STATUS(registration_.invoke(&context_, &node_));
-
-  TF_LITE_ENSURE(&context_, ValidateTempBufferDeallocated());
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus KernelRunner::Free() {
-  tflite::micro::ClearBufferApi(&context_);
-  context_.GetScratchBuffer = MicroContextGetScratchBuffer;
-
-  if (registration_.free == nullptr) {
-    MicroPrintf("TfLiteRegistration missing free function pointer!");
-    return kTfLiteError;
-  }
-
-  registration_.free(&context_, node_.user_data);
-  return kTfLiteOk;
-}
-}  // namespace micro
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_runner.h

@@ -1,81 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_
-#define TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h"
-#include "tensorflow/lite/micro/fake_micro_context.h"
-#include "tensorflow/lite/micro/mock_micro_graph.h"
-
-namespace tflite {
-namespace micro {
-
-// Helper class to perform a simulated kernel (i.e. TfLiteRegistration)
-// lifecycle (init, prepare, invoke). All internal allocations are handled by
-// this class. Simply pass in the registration, list of required tensors, inputs
-// array, outputs array, and any pre-builtin data. Calling Invoke() will
-// automatically walk the kernel and outputs will be ready on the TfLiteTensor
-// output provided during construction.
-class KernelRunner {
- public:
-  KernelRunner(const TfLiteRegistration& registration, TfLiteTensor* tensors,
-               int tensors_size, TfLiteIntArray* inputs,
-               TfLiteIntArray* outputs, void* builtin_data,
-               TfLiteIntArray* intermediates = nullptr);
-
-  // Calls init and prepare on the kernel (i.e. TfLiteRegistration) struct. Any
-  // exceptions will be DebugLog'd and returned as a status code.
-  TfLiteStatus InitAndPrepare(const char* init_data = nullptr,
-                              size_t length = 0);
-
-  // Calls init, prepare, and invoke on a given TfLiteRegistration pointer.
-  // After successful invoke, results will be available in the output tensor as
-  // passed into the constructor of this class.
-  TfLiteStatus Invoke();
-
-  // Calls Free on a given TfLiteRegistration pointer(if it's implemented).
-  // After successful Free, kTfLiteOk status will be returned. If Free is not
-  // implemented for a given kernel kTfLiteError will be returned.
-  TfLiteStatus Free();
-
-  // Returns a pointer to the internal MockMicroGraph which KernelRunner uses
-  // to stub out MicroGraph methods and track invocations on each subgraph.
-  MockMicroGraph* GetMockGraph() { return &mock_micro_graph_; }
-
-  // Returns true if all temp buffer in tests are deallocated.
-  // TODO(b/209453859): move this function to private after deallocation checks
-  // are enabled for all kernel tests.
-  bool ValidateTempBufferDeallocated();
-
- private:
-  static constexpr int kKernelRunnerBufferSize_ = 10000;
-  static uint8_t kKernelRunnerBuffer_[kKernelRunnerBufferSize_];
-
-  TfLiteContext context_ = {};
-  TfLiteNode node_ = {};
-  const TfLiteRegistration& registration_;
-
-  SingleArenaBufferAllocator* allocator_;
-  MockMicroGraph mock_micro_graph_;
-  FakeMicroContext fake_micro_context_;
-};
-
-}  // namespace micro
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_RUNNER_H_

code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_util.cc

@@ -1,260 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/memory_helpers.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-
-namespace tflite {
-namespace micro {
-
-namespace {
-
-int ValidateTensorIndexing(const TfLiteContext* context, int index,
-                           int max_size, const int* tensor_indices) {
-  if (index >= 0 && index < max_size) {
-    const int tensor_index = tensor_indices[index];
-    if (tensor_index != kTfLiteOptionalTensor) {
-      return tensor_index;
-    }
-  }
-  return -1;
-}
-
-}  // namespace
-
-TfLiteRegistration RegisterOp(
-    void* (*init)(TfLiteContext* context, const char* buffer, size_t length),
-    TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node),
-    TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node),
-    void (*free)(TfLiteContext* context, void* buffer)) {
-  return {/*init=*/init,
-          /*free=*/free,
-          /*prepare=*/prepare,
-          /*invoke=*/invoke,
-          /*profiling_string=*/nullptr,
-          /*builtin_code=*/0,
-          /*custom_name=*/nullptr,
-          /*version=*/0,
-          /*registration_external=*/nullptr};
-}
-
-// Returns a mutable tensor for a given input index. is_variable must be checked
-// during prepare when the full TfLiteTensor is available.
-TfLiteEvalTensor* GetMutableEvalInput(const TfLiteContext* context,
-                                      const TfLiteNode* node, int index) {
-  TFLITE_DCHECK(context != nullptr);
-  TFLITE_DCHECK(node != nullptr);
-  const int tensor_index = ValidateTensorIndexing(
-      context, index, node->inputs->size, node->inputs->data);
-
-  if (tensor_index < 0) {
-    return nullptr;
-  }
-
-  return context->GetEvalTensor(context, node->inputs->data[index]);
-}
-
-// Returns the TfLiteEvalTensor struct for a given input index in a node.
-const TfLiteEvalTensor* GetEvalInput(const TfLiteContext* context,
-                                     const TfLiteNode* node, int index) {
-  return GetMutableEvalInput(context, node, index);
-}
-
-// Returns the TfLiteEvalTensor struct for a given output index in a node.
-TfLiteEvalTensor* GetEvalOutput(const TfLiteContext* context,
-                                const TfLiteNode* node, int index) {
-  TFLITE_DCHECK(context != nullptr);
-  TFLITE_DCHECK(node != nullptr);
-  return context->GetEvalTensor(context, node->outputs->data[index]);
-}
-
-bool HaveSameShapes(const TfLiteEvalTensor* input1,
-                    const TfLiteEvalTensor* input2) {
-  TFLITE_DCHECK(input1 != nullptr);
-  TFLITE_DCHECK(input2 != nullptr);
-  return TfLiteIntArrayEqual(input1->dims, input2->dims);
-}
-
-const RuntimeShape GetTensorShape(const TfLiteEvalTensor* tensor) {
-  if (tensor == nullptr || tensor->dims == nullptr) {
-    return RuntimeShape();
-  }
-  TfLiteIntArray* dims = tensor->dims;
-  const int dims_size = dims->size;
-  const int32_t* dims_data = reinterpret_cast<const int32_t*>(dims->data);
-  return RuntimeShape(dims_size, dims_data);
-}
-
-PaddingType RuntimePaddingType(TfLitePadding padding) {
-  switch (padding) {
-    case TfLitePadding::kTfLitePaddingSame:
-      return PaddingType::kSame;
-    case TfLitePadding::kTfLitePaddingValid:
-      return PaddingType::kValid;
-    case TfLitePadding::kTfLitePaddingUnknown:
-    default:
-      return PaddingType::kNone;
-  }
-}
-
-// Relocate tensor dims from FlatBuffer to the persistent storage arena.
-// The old dims data is copied to the new storage area.
-// The tensor and eval_tensor must be the same tensor.
-// Only use during Prepare phase.
-TfLiteStatus CreateWritableTensorDimsWithCopy(TfLiteContext* context,
-                                              TfLiteTensor* tensor,
-                                              TfLiteEvalTensor* eval_tensor) {
-  TF_LITE_ENSURE(context, tensor != nullptr);
-  TF_LITE_ENSURE(context, eval_tensor != nullptr);
-  TF_LITE_ENSURE(context, context->AllocatePersistentBuffer != nullptr);
-  int ranks = tensor->dims->size;
-  size_t alloc_size = TfLiteIntArrayGetSizeInBytes(ranks);
-  TfLiteIntArray* new_dims = static_cast<TfLiteIntArray*>(
-      context->AllocatePersistentBuffer(context, alloc_size));
-  TfLiteIntArray* old_dims = tensor->dims;
-  new_dims->size = ranks;
-  tensor->dims = new_dims;
-  eval_tensor->dims = new_dims;
-  for (int i = 0; i < ranks; i++) {
-    new_dims->data[i] = old_dims->data[i];
-  }
-
-  return kTfLiteOk;
-}
-
-// Verify that both tensors have the same type and size, then return the size
-// of both tensors in bytes if they are the same, or -1 if they are different.
-size_t ValidateAndGetTensorSizes(const TfLiteEvalTensor* tensor1,
-                                 const TfLiteEvalTensor* tensor2) {
-  TFLITE_DCHECK(tensor1->type == tensor2->type);
-  size_t tensor1_size = 0;
-  size_t tensor2_size = 0;
-  TfLiteEvalTensorByteLength(tensor1, &tensor1_size);
-  TfLiteEvalTensorByteLength(tensor2, &tensor2_size);
-  return (tensor1_size == tensor2_size) ? tensor1_size : -1;
-}
-
-TfLiteStatus CopyOpInputsToOpOutputs(TfLiteContext* context, TfLiteNode* node) {
-  TF_LITE_ENSURE(context, node->inputs->size == node->outputs->size);
-  for (int i = 0; i < node->inputs->size; i++) {
-    const TfLiteEvalTensor* input =
-        tflite::micro::GetEvalInput(context, node, i);
-    TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, i);
-    int bytes = ValidateAndGetTensorSizes(input, output);
-    TF_LITE_ENSURE(context, bytes >= 0);
-    memcpy(output->data.raw, input->data.raw, bytes);
-  }
-  return kTfLiteOk;
-}
-
-//  Args:
-//    1. int8_t tensor_data - int8_t buffer of unknown size who's data you'd
-//    like
-//  to print
-//    2. int n_btyes -  a small int representing number of bytes you want to
-//    print
-//  to debug output. It should always be <= tensor_data's size.
-//    3. prefix - optional message you'd like to print before printing bytes
-//
-//  Purpose:
-//    Function takes in paramaters above and prints n_bytes bytes from the
-//  tensor_data buffer. This can be use to debug  the output of a model and it's
-//  op.
-
-void PrintNBytes(const int8_t* tensor_data, int n_bytes, const char* prefix) {
-  if (prefix != nullptr) {
-    MicroPrintf("%s", prefix);
-  }
-
-  for (int i = 0; i < n_bytes; ++i) {
-    MicroPrintf(" %x", tensor_data[i]);
-  }
-  MicroPrintf("\n");
-}
-
-// same as the PrintNBytes above but the buffer needs to be extracted out of the
-// TfLiteEvalTensor*
-void PrintNBytes(const TfLiteEvalTensor* tensor, int n_bytes,
-                 const char* prefix) {
-  const int8_t* tensor_data = tflite::micro::GetTensorData<int8_t>(tensor);
-  PrintNBytes(tensor_data, n_bytes, prefix);
-}
-
-// same as the PrintNBytes above but the buffer needs to be extracted out of the
-// TfLiteEvalTensor*
-void PrintNBytes(const TfLiteTensor* tensor, int n_bytes, const char* prefix) {
-  const int8_t* tensor_data = tflite::GetTensorData<int8_t>(tensor);
-  PrintNBytes(tensor_data, n_bytes, prefix);
-}
-
-TfLiteStatus CopyOpInputsToSubgraphInputs(TfLiteContext* context,
-                                          TfLiteNode* node,
-                                          MicroGraph* graph_info,
-                                          int subgraph_idx,
-                                          int first_tensor_idx) {
-  TF_LITE_ENSURE(context,
-                 static_cast<size_t>(node->inputs->size - first_tensor_idx) ==
-                     graph_info->NumSubgraphInputs(subgraph_idx));
-  for (int i = 0; i < node->inputs->size - first_tensor_idx; i++) {
-    const TfLiteEvalTensor* input =
-        tflite::micro::GetEvalInput(context, node, i + first_tensor_idx);
-    TfLiteEvalTensor* subgraph_input =
-        graph_info->GetSubgraphInput(subgraph_idx, i);
-    int bytes = ValidateAndGetTensorSizes(input, subgraph_input);
-    TF_LITE_ENSURE(context, bytes >= 0);
-    memcpy(subgraph_input->data.raw, input->data.raw, bytes);
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus CopyOpOutputsToSubgraphInputs(TfLiteContext* context,
-                                           TfLiteNode* node,
-                                           MicroGraph* graph_info,
-                                           int subgraph_idx) {
-  TF_LITE_ENSURE(context, static_cast<size_t>(node->outputs->size) ==
-                              graph_info->NumSubgraphInputs(subgraph_idx));
-  for (int i = 0; i < node->outputs->size; i++) {
-    TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, i);
-    TfLiteEvalTensor* subgraph_input =
-        graph_info->GetSubgraphInput(subgraph_idx, i);
-    int bytes = ValidateAndGetTensorSizes(output, subgraph_input);
-    TF_LITE_ENSURE(context, bytes >= 0);
-    memcpy(subgraph_input->data.raw, output->data.raw, bytes);
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus CopySubgraphOutputsToOpOutputs(TfLiteContext* context,
-                                            TfLiteNode* node,
-                                            MicroGraph* graph_info,
-                                            int subgraph_idx) {
-  TF_LITE_ENSURE(context, static_cast<size_t>(node->outputs->size) ==
-                              graph_info->NumSubgraphOutputs(subgraph_idx));
-  for (int i = 0; i < node->outputs->size; i++) {
-    TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, i);
-    TfLiteEvalTensor* subgraph_output =
-        graph_info->GetSubgraphOutput(subgraph_idx, i);
-    int bytes = ValidateAndGetTensorSizes(output, subgraph_output);
-    TF_LITE_ENSURE(context, bytes >= 0);
-    memcpy(output->data.raw, subgraph_output->data.raw, bytes);
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace micro
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/kernel_util.h

@@ -1,138 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_
-#define TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_
-
-#include <cstdint>
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/micro/micro_context.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-
-namespace tflite {
-namespace micro {
-
-TfLiteRegistration RegisterOp(
-    void* (*init)(TfLiteContext* context, const char* buffer, size_t length),
-    TfLiteStatus (*prepare)(TfLiteContext* context, TfLiteNode* node),
-    TfLiteStatus (*invoke)(TfLiteContext* context, TfLiteNode* node),
-    void (*free)(TfLiteContext* context, void* buffer) = nullptr);
-
-// Prints out n bytes in a int8_t buffer as hex
-void PrintNBytes(const int8_t* tensor_data, int n_bytes,
-                 const char* prefix = nullptr);
-
-// Prints out the the n bytes in a TfLiteEvalTensor as hex
-void PrintNBytes(const TfLiteEvalTensor* tensor, int n_bytes,
-                 const char* prefix = nullptr);
-
-// Prints out the the n bytes in a TfLiteTensor as hex
-void PrintNBytes(const TfLiteTensor* tensor, int n_bytes,
-                 const char* prefix = nullptr);
-
-// Returns a mutable tensor for a given input index. is_variable must be checked
-// during prepare when the full TfLiteTensor is available.
-TfLiteEvalTensor* GetMutableEvalInput(const TfLiteContext* context,
-                                      const TfLiteNode* node, int index);
-
-// Returns the TfLiteEvalTensor struct for a given input index in a node.
-const TfLiteEvalTensor* GetEvalInput(const TfLiteContext* context,
-                                     const TfLiteNode* node, int index);
-
-// Returns the TfLiteEvalTensor struct for a given output index in a node.
-TfLiteEvalTensor* GetEvalOutput(const TfLiteContext* context,
-                                const TfLiteNode* node, int index);
-
-// Returns data for a TfLiteEvalTensor struct that are expected to exist.
-template <typename T>
-T* GetTensorData(TfLiteEvalTensor* tensor) {
-  TFLITE_DCHECK(tensor != nullptr);
-  return reinterpret_cast<T*>(tensor->data.raw);
-}
-
-// Returns const data for a TfLiteEvalTensor struct that are expected to exist.
-template <typename T>
-const T* GetTensorData(const TfLiteEvalTensor* tensor) {
-  TFLITE_DCHECK(tensor != nullptr);
-  return reinterpret_cast<const T*>(tensor->data.raw);
-}
-
-// Returns data for a TfLiteEvalTensor struct that could be null.
-template <typename T>
-T* GetOptionalTensorData(TfLiteEvalTensor* tensor) {
-  return tensor == nullptr ? nullptr : reinterpret_cast<T*>(tensor->data.raw);
-}
-
-// Returns const data for a TfLiteEvalTensor struct that could be null.
-template <typename T>
-const T* GetOptionalTensorData(const TfLiteEvalTensor* tensor) {
-  return tensor == nullptr ? nullptr
-                           : reinterpret_cast<const T*>(tensor->data.raw);
-}
-
-// Returns the shape of a TfLiteEvalTensor struct.
-const RuntimeShape GetTensorShape(const TfLiteEvalTensor* tensor);
-
-// Return true if the given tensors have the same shape.
-bool HaveSameShapes(const TfLiteEvalTensor* input1,
-                    const TfLiteEvalTensor* input2);
-
-PaddingType RuntimePaddingType(TfLitePadding padding);
-
-// Relocate tensor dims from FlatBuffer to the persistent storage arena.
-// The old dims data is copied to the new storage area.
-// The tensor and eval_tensor must be the same tensor.
-// Only use during Prepare phase.
-TfLiteStatus CreateWritableTensorDimsWithCopy(TfLiteContext* context,
-                                              TfLiteTensor* tensor,
-                                              TfLiteEvalTensor* eval_tensor);
-
-// Copy all op input tensors to op output tensors. Requires all op input tensor
-// shapes and types to be identical to op output tensor shapes and types.
-TfLiteStatus CopyOpInputsToOpOutputs(TfLiteContext* context, TfLiteNode* node);
-
-// Copy all op input tensors to subgraph input tensors. Requires all op input
-// tensor shapes and types to be identical to subgraph input tensor shapes and
-// types.
-TfLiteStatus CopyOpInputsToSubgraphInputs(TfLiteContext* context,
-                                          TfLiteNode* node,
-                                          MicroGraph* graph_info,
-                                          int subgraph_idx,
-                                          int first_tensor_idx);
-
-// Copy all op output tensors to subgraph input tensors. Requires all op output
-// tensor shapes and types to be identical to subgraph input tensor shapes and
-// types.
-TfLiteStatus CopyOpOutputsToSubgraphInputs(TfLiteContext* context,
-                                           TfLiteNode* node,
-                                           MicroGraph* graph_info,
-                                           int subgraph_idx);
-
-// Copy all subgraph output tensors to op outputs. Requires all subgraph output
-// tensor shapes and types to be identical to op output tensor shapes and types.
-TfLiteStatus CopySubgraphOutputsToOpOutputs(TfLiteContext* context,
-                                            TfLiteNode* node,
-                                            MicroGraph* graph_info,
-                                            int subgraph_idx);
-
-}  // namespace micro
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_MICRO_KERNELS_KERNEL_UTIL_H_

code/components/tflite-lib/tensorflow/lite/micro/kernels/l2_pool_2d.cc

@@ -1,141 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include <stddef.h>
-#include <stdint.h>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/reference/pooling.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/padding.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-// Input/output tensor index.
-constexpr int kInputTensor = 0;
-constexpr int kOutputTensor = 0;
-
-// required rank for input/output tensor shape
-constexpr int kTensorShapeRank = 4;
-
-// input/output tensor shape rank associations
-enum { kBatchRank = 0, kHeightRank, kWidthRank, kChannelRank };
-
-TfLiteStatus L2Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  auto* params = static_cast<TfLitePoolParams*>(node->builtin_data);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TF_LITE_ENSURE_EQ(context, NumDimensions(input), kTensorShapeRank);
-  TF_LITE_ENSURE_EQ(context, NumDimensions(output), kTensorShapeRank);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-
-  int batches = SizeOfDimension(input, kBatchRank);
-  int height = SizeOfDimension(input, kHeightRank);
-  int width = SizeOfDimension(input, kWidthRank);
-  int channels_out = SizeOfDimension(input, kChannelRank);
-
-  // Matching GetWindowedOutputSize in TensorFlow.
-  auto padding = params->padding;
-  int out_width, out_height;
-
-  params->computed.padding = ComputePaddingHeightWidth(
-      params->stride_height, params->stride_width, 1, 1, height, width,
-      params->filter_height, params->filter_width, padding, &out_height,
-      &out_width);
-
-  // We currently don't have a quantized implementation of L2Pool
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteFloat32);
-
-  // We must update the output tensor dimensions.
-  // The dims storage is expected to be the same area in memory
-  // for both TfLiteTensor and TfLiteEvalTensor.  This is important
-  // because TfLiteTensor in the MicroInterpreter is a temporary
-  // allocation.  For the KernelRunner interpreter, TfLiteEvalTensor
-  // is a temporary allocation.  We must therefore relocate the dims
-  // from the FlatBuffer to the persistant storage arena.
-  TfLiteEvalTensor* output_eval =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  TF_LITE_ENSURE_OK(context, tflite::micro::CreateWritableTensorDimsWithCopy(
-                                 context, output, output_eval));
-  output->dims->data[kBatchRank] = batches;
-  output->dims->data[kHeightRank] = out_height;
-  output->dims->data[kWidthRank] = out_width;
-  output->dims->data[kChannelRank] = channels_out;
-
-  micro_context->DeallocateTempTfLiteTensor(output);
-  micro_context->DeallocateTempTfLiteTensor(input);
-
-  return kTfLiteOk;
-}
-
-void L2EvalFloat(const TfLitePoolParams& params, const TfLiteEvalTensor& input,
-                 tflite::PoolParams* op_params, TfLiteEvalTensor* output) {
-  float activation_min, activation_max;
-  CalculateActivationRange(params.activation, &activation_min, &activation_max);
-
-  op_params->float_activation_min = activation_min;
-  op_params->float_activation_max = activation_max;
-  reference_ops::L2Pool(*op_params, tflite::micro::GetTensorShape(&input),
-                        tflite::micro::GetTensorData<float>(&input),
-                        tflite::micro::GetTensorShape(output),
-                        tflite::micro::GetTensorData<float>(output));
-}
-
-TfLiteStatus L2Eval(TfLiteContext* context, TfLiteNode* node) {
-  auto* params = static_cast<const TfLitePoolParams*>(node->builtin_data);
-
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-
-  tflite::PoolParams op_params;
-  op_params.stride_height = params->stride_height;
-  op_params.stride_width = params->stride_width;
-  op_params.filter_height = params->filter_height;
-  op_params.filter_width = params->filter_width;
-  op_params.padding_values.height = params->computed.padding.height;
-  op_params.padding_values.width = params->computed.padding.width;
-
-  switch (input->type) {  // Already know in/out types are same.
-    case kTfLiteFloat32:
-      L2EvalFloat(*params, *input, &op_params, output);
-      break;
-    default:
-      MicroPrintf("L2_POOL_2D only supports float32 currently, got %s.",
-                  TfLiteTypeGetName(input->type));
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_L2_POOL_2D() {
-  return tflite::micro::RegisterOp(nullptr, L2Prepare, L2Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/l2norm.cc

@@ -1,147 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/portable_tensor.h"
-#include "tensorflow/lite/kernels/internal/reference/integer_ops/l2normalization.h"
-#include "tensorflow/lite/kernels/internal/reference/l2normalization.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace l2norm {
-
-namespace {
-
-// This file has two implementation of L2Norm.
-enum KernelType {
-  kReference,
-  kGenericOptimized,
-};
-
-constexpr int kInputTensor = 0;
-constexpr int kOutputTensor = 0;
-
-}  // namespace
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-
-  auto* params = reinterpret_cast<TfLiteL2NormParams*>(node->builtin_data);
-  L2NormalizationParams* data =
-      static_cast<L2NormalizationParams*>(node->user_data);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-  TF_LITE_ENSURE(context, NumDimensions(input) <= 4);
-
-  TF_LITE_ENSURE(context,
-                 output->type == kTfLiteFloat32 || output->type == kTfLiteInt8);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-
-  if (output->type == kTfLiteInt8) {
-    data->input_zero_point = input->params.zero_point;
-  } else if (output->type == kTfLiteFloat32) {
-    data->input_zero_point = 0;
-  }
-
-  // Our implementations don't currently support activations.
-  TF_LITE_ENSURE_EQ(context, params->activation, kTfLiteActNone);
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context,
-                                           sizeof(L2NormalizationParams));
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const L2NormalizationParams& data =
-      *(static_cast<const L2NormalizationParams*>(node->user_data));
-
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  // TODO(b/143912164): instead of hardcode the epsilon here, we should read it
-  // from tensorflow, i.e., adding a params.
-  // We don't compute epsilon for quantized kernel:
-  //
-  // epsilon_float = (epsilon_quant - zp) * scale
-  // so
-  // espsilon_quant = epsilon_float / scale + zp
-  // We know epsilon_float is just a very small number to avoid division by
-  // zero error, and scale is > 1, so the integer value of epsilon for quant
-  // is just dominated by the zero point.
-  // Also, GetInvSqrtQuantizedMultiplierExp handles the scenario where the sum
-  // of input value squared is zero case well.
-  // So we don't even need to do handle the epsilon for quantized kernel case.
-  const float epsilon = 1e-6f;
-  if (output->type == kTfLiteFloat32) {
-    reference_ops::L2Normalization(data, tflite::micro::GetTensorShape(input),
-                                   tflite::micro::GetTensorData<float>(input),
-                                   tflite::micro::GetTensorShape(output),
-                                   tflite::micro::GetTensorData<float>(output),
-                                   epsilon);
-  } else if (output->type == kTfLiteInt8) {
-    const auto input_shape = tflite::micro::GetTensorShape(input);
-    const auto output_shape = tflite::micro::GetTensorShape(output);
-    const int trailing_dim = input_shape.DimensionsCount() - 1;
-    const int depth =
-        MatchingDim(input_shape, trailing_dim, output_shape, trailing_dim);
-    const int outer_size =
-        MatchingFlatSizeSkipDim(input_shape, trailing_dim, output_shape);
-    reference_integer_ops::L2Normalization(
-        data.input_zero_point, outer_size, depth,
-        tflite::micro::GetTensorData<int8_t>(input),
-        tflite::micro::GetTensorData<int8_t>(output));
-  } else {
-    MicroPrintf("Output type is %s, requires float.",
-                TfLiteTypeGetName(output->type));
-    return kTfLiteError;
-  }
-
-  return kTfLiteOk;
-}
-
-}  // namespace l2norm
-
-TfLiteRegistration Register_L2NORM_REF() {
-  return tflite::micro::RegisterOp(l2norm::Init, l2norm::Prepare, l2norm::Eval);
-}
-
-TfLiteRegistration Register_L2_NORMALIZATION() { return Register_L2NORM_REF(); }
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/log_softmax.cc

@@ -1,147 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/kernels/internal/reference/log_softmax.h"
-
-#include <cstddef>
-#include <cstdint>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-// used only with quantized data
-struct LogSoftmaxOpData {
-  int32_t input_multiplier;
-  int32_t input_left_shift;
-  int32_t reverse_scaling_divisor;
-  int32_t reverse_scaling_right_shift;
-  int diff_min;
-  size_t outer_size;  // number of tensor elements skipping computation axis
-  size_t depth;       // number of tensor elements on computation axis
-};
-
-// input/output tensor index
-constexpr int kInputTensor = 0;
-constexpr int kOutputTensor = 0;
-
-TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-
-  TF_LITE_ENSURE(context, HaveSameShapes(input, output));
-
-  if (input->type == kTfLiteInt8) {
-    node->user_data =
-        context->AllocatePersistentBuffer(context, sizeof(LogSoftmaxOpData));
-    auto data = static_cast<LogSoftmaxOpData*>(node->user_data);
-
-    // quantization datum
-    constexpr int32_t kOutputZeroPoint = 127;
-    constexpr float kOutputScale = 16.0 / 256;
-    constexpr double kBeta = 1.0;
-    constexpr int kScaledDiffIntegerBits = 5;
-
-    TF_LITE_ENSURE(context, output->params.scale == kOutputScale);
-    TF_LITE_ENSURE(context, output->params.zero_point == kOutputZeroPoint);
-
-    int input_left_shift;
-    int reverse_scaling_right_shift;
-    tflite::PreprocessLogSoftmaxScalingExp(
-        kBeta, static_cast<double>(input->params.scale), kScaledDiffIntegerBits,
-        &data->input_multiplier, &input_left_shift,
-        &data->reverse_scaling_divisor, &reverse_scaling_right_shift);
-    data->input_left_shift = static_cast<int32_t>(input_left_shift);
-    data->reverse_scaling_right_shift =
-        static_cast<int32_t>(-reverse_scaling_right_shift);
-    // diff_min has a negative value, and is used to limit the maximum magnitude
-    // of the diffs, which are <= 0.
-    data->diff_min =
-        -tflite::CalculateInputRadius(kScaledDiffIntegerBits, input_left_shift);
-
-    RuntimeShape input_shape = GetTensorShape(input);
-    const int trailing_dim = input_shape.DimensionsCount() - 1;
-    data->outer_size =
-        static_cast<size_t>(FlatSizeSkipDim(input_shape, trailing_dim));
-    data->depth = static_cast<size_t>(input_shape.Dims(trailing_dim));
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-TfLiteStatus LogSoftmaxPrepare(TfLiteContext* context, TfLiteNode* node) {
-  return CalculateOpData(context, node);
-}
-
-TfLiteStatus LogSoftmaxEval(TfLiteContext* context, TfLiteNode* node) {
-  const LogSoftmaxOpData* data =
-      static_cast<LogSoftmaxOpData*>(node->user_data);
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      SoftmaxParams op_params = {};
-      reference_ops::LogSoftmax(op_params, tflite::micro::GetTensorShape(input),
-                                tflite::micro::GetTensorData<float>(input),
-                                tflite::micro::GetTensorShape(output),
-                                tflite::micro::GetTensorData<float>(output));
-      return kTfLiteOk;
-    }
-    case kTfLiteInt8: {
-      SoftmaxParams op_params = {};
-      op_params.input_multiplier = data->input_multiplier;
-      op_params.input_left_shift = data->input_left_shift;
-      op_params.reverse_scaling_divisor = data->reverse_scaling_divisor;
-      op_params.reverse_scaling_right_shift = data->reverse_scaling_right_shift;
-      op_params.diff_min = data->diff_min;
-      reference_ops::LogSoftmax(op_params, data->outer_size, data->depth,
-                                tflite::micro::GetTensorShape(input),
-                                tflite::micro::GetTensorData<int8_t>(input),
-                                tflite::micro::GetTensorShape(output),
-                                tflite::micro::GetTensorData<int8_t>(output));
-      return kTfLiteOk;
-    }
-    default:
-      MicroPrintf("LOG_SOFTMAX only supports float32, int8, got %s.",
-                  TfLiteTypeGetName(input->type));
-      return kTfLiteError;
-  }
-}
-
-}  // namespace
-
-TfLiteRegistration Register_LOG_SOFTMAX() {
-  return tflite::micro::RegisterOp(nullptr, LogSoftmaxPrepare, LogSoftmaxEval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/lstm_eval.cc

@@ -1,2981 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/micro/kernels/lstm_eval.h"
-
-#include <cmath>
-#include <cstdint>
-#include <cstring>
-#include <memory>
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/kernels/internal/reference/integer_ops/logistic.h"
-#include "tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/micro_tensor_utils.h"
-namespace tflite {
-namespace {
-
-void ComputeRowSums(
-    int32_t* input_to_input_row_sums, int32_t* input_to_forget_row_sums,
-    int32_t* input_to_cell_row_sums, int32_t* input_to_output_row_sums,
-    int32_t* aux_input_to_input_row_sums, int32_t* aux_input_to_forget_row_sums,
-    int32_t* aux_input_to_cell_row_sums, int32_t* aux_input_to_output_row_sums,
-    int32_t* recurrent_to_input_row_sums, int32_t* recurrent_to_forget_row_sums,
-    int32_t* recurrent_to_cell_row_sums, int32_t* recurrent_to_output_row_sums,
-    int32_t* projection_weights_row_sums, int32_t* row_sums, int n_cell,
-    int n_input, int n_aux_input, int n_output,
-    const int8_t* input_to_input_weights_ptr,
-    const int8_t* input_to_forget_weights_ptr,
-    const int8_t* input_to_cell_weights_ptr,
-    const int8_t* input_to_output_weights_ptr,
-    const int8_t* aux_input_to_input_weights_ptr,
-    const int8_t* aux_input_to_forget_weights_ptr,
-    const int8_t* aux_input_to_cell_weights_ptr,
-    const int8_t* aux_input_to_output_weights_ptr,
-    const int8_t* recurrent_to_input_weights_ptr,
-    const int8_t* recurrent_to_forget_weights_ptr,
-    const int8_t* recurrent_to_cell_weights_ptr,
-    const int8_t* recurrent_to_output_weights_ptr,
-    const int8_t* projection_weights_ptr, bool use_cifg,
-    const float* aux_input_ptr) {
-  // Compute the row sums for dequantization
-  if (!use_cifg) {
-    micro_tensor_utils::ReductionSumVector(
-        input_to_input_weights_ptr, input_to_input_row_sums, n_cell, n_input);
-  }
-  micro_tensor_utils::ReductionSumVector(
-      input_to_forget_weights_ptr, input_to_forget_row_sums, n_cell, n_input);
-  micro_tensor_utils::ReductionSumVector(
-      input_to_cell_weights_ptr, input_to_cell_row_sums, n_cell, n_input);
-  micro_tensor_utils::ReductionSumVector(
-      input_to_output_weights_ptr, input_to_output_row_sums, n_cell, n_input);
-
-  if (aux_input_ptr) {
-    if (!use_cifg) {
-      micro_tensor_utils::ReductionSumVector(aux_input_to_input_weights_ptr,
-                                             aux_input_to_input_row_sums,
-                                             n_cell, n_aux_input);
-    }
-    micro_tensor_utils::ReductionSumVector(aux_input_to_forget_weights_ptr,
-                                           aux_input_to_forget_row_sums, n_cell,
-                                           n_aux_input);
-    micro_tensor_utils::ReductionSumVector(aux_input_to_cell_weights_ptr,
-                                           aux_input_to_cell_row_sums, n_cell,
-                                           n_aux_input);
-    micro_tensor_utils::ReductionSumVector(aux_input_to_output_weights_ptr,
-                                           aux_input_to_output_row_sums, n_cell,
-                                           n_aux_input);
-  }
-  if (!use_cifg) {
-    micro_tensor_utils::ReductionSumVector(recurrent_to_input_weights_ptr,
-                                           recurrent_to_input_row_sums, n_cell,
-                                           n_output);
-  }
-  micro_tensor_utils::ReductionSumVector(recurrent_to_forget_weights_ptr,
-                                         recurrent_to_forget_row_sums, n_cell,
-                                         n_output);
-  micro_tensor_utils::ReductionSumVector(recurrent_to_cell_weights_ptr,
-                                         recurrent_to_cell_row_sums, n_cell,
-                                         n_output);
-  micro_tensor_utils::ReductionSumVector(recurrent_to_output_weights_ptr,
-                                         recurrent_to_output_row_sums, n_cell,
-                                         n_output);
-
-  if (projection_weights_ptr != nullptr) {
-    micro_tensor_utils::ReductionSumVector(
-        projection_weights_ptr, projection_weights_row_sums, n_output, n_cell);
-  }
-}
-
-// Calculates a single LSTM gate.
-//
-// Implements the following formula: (* is matrix multiply)
-//   gate = activate(W_input    * input + W_aux       * aux_input   +
-//                   W_peephole * cell  + W_recurrent * prev_output + bias)
-// with layer norm:
-//   gate = activate(W_norm * normalize(...) + bias) // not adding bias inside
-//
-// Activation is sigmoid except for the "cell" gate (configurable, usually tanh)
-//
-// Parameters:
-// Input vectors (to LSTM):    | Size:                | Optional?
-//   input                     | n_input              |
-//   aux_input                 | n_aux_input          | y (bidir LSTM)
-// Input vectors (persistent states):
-//   output_state              | n_output             |
-//   cell_state                | n_cell               |
-// 'Constant' inputs:
-//   input_to_gate_weights     | n_cell * n_input     |
-//   aux_input_to_gate_weights | n_cell * n_aux_input | y (bidir LSTM)
-//   recurrent_to_gate_weights | n_cell * n_output    |
-//   cell_to_gate_weights      | n_cell               | y (peephole)
-//   gate_bias                 | n_cell               |
-//   layer_norm_coefficients   | n_cell               | y (layer norm)
-// Output vector:
-//   gate                      | n_cell               |
-// Scalar parameters:
-//   n_batch                                    - batch size / number of vectors
-//   n_input, n_aux_input, n_output, n_cell     - size of vectors.
-//   activation                                 - activation to use.
-//   is_input_all_zeros, is_aux_input_all_zeros - if input vectors are all zero.
-//   use_layer_norm                             - if doing layer norm LSTM.
-inline void CalculateLstmGateFloat(
-    const float* input, const float* input_to_gate_weights,
-    const float* aux_input, const float* aux_input_to_gate_weights,
-    const float* output_state, const float* recurrent_to_gate_weights,
-    const float* cell_state, const float* cell_to_gate_weights,
-    const float* layer_norm_coefficients, const float* gate_bias,
-    const int n_batch, const int n_input, const int n_aux_input,
-    const int n_output, const int n_cell,
-    const TfLiteFusedActivation activation, float* gate,
-    const bool is_input_all_zeros, const bool is_aux_input_all_zeros) {
-  const bool use_peephole = (cell_to_gate_weights != nullptr);
-  const bool use_layer_norm = (layer_norm_coefficients != nullptr);
-
-  // Initialize scratch buffers with bias for regular lstm or initialize with
-  // zero for layer norm lstm.
-  if (use_layer_norm) {
-    memset(gate, 0, n_cell * n_batch * sizeof(float));
-  } else {
-    micro_tensor_utils::VectorBatchVectorAssign(gate_bias, n_cell, n_batch,
-                                                gate);
-  }
-  // For each batch and cell: compute input_weight * input.
-  // Skip if input is all zeros.
-  if (!is_input_all_zeros) {
-    micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        input_to_gate_weights, n_cell, n_input, input, n_batch, gate);
-  }
-  // For each batch and cell: compute aux_input_weight * aux_input.
-  // Skip if auxiliary input is not available or all zeros.
-  if (!is_aux_input_all_zeros) {
-    micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        aux_input_to_gate_weights, n_cell, n_aux_input, aux_input, n_batch,
-        gate);
-  }
-  // For each batch and cell: compute recurrent_weight * output_state.
-  micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      recurrent_to_gate_weights, n_cell, n_output, output_state, n_batch, gate);
-  // For each batch and cell: compute cell_weight .* cell_state (peephole LSTM)
-  if (use_peephole) {
-    micro_tensor_utils::VectorBatchVectorCwiseProductAccumulate(
-        cell_to_gate_weights, n_cell, cell_state, n_batch, gate);
-  }
-  // Do layer normalization (if layer norm LSTM)
-  if (use_layer_norm) {
-    micro_tensor_utils::MeanStddevNormalization(gate, gate, n_cell, n_batch);
-    micro_tensor_utils::VectorBatchVectorCwiseProduct(
-        layer_norm_coefficients, n_cell, gate, n_batch, gate);
-    micro_tensor_utils::VectorBatchVectorAdd(gate_bias, n_cell, n_batch, gate);
-  }
-  // Apply activation
-  micro_tensor_utils::ApplyActivationToVector(gate, n_batch * n_cell,
-                                              activation, gate);
-}
-
-// Updates the LSTM cell state, used by both float and hybrid LSTM versions.
-//
-// Implements the following formula:
-//   cell_state_new = clip(forget_gate * cell_state + input_gate * cell_gate)
-//
-// With CIFG LSTM, input gate is replaced by (1-forget_gate).
-//
-// Parameters:
-//  - n_batch, n_cell: sizes of vectors
-//  - cell_state: input/output vector, size n_batch*n_cell
-//  - input_gate: input vector, size n_batch*n_cell.
-//  - forget_gate: input/scratch vector, size n_batch*n_cell, modified with CIFG
-//  - cell_gate: input vector, size n_batch*n_cell.
-//  - use_cifg: use 1-forget_gate instead of input_gate.
-//  - clip: if > 0, clip the resulting cell state to [-clip, +clip].
-void UpdateLstmCellFloat(int n_batch, int n_cell, float* cell_state,
-                         const float* input_gate, float* forget_gate,
-                         const float* cell_gate, bool use_cifg, float clip) {
-  micro_tensor_utils::VectorVectorCwiseProduct(forget_gate, cell_state,
-                                               n_batch * n_cell, cell_state);
-
-  if (use_cifg) {
-    // With CIFG, input_gate = 1-forget_gate. Use the forget_gate array as
-    // scratch, as input_gate array is not allocated in this case. (Be careful
-    // not to write to the scratch before reading the forget gate data.)
-    float* scratch = forget_gate;
-    micro_tensor_utils::Sub1Vector(forget_gate, n_batch * n_cell, scratch);
-    micro_tensor_utils::VectorVectorCwiseProductAccumulate(
-        cell_gate, scratch, n_batch * n_cell, cell_state);
-  } else {
-    micro_tensor_utils::VectorVectorCwiseProductAccumulate(
-        cell_gate, input_gate, n_batch * n_cell, cell_state);
-  }
-  if (clip > 0.0f) {
-    micro_tensor_utils::CwiseClipping(cell_state, n_batch * n_cell, clip);
-  }
-}
-
-// Calculates the output state tensor of an LSTM step.
-//
-// Implements the following formula:
-//   output_no_projection = output_gate .* activate(cell_state)
-//     (elementwise vector product)
-// If no projection is used:
-//   output = output_state = output_no_projection
-// With projection:
-//   output = output_state = clip(W*output_no_projection + bias)
-//
-// Output might not have a different 'stride' than n_batch, so we need to copy.
-//
-// Parameters:
-//  - n_batch: batches: the number of distinct vectors in each array.
-//  - n_cell, n_output: sizes of vectors.
-//  - cell_state, output_gate: input vectors, size n_batch*n_cell.
-//  - projection_weights, projection_weights_scale, projection_bias:
-//      constant inputs, describing projection matrix and bias.
-//  - proj_clip: if > 0, clip the output of the projection.
-//  - output_state: output vector, size n_batch*n_output. Must be contigous.
-//  - scratch: scratch area, size n_batch*n_cell.
-void CalculateLstmOutputFloat(int n_batch, int n_cell, int n_output,
-                              const float* cell_state, const float* output_gate,
-                              TfLiteFusedActivation activation,
-                              const float* projection_weights,
-                              const float* projection_bias,
-                              const float proj_clip, float* output_state,
-                              float* scratch) {
-  micro_tensor_utils::ApplyActivationToVector(cell_state, n_batch * n_cell,
-                                              activation, scratch);
-  micro_tensor_utils::VectorVectorCwiseProduct(output_gate, scratch,
-                                               n_batch * n_cell, scratch);
-
-  const bool use_projection = (projection_weights != nullptr);
-  const bool use_projection_bias = (projection_bias != nullptr);
-
-  if (use_projection) {
-    if (use_projection_bias) {
-      micro_tensor_utils::VectorBatchVectorAssign(projection_bias, n_output,
-                                                  n_batch, output_state);
-    } else {
-      memset(output_state, 0, n_batch * n_output * sizeof(float));
-    }
-    micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        projection_weights, n_output, n_cell, scratch, n_batch, output_state);
-    if (proj_clip > 0.0f) {
-      micro_tensor_utils::CwiseClipping(output_state, n_batch * n_output,
-                                        proj_clip);
-    }
-  } else {
-    std::memcpy(output_state, scratch, n_batch * n_output * sizeof(float));
-  }
-}
-
-// Calculates a single LSTM gate, hybrid version.
-// Implements the same functionality as CalculateLstmGateFloat.
-void CalculateLstmGateHybrid(
-    // Input and weights
-    const int8_t* input, const float* input_sf, const int32_t* input_zp,
-    const int8_t* input_to_gate_weights,
-    const uint8_t* input_to_gate_weights_ledger,
-    const float input_to_gate_weights_scale, int32_t* input_to_gate_row_sums,
-    // Aux input and weights
-    const int8_t* aux_input, const float* aux_input_sf,
-    const int32_t* aux_input_zp, const int8_t* aux_input_to_gate_weights,
-    const float aux_input_to_gate_weights_scale,
-    int32_t* aux_input_to_gate_row_sums,
-    // Output state and weights
-    const int8_t* output_state, const float* output_state_sf,
-    const int32_t* output_state_zp, const int8_t* recurrent_to_gate_weights,
-    const uint8_t* recurrent_to_gate_weights_ledger,
-    const float recurrent_to_gate_weights_scale,
-    int32_t* recurrent_to_gate_row_sums,
-    // Cell state and weights (peephole LSTM)
-    const float* cell_state, const int8_t* cell_to_gate_weights,
-    const float cell_to_gate_weights_scale,
-    // Layer normalization coefficients (layer norm LSTM) + gate bias
-    const float* layer_norm_coefficients, const float* gate_bias,
-    // Array sizes
-    const int n_batch, const int n_input, const int n_aux_input,
-    const int n_output, const int n_cell,
-    const TfLiteFusedActivation activation,
-    // Output
-    float* gate,
-    // Parameters for performance optimizations
-    const bool is_input_all_zeros, const bool is_aux_input_all_zeros,
-    const bool is_output_state_all_zeros, bool* compute_row_sums,
-    // Scratch arrays
-    float* scratch0,        // size: n_batch
-    float* scratch1,        // size: n_cell, only used if peephole LSTM
-    float* scales,          // size: n_batch
-    int32_t* accum_scratch  // For MatrixBatchVectorMultiplyAccumulate
-) {
-  const bool use_peephole = (cell_to_gate_weights != nullptr);
-  const bool use_layer_norm = (layer_norm_coefficients != nullptr);
-
-  // Initialize scratch buffers with bias for regular lstm or initialize with
-  // zero for layer norm lstm.
-  if (use_layer_norm) {
-    memset(gate, 0, n_cell * n_batch * sizeof(float));
-  } else {
-    micro_tensor_utils::VectorBatchVectorAssign(gate_bias, n_cell, n_batch,
-                                                gate);
-  }
-  // For each batch and cell: compute input_weight * input.
-  // Skip if input is all zeros.
-  if (!is_input_all_zeros) {
-    if (input_to_gate_weights_ledger != nullptr) {
-      for (int i = 0; i < n_batch; i++) {
-        scales[i] = input_to_gate_weights_scale * input_sf[i];
-      }
-      micro_tensor_utils::SparseMatrixBatchVectorMultiplyAccumulate(
-          input_to_gate_weights, input_to_gate_weights_ledger, n_cell, n_input,
-          input, scales, n_batch, gate);
-
-    } else {
-      micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-          input_to_gate_weights, n_cell, n_input, input,
-          input_to_gate_weights_scale, input_sf, n_batch, gate,
-          /*per_channel_scale=*/nullptr, input_zp, accum_scratch,
-          input_to_gate_row_sums, compute_row_sums, scratch0, nullptr);
-    }
-  }
-  // For each batch and cell: compute aux_input_weight * aux_input.
-  // Skip if auxiliary input is not available or all zeros.
-  if (!is_aux_input_all_zeros) {
-    micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        aux_input_to_gate_weights, n_cell, n_aux_input, aux_input,
-        aux_input_to_gate_weights_scale, aux_input_sf, n_batch, gate,
-        /*per_channel_scale=*/nullptr, aux_input_zp, accum_scratch,
-        aux_input_to_gate_row_sums, compute_row_sums, scratch0, nullptr);
-  }
-  // For each batch and cell: compute recurrent_weight * output_state.
-  // Skip if output state is all zeros.
-  if (!is_output_state_all_zeros) {
-    if (recurrent_to_gate_weights_ledger != nullptr) {
-      for (int i = 0; i < n_batch; i++) {
-        scales[i] = recurrent_to_gate_weights_scale * input_sf[i];
-      }
-      micro_tensor_utils::SparseMatrixBatchVectorMultiplyAccumulate(
-          recurrent_to_gate_weights, recurrent_to_gate_weights_ledger, n_cell,
-          n_output, output_state, scales, n_batch, gate);
-    } else {
-      micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-          recurrent_to_gate_weights, n_cell, n_output, output_state,
-          recurrent_to_gate_weights_scale, output_state_sf, n_batch, gate,
-          /*per_channel_scale=*/nullptr, output_state_zp, accum_scratch,
-          recurrent_to_gate_row_sums, compute_row_sums, scratch0, nullptr);
-    }
-  }
-  // For each batch and cell: compute cell_weight .* cell_state (peephole LSTM)
-  if (use_peephole) {
-    float* recovered_cell_weights = scratch1;
-    micro_tensor_utils::VectorScalarMultiply(cell_to_gate_weights, n_cell,
-                                             cell_to_gate_weights_scale,
-                                             recovered_cell_weights);
-    micro_tensor_utils::VectorBatchVectorCwiseProductAccumulate(
-        recovered_cell_weights, n_cell, cell_state, n_batch, gate);
-  }
-  // Do layer normalization (if layer norm LSTM)
-  if (use_layer_norm) {
-    micro_tensor_utils::MeanStddevNormalization(gate, gate, n_cell, n_batch);
-    micro_tensor_utils::VectorBatchVectorCwiseProduct(
-        layer_norm_coefficients, n_cell, gate, n_batch, gate);
-    micro_tensor_utils::VectorBatchVectorAdd(gate_bias, n_cell, n_batch, gate);
-  }
-  // Apply activation
-  micro_tensor_utils::ApplyActivationToVector(gate, n_cell * n_batch,
-                                              activation, gate);
-}
-
-// Calculates the output state tensor of an LSTM step. See Float version too.
-//
-// Parameters:
-//  - n_batch: batches: the number of distinct vectors in each array.
-//  - n_cell, n_output: sizes of vectors.
-//  - cell_state, output_gate: input vectors, size n_batch*n_cell.
-//  - projection_weights, projection_weights_scale, projection_bias:
-//      constant inputs, describing projection matrix and bias.
-//  - proj_clip: if > 0, clip the output of the projection.
-//  - output_state: output vector, size n_batch*n_output. Must be contigous.
-//  - asymmetric_quantize_inputs: parameter to control quantization.
-//  - projection_weights_row_sums, compute_row_sums: Data for optimized
-//      MatrixBatchVectorMultiplyAccumulate.
-//  - scratch0: scratch area of size n_batch*n_cell
-//  - scratch1: scratch area of size n_batch*n_cell
-//  - scratch2: scratch area of size n_batch
-//  - scratch3: scratch area of size n_batch
-//  - scratch4: scratch area used by MatrixBatchVectorMultiplyAccumulate
-//  - scales: scratch area of size n_batch
-void CalculateLstmOutputHybrid(
-    int n_batch, int n_cell, int n_output, const float* cell_state,
-    const float* output_gate, TfLiteFusedActivation activation,
-    const int8_t* projection_weights, const uint8_t* projection_weights_ledger,
-    float projection_weights_scale, const float* projection_bias,
-    const float proj_clip, float* output_state, bool asymmetric_quantize_inputs,
-    int32_t* projection_weights_row_sums, bool* compute_row_sums,
-    float* scratch0, int8_t* scratch1, float* scratch2, int32_t* scratch3,
-    int32_t* scratch4, float* scales) {
-  micro_tensor_utils::ApplyActivationToVector(cell_state, n_batch * n_cell,
-                                              activation, scratch0);
-  micro_tensor_utils::VectorVectorCwiseProduct(output_gate, scratch0,
-                                               n_batch * n_cell, scratch0);
-
-  const bool use_projection = (projection_weights != nullptr);
-  const bool use_projection_bias = (projection_bias != nullptr);
-
-  if (use_projection) {
-    if (use_projection_bias) {
-      micro_tensor_utils::VectorBatchVectorAssign(projection_bias, n_output,
-                                                  n_batch, output_state);
-    } else {
-      memset(output_state, 0, n_batch * n_output * sizeof(float));
-    }
-    if (!micro_tensor_utils::IsZeroVector(scratch0, n_batch * n_cell)) {
-      // Save quantization and matmul computation for all zero output.
-      micro_tensor_utils::BatchQuantizeFloats(scratch0, n_batch, n_cell,
-                                              scratch1, scratch2, scratch3,
-                                              asymmetric_quantize_inputs);
-      if (projection_weights_ledger != nullptr) {
-        for (int i = 0; i < n_batch; i++) {
-          scales[i] = projection_weights_scale * scratch2[i];
-        }
-        micro_tensor_utils::SparseMatrixBatchVectorMultiplyAccumulate(
-            projection_weights, projection_weights_ledger, n_output, n_cell,
-            scratch1, scales, n_batch, output_state);
-      } else {
-        micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-            projection_weights, n_output, n_cell, scratch1,
-            projection_weights_scale, scratch2, n_batch, output_state,
-            /*per_channel_scale=*/nullptr, scratch3, scratch4,
-            projection_weights_row_sums, compute_row_sums, scratch2, nullptr);
-      }
-    }
-    if (proj_clip > 0.0f) {
-      micro_tensor_utils::CwiseClipping(output_state, n_batch * n_output,
-                                        proj_clip);
-    }
-  } else {
-    std::memcpy(output_state, scratch0, n_batch * n_output * sizeof(float));
-  }
-}
-
-// Calculates a single LSTM gate, int8x8_16 version.
-// Implements the same functionality as CalculateLstmGateFloat.
-void CalculateLstmGateInteger8x8_16(
-    // Input and weights
-    const int8_t* input, const int8_t* input_to_gate_weights,
-    const int32_t* input_to_gate_bias, const int32_t input_to_gate_scale_a,
-    const int32_t input_to_gate_scale_b,
-    // Output state and weights
-    const int8_t* output_state, const int8_t* recurrent_to_gate_weights,
-    const int32_t* recurrent_to_gate_bias,
-    const int32_t recurrent_to_gate_scale_a,
-    const int32_t recurrent_to_gate_scale_b,
-    // Cell state and weights
-    const int16_t* cell_state, const int16_t* cell_to_gate_weights,
-    const int32_t cell_to_gate_scale_a, const int32_t cell_to_gate_scale_b,
-    // Layer normalization parameters (layer norm LSTM)
-    const int16_t* layer_norm_coefficients, const int32_t* layer_norm_bias,
-    const int32_t layer_norm_input_scale_a,
-    const int32_t layer_norm_input_scale_b,
-    const int32_t layer_norm_variance_guard,
-    // Array sizes
-    const int n_batch, const int n_input, const int n_output, const int n_cell,
-    const TfLiteFusedActivation activation,
-    // Output
-    int16_t* gate,
-    // Parameters for performance optimizations
-    // Scratch arrays
-    int32_t* scratch5) {
-  const bool use_peephole = (cell_to_gate_weights != nullptr);
-  const bool use_layer_norm = (layer_norm_coefficients != nullptr);
-
-  // Initialize scratch buffers with zeros. Note that unlike float and hybrid
-  // versions, bias is only used in layer normalization.
-  memset(gate, 0, n_batch * n_cell * sizeof(int16_t));
-  // For each batch and cell: compute input_weight * input.
-  micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      input, input_to_gate_bias, input_to_gate_weights, input_to_gate_scale_a,
-      input_to_gate_scale_b, n_batch, n_input, n_cell, 0, scratch5, gate,
-      nullptr);
-  // Note: no aux_input.
-
-  // For each batch and cell: compute recurrent_weight * output_state.
-  micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-      output_state, recurrent_to_gate_bias, recurrent_to_gate_weights,
-      recurrent_to_gate_scale_a, recurrent_to_gate_scale_b, n_batch, n_output,
-      n_cell, 0, scratch5, gate, nullptr);
-  // For each batch and cell: compute cell_weight * cell_state (peephole LSTM)
-  if (use_peephole) {
-    micro_tensor_utils::VectorBatchVectorCwiseProductAccumulate(
-        cell_to_gate_weights, n_output, cell_state, n_batch,
-        cell_to_gate_scale_a, cell_to_gate_scale_b, gate);
-  }
-  // Do layer normalization (if layer norm LSTM)
-  if (use_layer_norm) {
-    micro_tensor_utils::ApplyLayerNorm(
-        gate, layer_norm_coefficients, layer_norm_bias,
-        layer_norm_input_scale_a, layer_norm_input_scale_b,
-        layer_norm_variance_guard, n_batch, n_cell, gate);
-  }
-  // Apply activation
-  switch (activation) {
-    case kTfLiteActSigmoid:
-
-      reference_integer_ops::Logistic(
-          0 /*data->input_multiplier*/, 0 /*data->input_left_shift */,
-          n_batch * n_cell /*NumElements(input->dims)*/,
-          gate /* tflite::micro::GetTensorData<int16_t>(input) */,
-          gate /*tflite::micro::GetTensorData<int16_t>(output) */);
-
-      break;
-    case kTfLiteActTanh: {
-      int32_t dims_data = n_batch * n_cell;
-      RuntimeShape tanh_inp_shape = RuntimeShape(1, &dims_data);
-      reference_integer_ops::Tanh(0, 0, tanh_inp_shape, gate, tanh_inp_shape,
-                                  gate);
-    } break;
-    default:
-      // Only Sigmoid or Tanh is used.
-      TFLITE_ASSERT_FALSE;
-  }
-}
-
-// Updates the LSTM cell state, used by both integer LSTM versions.
-// Also see UpdateLstmCellFloat.
-//
-// Parameters:
-//  - n_batch, n_cell: sizes of vectors
-//  - cell_state: input/output vector, size n_batch*n_cell
-//  - cell_state_scale: scaling factor of cell state.
-//  - input_gate: input vector, size n_batch*n_cell.
-//  - forget_gate: input/scratch vector, size n_batch*n_cell, always modified.
-//  - cell_gate: input vector, size n_batch*n_cell.
-//  - use_cifg: use 1-forget_gate instead of input_gate.
-//  - clip: if > 0, clip the resulting cell state to [-clip, +clip].
-void UpdateLstmCellInteger(int n_batch, int n_cell, int16_t* cell_state,
-                           int32_t cell_state_scale, const int16_t* input_gate,
-                           int16_t* forget_gate, const int16_t* cell_gate,
-                           bool use_cifg, int16_t clip) {
-  // Use the forget_gate array as scratch, as input_gate array is not allocated
-  // in CIFG case. (Be careful not to write to the scratch before reading the
-  // forget gate data.)
-  int16_t* scratch = forget_gate;
-
-  micro_tensor_utils::CwiseMul(forget_gate, cell_state, n_batch, n_cell, 15,
-                               cell_state);
-  if (use_cifg) {
-    micro_tensor_utils::Sub1Vector(forget_gate, n_batch * n_cell, scratch);
-    micro_tensor_utils::CwiseMul(scratch, cell_gate, n_batch, n_cell,
-                                 30 + cell_state_scale, scratch);
-  } else {
-    micro_tensor_utils::CwiseMul(input_gate, cell_gate, n_batch, n_cell,
-                                 30 + cell_state_scale, scratch);
-  }
-  micro_tensor_utils::CwiseAdd(cell_state, scratch, n_batch, n_cell,
-                               cell_state);
-
-  if (clip > 0) {
-    micro_tensor_utils::CwiseClipping(cell_state, n_batch * n_cell, clip);
-  }
-}
-
-// Calculates the output state tensor of an LSTM step. See Float and hybrid
-// versions as well.
-//
-// Parameters:
-//  - n_batch: batches: the number of distinct vectors in each array.
-//  - n_cell, n_output: sizes of vectors.
-//  - cell_state, output_gate: input vectors, size n_batch*n_cell.
-//  - cell_state_scale: scaling of cell_state.
-//  - hidden_scale_[a|b]: effective scale of cell_state.*output_gate
-//  - hidden_zp: zero_point for cell_state.*output_gate
-//  - projection_weights, proj_scale_[a|b], projection_bias:
-//      constant inputs, describing projection matrix and bias.
-//  - output_state_zp: zero point of output_state. (Input, calibrated value.)
-//  - quantized_proj_clip: if > 0, clip the output of the projection.
-//  - output_state: output vector, size n_batch*n_output. Must be contigous.
-//  - scratch0: scratch area of size n_batch*n_cell
-//  - scratch1: scratch area of size n_batch*n_cell
-//  - scratch2: scratch area used by MatrixBatchVectorMultiplyAccumulate
-void CalculateLstmOutputInteger8x8_16(
-    int n_batch, int n_cell, int n_output, int16_t* cell_state,
-    int32_t cell_state_scale, const int16_t* output_gate,
-    int32_t hidden_scale_a, int32_t hidden_scale_b, int32_t hidden_zp,
-    const int8_t* projection_weights, int32_t proj_scale_a,
-    int32_t proj_scale_b, const int32_t* projection_bias,
-    int32_t output_state_zp, int8_t quantized_proj_clip, int8_t* output_state,
-    int16_t* scratch0, int8_t* scratch1, int32_t* scratch2) {
-  // Note: unlike float/hybrid, the activation is always Tanh.
-
-  {
-    int32_t tanh_input_left_shift = (15 + cell_state_scale) - 3;
-    int32_t dims_data = n_batch * n_cell;
-    if (tanh_input_left_shift < 0) /* handling negative shift value */
-    {
-      int32_t i;
-      tanh_input_left_shift = -tanh_input_left_shift;
-      for (i = 0; i < dims_data; i++) {
-        cell_state[i] = cell_state[i] >> tanh_input_left_shift;
-      }
-      tanh_input_left_shift = 0;
-    }
-    RuntimeShape tanh_inp_shape = RuntimeShape(1, &dims_data);
-    reference_integer_ops::Tanh(0, tanh_input_left_shift, tanh_inp_shape,
-                                cell_state, tanh_inp_shape, scratch0);
-  }
-  micro_tensor_utils::CwiseMul(output_gate, scratch0, hidden_scale_a,
-                               hidden_scale_b, n_batch, n_cell, hidden_zp,
-                               scratch1);
-
-  const bool use_projection = (projection_weights != nullptr);
-
-  if (use_projection) {
-    // Note: no bias like in float/hybrid
-    memset(output_state, 0, n_batch * n_output * sizeof(int8_t));
-    micro_tensor_utils::MatrixBatchVectorMultiplyAccumulate(
-        scratch1, projection_bias, projection_weights, proj_scale_a,
-        proj_scale_b, n_batch, n_cell, n_output, output_state_zp, scratch2,
-        output_state, nullptr);
-    if (quantized_proj_clip > 0) {
-      micro_tensor_utils::CwiseClipping(output_state, n_batch * n_output,
-                                        quantized_proj_clip);
-    }
-  } else {
-    std::memcpy(output_state, scratch1, n_batch * n_output * sizeof(int8_t));
-  }
-}
-
-// Calculates a single LSTM gate, int8x8_8 version.
-// Implements the same functionality as CalculateLstmGateFloat.
-void CalculateLstmGateInteger8x8_8(
-    // Inputs and weights
-    const int8_t* input, int32_t input_zp, const int8_t* input_to_gate_weight,
-    const int32_t input_to_gate_scale_a, const int32_t input_to_gate_scale_b,
-    const int32_t input_times_weights_scale_a,
-    const int32_t input_times_weights_scale_b,
-    const int32_t input_times_weights_zp,
-    // Output state and weights
-    const int8_t* output_state, const int32_t output_state_zp,
-    const int8_t* recurrent_to_gate_weight,
-    const int32_t recurrent_to_gate_scale_a,
-    const int32_t recurrent_to_gate_scale_b,
-    const int32_t output_state_times_weights_scale_a,
-    const int32_t output_state_times_weights_scale_b,
-    const int32_t output_state_times_weights_zp,
-    // Layer normalization parameters (layer norm LSTM)
-    const int16_t* layer_norm_gate_weight,
-    const int32_t layer_norm_gate_scale_a,
-    const int32_t layer_norm_gate_scale_b, const int32_t* gate_bias,
-    // Array sizes
-    const int n_batch, const int n_input, const int n_output, const int n_cell,
-    const TfLiteFusedActivation activation,
-    // Output
-    int16_t* gate,
-    // Scratch arrays, both sized n_batch*n_cell
-    int8_t* scratch0, int8_t* scratch1) {
-  // Multiply input * input_weights => scratch0
-  micro_tensor_utils::MatrixBatchVectorMultiply(
-      input, input_zp, input_to_gate_weight, input_to_gate_scale_a,
-      input_to_gate_scale_b, n_batch, n_input, n_cell, scratch0,
-      input_times_weights_zp);
-  // Multiply output_state * recurrent_weights => scratch1
-  micro_tensor_utils::MatrixBatchVectorMultiply(
-      output_state, output_state_zp, recurrent_to_gate_weight,
-      recurrent_to_gate_scale_a, recurrent_to_gate_scale_b, n_batch, n_output,
-      n_cell, scratch1, output_state_times_weights_zp);
-  // Add scratch0 + scratch1 => gate
-  micro_tensor_utils::TwoGateSaturatingAdd(
-      scratch0, input_times_weights_zp, scratch1, output_state_times_weights_zp,
-      input_times_weights_scale_a, input_times_weights_scale_b,
-      output_state_times_weights_scale_a, output_state_times_weights_scale_b,
-      n_batch, n_cell, gate);
-  // Apply layer normalization.
-  micro_tensor_utils::ApplyLayerNormFloat(
-      gate, layer_norm_gate_weight, layer_norm_gate_scale_a,
-      layer_norm_gate_scale_b, gate_bias, n_batch, n_cell, gate);
-  // Apply activation.
-  switch (activation) {
-    case kTfLiteActSigmoid:
-      micro_tensor_utils::ApplySigmoidFloat(gate, n_batch, n_cell, gate);
-      break;
-    case kTfLiteActTanh:
-      micro_tensor_utils::ApplyTanhFloat(gate, n_batch, n_cell, -12, gate);
-      break;
-    default:
-      // Only Sigmoid or Tanh is used.
-      TFLITE_ASSERT_FALSE;
-  }
-}
-
-// Calculates the output state tensor of an LSTM step. See Float and hybrid
-// versions as well.
-//
-// Parameters:
-//  - n_batch: batches: the number of distinct vectors in each array.
-//  - n_cell, n_output: sizes of vectors.
-//  - cell_state, output_gate: input vectors, size n_batch*n_cell.
-//  - projection_weights, proj_scale_[a|b], projection_bias:
-//      constant inputs, describing projection matrix and bias.
-//  - output_state_zp: zero point of the output state.
-//  - quantized_proj_clip: if > 0, clip the output of the projection.
-//  - output_state: output vector, size n_batch*n_output. Must be contigous.
-//  - scratch: scratch area of size n_batch*n_cell
-void CalculateLstmOutputInteger8x8_8(
-    int n_batch, int n_cell, int n_output, const int16_t* cell_state,
-    const int16_t* output_gate, const int8_t* projection_weights,
-    int32_t proj_scale_a, int32_t proj_scale_b, const int32_t* projection_bias,
-    int32_t output_state_zp, int32_t quantized_proj_clip, int8_t* output_state,
-    int16_t* scratch) {
-  // Note: unlike float/hybrid, the activation is always Tanh.
-  micro_tensor_utils::ApplyTanhFloat(cell_state, n_batch, n_cell, -15, scratch);
-  micro_tensor_utils::CwiseMul(output_gate, scratch, n_batch, n_cell,
-                               15 + 15 - 15, scratch);
-  // Note: no bias like in float/hybrid
-  micro_tensor_utils::MatrixBatchVectorMultiply(
-      scratch, projection_weights, proj_scale_a, proj_scale_b, projection_bias,
-      n_batch, n_cell, n_output, output_state_zp, output_state);
-  if (quantized_proj_clip > 0) {
-    micro_tensor_utils::CwiseClipping(output_state, n_batch * n_output,
-                                      quantized_proj_clip);
-  }
-}
-
-// Performs an LSTM batch inference step for input specified by input_ptr.
-// The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and
-// biases (*_bias_ptr), and buffers (*_scratch), along with additional
-// parameters:
-//  - params: various LSTM params including activation, clipping, etc.,
-//  - n_batch: size of batch,
-//  - n_cell: number of cells (or units),
-//  - n_input: the input size,
-//  - n_aux_input: the auxiliary input size.
-//  - n_output: the output size.
-//  - output_batch_leading_dim: the leading dimension of the output buffer.
-//
-// Input of size 'n_batch * n_input':
-//   input_ptr
-// Input of size 'n_batch * n_aux_input':
-//   aux_input_ptr                     - optional (can be nullptr)
-//
-// LSTM weights:
-// Input weights of size 'n_cell * n_input':
-//   input_to_input_weights            - optional
-//   input_to_forget_weights
-//   input_to_cell_weights
-//   input_to_output_weights
-// Auxiliary input weights of size 'n_cell * n_aux_input':
-//   aux_input_to_input_weights        - optional
-//   aux_input_to_forget_weights       - optional
-//   aux_input_to_cell_weights         - optional
-//   aux_input_to_output_weights       - optional
-// Recurrent weights of size 'n_cell * n_output':
-//   recurrent_to_input_weights        - optional
-//   recurrent_to_forget_weights
-//   recurrent_to_cell_weights
-//   recurrent_to_input_weights
-// Peephole weights of size 'n_cell', representing diagonal matrices.
-//   cell_to_input_weights             - optional
-//   cell_to_cell_weights              - optional
-//   cell_to_output_weights            - optional
-// Projection weights of size 'n_output * n_cell'
-//   projection_weights_ptr            - optional
-// Gate biases of size 'n_cell':
-//   input_gate_bias_ptr               - optional
-//   forget_gate_bias_ptr
-//   cell_gate_bias_ptr
-//   output_gate_bias_ptr
-//
-// Layer norm coefficients of size 'n_cell', representing diagonal matrices.
-//   input_layer_norm_coefficients_ptr  - optional
-//   forget_layer_norm_coefficients_ptr - optional
-//   cell_layer_norm_coefficients_ptr   - optional
-//   output_layer_norm_coefficients_ptr - optional
-//
-// The pointers to the cell and output state and the output are updated.
-//
-// The pointers input_ptr, aux_input_ptr, and output_ptr point to data aligned
-// in batch_major order, and each step processes batch_size many inputs from
-// input_ptr, and updates batch_size many cell and output states.
-//
-// The output_batch_dim is output.shape[-1], i.e. the outermost dimension of the
-// output tensor, and in most cases will be equal to n_output. It is usually not
-// when we want to store the LSTM output into a slice of the output tensor, e.g.
-// for bidirectional LSTMs with merge_outputs. In this case, the batched
-// operations cannot be used since they assume that the batched outputs are
-// contiguous, and we manually loop over the batched outputs.
-inline void LstmStepFloat(
-    const float* input_ptr, const float* input_to_input_weights_ptr,
-    const float* input_to_forget_weights_ptr,
-    const float* input_to_cell_weights_ptr,
-    const float* input_to_output_weights_ptr, const float* aux_input_ptr,
-    const float* aux_input_to_input_weights_ptr,
-    const float* aux_input_to_forget_weights_ptr,
-    const float* aux_input_to_cell_weights_ptr,
-    const float* aux_input_to_output_weights_ptr,
-    const float* recurrent_to_input_weights_ptr,
-    const float* recurrent_to_forget_weights_ptr,
-    const float* recurrent_to_cell_weights_ptr,
-    const float* recurrent_to_output_weights_ptr,
-    const float* cell_to_input_weights_ptr,
-    const float* cell_to_forget_weights_ptr,
-    const float* cell_to_output_weights_ptr,
-    const float* input_layer_norm_coefficients_ptr,
-    const float* forget_layer_norm_coefficients_ptr,
-    const float* cell_layer_norm_coefficients_ptr,
-    const float* output_layer_norm_coefficients_ptr,
-    const float* input_gate_bias_ptr, const float* forget_gate_bias_ptr,
-    const float* cell_gate_bias_ptr, const float* output_gate_bias_ptr,
-    const float* projection_weights_ptr, const float* projection_bias_ptr,
-    const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input,
-    int n_aux_input, int n_output, int output_batch_leading_dim,
-    float* output_state_ptr, float* cell_state_ptr, float* scratch0,
-    float* scratch1, float* scratch2, float* scratch3, float* output_ptr) {
-  // Since we have already checked that weights are all there or none, we can
-  // check the existence of only one to the get the condition.
-  const bool use_cifg = (input_to_input_weights_ptr == nullptr);
-
-  // Make named scratch buffers.
-  float* input_gate_scratch = scratch0;
-  float* forget_gate_scratch = scratch1;
-  float* cell_gate_scratch = scratch2;
-  float* output_gate_scratch = scratch3;
-
-  // Check if inputs are all zeros so we can skip some computations.
-  const bool is_input_all_zeros =
-      micro_tensor_utils::IsZeroVector(input_ptr, n_batch * n_input);
-  const bool is_aux_input_all_zeros =
-      (aux_input_ptr == nullptr ||
-       micro_tensor_utils::IsZeroVector(aux_input_ptr, n_batch * n_aux_input));
-  if (!use_cifg) {
-    // Calculate the input gate. (If not CIFG.)
-    CalculateLstmGateFloat(
-        input_ptr, input_to_input_weights_ptr, aux_input_ptr,
-        aux_input_to_input_weights_ptr, output_state_ptr,
-        recurrent_to_input_weights_ptr, cell_state_ptr,
-        cell_to_input_weights_ptr, input_layer_norm_coefficients_ptr,
-        input_gate_bias_ptr, n_batch, n_input, n_aux_input, n_output, n_cell,
-        /*activation=*/kTfLiteActSigmoid, input_gate_scratch,
-        is_input_all_zeros, is_aux_input_all_zeros);
-  }
-  // Calculate the forget gate.
-  CalculateLstmGateFloat(
-      input_ptr, input_to_forget_weights_ptr, aux_input_ptr,
-      aux_input_to_forget_weights_ptr, output_state_ptr,
-      recurrent_to_forget_weights_ptr, cell_state_ptr,
-      cell_to_forget_weights_ptr, forget_layer_norm_coefficients_ptr,
-      forget_gate_bias_ptr, n_batch, n_input, n_aux_input, n_output, n_cell,
-      /*activation=*/kTfLiteActSigmoid, forget_gate_scratch, is_input_all_zeros,
-      is_aux_input_all_zeros);
-  // Calculate the cell update gate.
-  CalculateLstmGateFloat(input_ptr, input_to_cell_weights_ptr, aux_input_ptr,
-                         aux_input_to_cell_weights_ptr, output_state_ptr,
-                         recurrent_to_cell_weights_ptr, /*cell_state=*/nullptr,
-                         /*cell_to_gate_weights=*/nullptr,
-                         cell_layer_norm_coefficients_ptr, cell_gate_bias_ptr,
-                         n_batch, n_input, n_aux_input, n_output, n_cell,
-                         params->activation, cell_gate_scratch,
-                         is_input_all_zeros, is_aux_input_all_zeros);
-  // Update the cell state.
-  UpdateLstmCellFloat(n_batch, n_cell, cell_state_ptr, input_gate_scratch,
-                      forget_gate_scratch, cell_gate_scratch, use_cifg,
-                      params->cell_clip);
-  // Calculate output gate.
-  CalculateLstmGateFloat(
-      input_ptr, input_to_output_weights_ptr, aux_input_ptr,
-      aux_input_to_output_weights_ptr, output_state_ptr,
-      recurrent_to_output_weights_ptr, cell_state_ptr,
-      cell_to_output_weights_ptr, output_layer_norm_coefficients_ptr,
-      output_gate_bias_ptr, n_batch, n_input, n_aux_input, n_output, n_cell,
-      /*activation=*/kTfLiteActSigmoid, output_gate_scratch, is_input_all_zeros,
-      is_aux_input_all_zeros);
-  // Update the output state.
-  CalculateLstmOutputFloat(n_batch, n_cell, n_output, cell_state_ptr,
-                           output_gate_scratch, params->activation,
-                           projection_weights_ptr, projection_bias_ptr,
-                           params->proj_clip, output_state_ptr, scratch2);
-  // Copy output state to the output. Note that the output's rows may not be
-  // contiguous (output_batch_leading_dim != n_output).
-  for (int b = 0; b < n_batch; b++) {
-    std::memcpy(output_ptr + b * output_batch_leading_dim,
-                output_state_ptr + b * n_output, n_output * sizeof(float));
-  }
-}
-
-// Same as above but with quantized weight matrices. In detail:
-// Input of size 'n_batch * n_input':
-//   input_ptr
-// Input of size 'n_batch * n_aux_input':
-//   aux_input_ptr                     - optional (can be nullptr)
-//
-// LSTM weights:
-// Quantized input weights of size 'n_cell * n_input':
-//   input_to_input_weights            - optional
-//   input_to_forget_weights
-//   input_to_cell_weights
-//   input_to_input_weights
-// Quantized auxiliary input weights of size 'n_cell * n_aux_input':
-//   aux_input_to_input_weights        - optional
-//   aux_input_to_forget_weights       - optional
-//   aux_input_to_cell_weights         - optional
-//   aux_input_to_output_weights       - optional
-// Quantized recurrent weights of size 'n_cell * n_output':
-//   recurrent_to_input_weights        - optional
-//   recurrent_to_forget_weights
-//   recurrent_to_cell_weights
-//   recurrent_to_input_weights
-// Quantized peephole weights of size 'n_cell', representing diagonal matrices.
-//   cell_to_input_weights             - optional
-//   cell_to_cell_weights              - optional
-//   cell_to_output_weights            - optional
-// Quantized projection weights of size 'n_output * n_cell'
-//   projection_weights_ptr            - optional
-// Weight scales (scalars) for each of the weights above.
-//   input_to_input_weights_scale      - optional
-//   input_to_forget_weights_scale
-//   input_to_cell_weights_scale
-//   input_to_output_weights_scale
-//   aux_input_to_input_weights_scale  - optional
-//   aux_input_to_forget_weights_scale - optional
-//   aux_input_to_cell_weights_scale   - optional
-//   aux_input_to_output_weights_scale - optional
-//   recurrent_to_input_weights_scale  - optional
-//   recurrent_to_forget_weights_scale
-//   recurrent_to_cell_weights_scale
-//   recurrent_to_output_weights_scale
-//   cell_to_input_weights_scale,
-//   cell_to_forget_weights_scale,
-//   cell_to_output_weights_scale,
-//   projection_weights_scale          - optional
-// Gate biases of size 'n_cell':
-//   input_gate_bias_ptr               - optional
-//   forget_gate_bias_ptr
-//   cell_gate_bias_ptr
-//   output_gate_bias_ptr
-//
-// Layer norm coefficients of size 'n_cell', representing diagonal matrices.
-//   input_layer_norm_coefficients_ptr  - optional
-//   forget_layer_norm_coefficients_ptr - optional
-//   cell_layer_norm_coefficients_ptr   - optional
-//   output_layer_norm_coefficients_ptr - optional
-//
-// Temporary pre-allocated storage for quantized values:
-//   quantized_input_ptr (same size as input_ptr)
-//   quantized_output_state_ptr (same size as output_state_ptr)
-//   quantized_output_scratch (same size as cell_state_ptr)
-// Temporary pre-allocated storage for recovered values:
-//   recovered_cell_weights (same size as cell_to_*_weights)
-//
-// Outputs:
-//   output_state_ptr - size 'n_batch * n_output'
-//   cell_state_ptr   - size 'n_batch * n_cell'
-//   output_ptr       - size 'n_batch * output_batch_leading_dim'
-inline void LstmStepHybrid(
-    const float* input_ptr, const int8_t* input_to_input_weights_ptr,
-    const uint8_t* input_to_input_weights_ledger_ptr,
-    float input_to_input_weights_scale,
-    const int8_t* input_to_forget_weights_ptr,
-    const uint8_t* input_to_forget_weights_ledger_ptr,
-    float input_to_forget_weights_scale,
-    const int8_t* input_to_cell_weights_ptr,
-    const uint8_t* input_to_cell_weights_ledger_ptr,
-    float input_to_cell_weights_scale,
-    const int8_t* input_to_output_weights_ptr,
-    const uint8_t* input_to_output_weights_ledger_ptr,
-    float input_to_output_weights_scale, const float* aux_input_ptr,
-    const int8_t* aux_input_to_input_weights_ptr,
-    float aux_input_to_input_weights_scale,
-    const int8_t* aux_input_to_forget_weights_ptr,
-    float aux_input_to_forget_weights_scale,
-    const int8_t* aux_input_to_cell_weights_ptr,
-    float aux_input_to_cell_weights_scale,
-    const int8_t* aux_input_to_output_weights_ptr,
-    float aux_input_to_output_weights_scale,
-    const int8_t* recurrent_to_input_weights_ptr,
-    const uint8_t* recurrent_to_input_weights_ledger_ptr,
-    float recurrent_to_input_weights_scale,
-    const int8_t* recurrent_to_forget_weights_ptr,
-    const uint8_t* recurrent_to_forget_weights_ledger_ptr,
-    float recurrent_to_forget_weights_scale,
-    const int8_t* recurrent_to_cell_weights_ptr,
-    const uint8_t* recurrent_to_cell_weights_ledger_ptr,
-    float recurrent_to_cell_weights_scale,
-    const int8_t* recurrent_to_output_weights_ptr,
-    const uint8_t* recurrent_to_output_weights_ledger_ptr,
-    float recurrent_to_output_weights_scale,
-    const int8_t* cell_to_input_weights_ptr, float cell_to_input_weights_scale,
-    const int8_t* cell_to_forget_weights_ptr,
-    float cell_to_forget_weights_scale,
-    const int8_t* cell_to_output_weights_ptr,
-    float cell_to_output_weights_scale,
-    const float* input_layer_norm_coefficients_ptr,
-    const float* forget_layer_norm_coefficients_ptr,
-    const float* cell_layer_norm_coefficients_ptr,
-    const float* output_layer_norm_coefficients_ptr,
-    const float* input_gate_bias_ptr, const float* forget_gate_bias_ptr,
-    const float* cell_gate_bias_ptr, const float* output_gate_bias_ptr,
-    const int8_t* projection_weights_ptr,
-    const uint8_t* projection_weights_ledger_ptr,
-    float projection_weights_scale, const float* projection_bias_ptr,
-    const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input,
-    int n_aux_input, int n_output, int output_batch_leading_dim,
-    float* scratch0, float* scratch1, float* scratch2, float* scratch3,
-    float* scales, float* input_sf, float* aux_input_sf, float* output_state_sf,
-    float* scaling_factors_scratch, float* recovered_cell_weights,
-    int8_t* quantized_input_ptr, int8_t* quantized_aux_input_ptr,
-    int8_t* quantized_output_state_ptr, int8_t* quantized_output_scratch,
-    float* output_state_ptr, float* cell_state_ptr, int32_t* accum_scratch_ptr,
-    float* output_ptr, int32_t* input_zp, int32_t* aux_input_zp,
-    int32_t* output_state_zp, int32_t* row_sums, int row_sums_size,
-    bool* compute_row_sums, bool asymmetric_quantize_inputs) {
-  // Since we have already checked that weights are all there or none, we
-  // can check the existence of only one to the get the condition.
-  const bool use_cifg = (input_to_input_weights_ptr == nullptr);
-  // Make named scratch buffers for the different gates.
-  float* input_gate_scratch = scratch0;
-  float* forget_gate_scratch = scratch1;
-  float* cell_gate_scratch = scratch2;
-  float* output_gate_scratch = scratch3;
-
-  int32_t* input_to_input_row_sums = nullptr;
-  int32_t* input_to_forget_row_sums = nullptr;
-  int32_t* input_to_cell_row_sums = nullptr;
-  int32_t* input_to_output_row_sums = nullptr;
-  int32_t* aux_input_to_input_row_sums = nullptr;
-  int32_t* aux_input_to_forget_row_sums = nullptr;
-  int32_t* aux_input_to_cell_row_sums = nullptr;
-  int32_t* aux_input_to_output_row_sums = nullptr;
-  int32_t* recurrent_to_input_row_sums = nullptr;
-  int32_t* recurrent_to_forget_row_sums = nullptr;
-  int32_t* recurrent_to_cell_row_sums = nullptr;
-  int32_t* recurrent_to_output_row_sums = nullptr;
-  int32_t* projection_weights_row_sums = nullptr;
-
-  if (asymmetric_quantize_inputs) {
-    int num_row_sums = use_cifg ? 6 : 8;
-    if (aux_input_ptr != nullptr) {
-      num_row_sums += use_cifg ? 3 : 4;
-    }
-    if (projection_weights_ptr != nullptr) {
-      num_row_sums += ceil(static_cast<float>(n_output) / n_cell);
-    }
-    TFLITE_DCHECK(row_sums_size == num_row_sums);
-    input_to_input_row_sums = row_sums;
-    input_to_forget_row_sums =
-        use_cifg ? input_to_input_row_sums : input_to_input_row_sums + n_cell;
-    input_to_cell_row_sums = input_to_forget_row_sums + n_cell;
-    input_to_output_row_sums = input_to_cell_row_sums + n_cell;
-    if (aux_input_ptr != nullptr) {
-      aux_input_to_input_row_sums = input_to_output_row_sums + n_cell;
-      aux_input_to_forget_row_sums = use_cifg
-                                         ? aux_input_to_input_row_sums
-                                         : aux_input_to_input_row_sums + n_cell;
-      aux_input_to_cell_row_sums = aux_input_to_forget_row_sums + n_cell;
-      aux_input_to_output_row_sums = aux_input_to_cell_row_sums + n_cell;
-    }
-    recurrent_to_input_row_sums = aux_input_ptr
-                                      ? aux_input_to_output_row_sums + n_cell
-                                      : input_to_output_row_sums + n_cell;
-    recurrent_to_forget_row_sums = use_cifg
-                                       ? recurrent_to_input_row_sums
-                                       : recurrent_to_input_row_sums + n_cell;
-    recurrent_to_cell_row_sums = recurrent_to_forget_row_sums + n_cell;
-    recurrent_to_output_row_sums = recurrent_to_cell_row_sums + n_cell;
-    if (projection_weights_ptr != nullptr) {
-      projection_weights_row_sums = recurrent_to_output_row_sums + n_cell;
-    }
-    if (*compute_row_sums) {
-      ComputeRowSums(
-          input_to_input_row_sums, input_to_forget_row_sums,
-          input_to_cell_row_sums, input_to_output_row_sums,
-          aux_input_to_input_row_sums, aux_input_to_forget_row_sums,
-          aux_input_to_cell_row_sums, aux_input_to_output_row_sums,
-          recurrent_to_input_row_sums, recurrent_to_forget_row_sums,
-          recurrent_to_cell_row_sums, recurrent_to_output_row_sums,
-          projection_weights_row_sums, row_sums, n_cell, n_input, n_aux_input,
-          n_output, input_to_input_weights_ptr, input_to_forget_weights_ptr,
-          input_to_cell_weights_ptr, input_to_output_weights_ptr,
-          aux_input_to_input_weights_ptr, aux_input_to_forget_weights_ptr,
-          aux_input_to_cell_weights_ptr, aux_input_to_output_weights_ptr,
-          recurrent_to_input_weights_ptr, recurrent_to_forget_weights_ptr,
-          recurrent_to_cell_weights_ptr, recurrent_to_output_weights_ptr,
-          projection_weights_ptr, use_cifg, aux_input_ptr);
-      *compute_row_sums = false;
-    }
-  }
-
-  // Check if inputs are all zeros so we can skip some computations.
-  const bool is_input_all_zeros =
-      micro_tensor_utils::IsZeroVector(input_ptr, n_batch * n_input);
-  const bool is_aux_input_all_zeros =
-      (aux_input_ptr == nullptr ||
-       micro_tensor_utils::IsZeroVector(aux_input_ptr, n_batch * n_aux_input));
-  const bool is_output_state_all_zeros =
-      micro_tensor_utils::IsZeroVector(output_state_ptr, n_batch * n_output);
-  // Quantize inputs.
-  if (!is_input_all_zeros) {
-    micro_tensor_utils::BatchQuantizeFloats(
-        input_ptr, n_batch, n_input, quantized_input_ptr, input_sf, input_zp,
-        asymmetric_quantize_inputs);
-  }
-  if (!is_aux_input_all_zeros) {
-    micro_tensor_utils::BatchQuantizeFloats(
-        aux_input_ptr, n_batch, n_aux_input, quantized_aux_input_ptr,
-        aux_input_sf, aux_input_zp, asymmetric_quantize_inputs);
-  }
-  if (!is_output_state_all_zeros) {
-    micro_tensor_utils::BatchQuantizeFloats(
-        output_state_ptr, n_batch, n_output, quantized_output_state_ptr,
-        output_state_sf, output_state_zp, asymmetric_quantize_inputs);
-  }
-  if (!use_cifg) {
-    // Calculate the input gate. (If not CIFG.)
-    CalculateLstmGateHybrid(
-        quantized_input_ptr, input_sf, input_zp, input_to_input_weights_ptr,
-        input_to_input_weights_ledger_ptr, input_to_input_weights_scale,
-        input_to_input_row_sums, quantized_aux_input_ptr, aux_input_sf,
-        aux_input_zp, aux_input_to_input_weights_ptr,
-        aux_input_to_input_weights_scale, aux_input_to_input_row_sums,
-        quantized_output_state_ptr, output_state_sf, output_state_zp,
-        recurrent_to_input_weights_ptr, recurrent_to_input_weights_ledger_ptr,
-        recurrent_to_input_weights_scale, recurrent_to_input_row_sums,
-        cell_state_ptr, cell_to_input_weights_ptr, cell_to_input_weights_scale,
-        input_layer_norm_coefficients_ptr, input_gate_bias_ptr, n_batch,
-        n_input, n_aux_input, n_output, n_cell, kTfLiteActSigmoid,
-        input_gate_scratch, is_input_all_zeros, is_aux_input_all_zeros,
-        is_output_state_all_zeros, compute_row_sums, scaling_factors_scratch,
-        recovered_cell_weights, scales, accum_scratch_ptr);
-  }
-  // Calculate the forget gate.
-  CalculateLstmGateHybrid(
-      quantized_input_ptr, input_sf, input_zp, input_to_forget_weights_ptr,
-      input_to_forget_weights_ledger_ptr, input_to_forget_weights_scale,
-      input_to_forget_row_sums, quantized_aux_input_ptr, aux_input_sf,
-      aux_input_zp, aux_input_to_forget_weights_ptr,
-      aux_input_to_forget_weights_scale, aux_input_to_forget_row_sums,
-      quantized_output_state_ptr, output_state_sf, output_state_zp,
-      recurrent_to_forget_weights_ptr, recurrent_to_forget_weights_ledger_ptr,
-      recurrent_to_forget_weights_scale, recurrent_to_forget_row_sums,
-      cell_state_ptr, cell_to_forget_weights_ptr, cell_to_forget_weights_scale,
-      forget_layer_norm_coefficients_ptr, forget_gate_bias_ptr, n_batch,
-      n_input, n_aux_input, n_output, n_cell, kTfLiteActSigmoid,
-      forget_gate_scratch, is_input_all_zeros, is_aux_input_all_zeros,
-      is_output_state_all_zeros, compute_row_sums, scaling_factors_scratch,
-      recovered_cell_weights, scales, accum_scratch_ptr);
-  // Calculate the cell update gate.
-  CalculateLstmGateHybrid(
-      quantized_input_ptr, input_sf, input_zp, input_to_cell_weights_ptr,
-      input_to_cell_weights_ledger_ptr, input_to_cell_weights_scale,
-      input_to_cell_row_sums, quantized_aux_input_ptr, aux_input_sf,
-      aux_input_zp, aux_input_to_cell_weights_ptr,
-      aux_input_to_cell_weights_scale, aux_input_to_cell_row_sums,
-      quantized_output_state_ptr, output_state_sf, output_state_zp,
-      recurrent_to_cell_weights_ptr, recurrent_to_cell_weights_ledger_ptr,
-      recurrent_to_cell_weights_scale, recurrent_to_cell_row_sums,
-      /*cell_state=*/nullptr, /*cell_to_gate_weights=*/nullptr,
-      /*cell_to_gate_weights_scale=*/0.0f, cell_layer_norm_coefficients_ptr,
-      cell_gate_bias_ptr, n_batch, n_input, n_aux_input, n_output, n_cell,
-      params->activation, cell_gate_scratch, is_input_all_zeros,
-      is_aux_input_all_zeros, is_output_state_all_zeros, compute_row_sums,
-      scaling_factors_scratch, recovered_cell_weights, scales,
-      accum_scratch_ptr);
-  // Update the cell state.
-  UpdateLstmCellFloat(n_batch, n_cell, cell_state_ptr, input_gate_scratch,
-                      forget_gate_scratch, cell_gate_scratch, use_cifg,
-                      params->cell_clip);
-  // Calculate the output gate.
-  CalculateLstmGateHybrid(
-      quantized_input_ptr, input_sf, input_zp, input_to_output_weights_ptr,
-      input_to_output_weights_ledger_ptr, input_to_output_weights_scale,
-      input_to_output_row_sums, quantized_aux_input_ptr, aux_input_sf,
-      aux_input_zp, aux_input_to_output_weights_ptr,
-      aux_input_to_output_weights_scale, aux_input_to_output_row_sums,
-      quantized_output_state_ptr, output_state_sf, output_state_zp,
-      recurrent_to_output_weights_ptr, recurrent_to_output_weights_ledger_ptr,
-      recurrent_to_output_weights_scale, recurrent_to_output_row_sums,
-      cell_state_ptr, cell_to_output_weights_ptr, cell_to_output_weights_scale,
-      output_layer_norm_coefficients_ptr, output_gate_bias_ptr, n_batch,
-      n_input, n_aux_input, n_output, n_cell, kTfLiteActSigmoid,
-      output_gate_scratch, is_input_all_zeros, is_aux_input_all_zeros,
-      is_output_state_all_zeros, compute_row_sums, scaling_factors_scratch,
-      recovered_cell_weights, scales, accum_scratch_ptr);
-  // Update the output state.
-  CalculateLstmOutputHybrid(
-      n_batch, n_cell, n_output, cell_state_ptr, output_gate_scratch,
-      params->activation, projection_weights_ptr, projection_weights_ledger_ptr,
-      projection_weights_scale, projection_bias_ptr, params->proj_clip,
-      output_state_ptr, asymmetric_quantize_inputs, projection_weights_row_sums,
-      compute_row_sums, scratch2, quantized_output_scratch, input_sf, input_zp,
-      accum_scratch_ptr, scales);
-  // Copy output state to the output. Note that the output's rows may not be
-  // contiguous (output_batch_leading_dim != n_output).
-  for (int b = 0; b < n_batch; b++) {
-    std::memcpy(output_ptr + b * output_batch_leading_dim,
-                output_state_ptr + b * n_output, n_output * sizeof(float));
-  }
-}
-
-// Fully quantized lstm kernel for 16 bit gate matmul output.
-//
-// Input tensor of size n_batch * n_input:
-//   input_ptr
-//
-// LSTM weights:
-// Quantized input weights of size 'n_cell * n_input':
-//   input_to_input_weight_ptr            - optional
-//   input_to_forget_weight_ptr           - optional
-//   input_to_cell_weight_ptr             - optional
-//   input_to_output_weight_ptr           - optional
-//
-// Quantized recurrent weights of size 'n_cell * n_output':
-//   recurrent_to_input_weight_ptr        - optional
-//   recurrent_to_forget_weights_ptr
-//   recurrent_to_cell_weights_ptr
-//   recurrent_to_input_weights_ptr
-//
-// Quantized peephole weights of size 'n_cell', representing diagonal matrices.
-//   cell_to_input_weights               - optional
-//   cell_to_cell_weights                - optional
-//   cell_to_output_weights              - optional
-//
-// Quantized projection weights of size 'n_output * n_cell'
-//   projection_weight_ptr                     - optional
-//
-// Weight scales (scalars) for each of the weights above.
-//   effective_input_to_input_scale_a    - optional
-//   effective_input_to_input_scale_b    - optional
-//   effective_input_to_forget_scale_a
-//   effective_input_to_forget_scale_b
-//   effective_input_to_cell_scale_a
-//   effective_input_to_cell_scale_b
-//   effective_input_to_output_scale_a
-//   effective_input_to_output_scale_b
-//   effective_recurrent_to_input_scale_a    - optional
-//   effective_recurrent_to_input_scale_b    - optional
-//   effective_recurrent_to_forget_scale_a
-//   effective_recurrent_to_forget_scale_b
-//   effective_recurrent_to_cell_scale_a
-//   effective_recurrent_to_cell_scale_b
-//   effective_recurrent_to_output_scale_a
-//   effective_recurrent_to_output_scale_b
-//   effective_proj_scale_a                  - optional
-//   effective_proj_scale_b                  - optional
-//
-// Gate biases of size 'n_cell':
-//   input_gate_bias_ptr                 - optional
-//   forget_gate_bias_ptr
-//   cell_gate_bias_ptr
-//   output_gate_bias_ptr
-//
-// Layer norm coefficients of size 'n_cell', representing diagonal matrices.
-//   layer_norm_input_weight_ptr    - optional
-//   layer_norm_forget_weight_ptr   - optional
-//   layer_norm_cell_weight_ptr     - optional
-//   layer_norm_output_weight_ptr   - optional
-//
-// Layer norm scales of size 'n_cell'.
-//   layer_norm_input_scale_a     - optional
-//   layer_norm_input_scale_b     - optional
-//   layer_norm_forget_scale_a    - optional
-//   layer_norm_forget_scale_b    - optional
-//   layer_norm_cell_scale_a      - optional
-//   layer_norm_cell_scale_b      - optional
-//   layer_norm_output_scale_a    - optional
-//   layer_norm_output_scale_b    - optional
-//
-// Scalar values:
-//   quantized_cell_clip: quantized clip value for cell.
-//   quantized_proj_clip: quantized clip value for projection.
-//   cell_state_scale: the power of two scale for cell state.
-//
-// Zero points:
-//   output_state_zp: zero point of output state
-//   hidden_zp: zero point for hidden state.
-//
-// Temporary pre-allocated storage for the calculation. Each is of size n_cell *
-// n_batch.
-//   scratch0
-//   scratch1
-//   scratch2
-//   scratch3
-//   scratch4
-//   scratch5: this scratch buffer is created purely for optimizing the
-//              MatrixBatchVectorMultiplyAccumulate.
-//
-// Outputs:
-//   output_state_ptr - size 'n_batch * n_output'
-//   cell_state_ptr   - size 'n_batch * n_cell'
-//   output_ptr       - size 'n_batch * n_output'
-// TODO(b/159947023): scratch0 is not used if (!cifg). Don't allocate then.
-inline void LstmStepInteger8x8_16(
-    const int8_t* input_ptr, const int8_t* input_to_input_weight_ptr,
-    int32_t effective_input_to_input_scale_a,
-    int32_t effective_input_to_input_scale_b,
-    const int8_t* input_to_forget_weight_ptr,
-    int32_t effective_input_to_forget_scale_a,
-    int32_t effective_input_to_forget_scale_b,
-    const int8_t* input_to_cell_weight_ptr,
-    int32_t effective_input_to_cell_scale_a,
-    int32_t effective_input_to_cell_scale_b,
-    const int8_t* input_to_output_weight_ptr,
-    int32_t effective_input_to_output_scale_a,
-    int32_t effective_input_to_output_scale_b,
-    const int8_t* recurrent_to_input_weight_ptr,
-    int32_t effective_recurrent_to_input_scale_a,
-    int32_t effective_recurrent_to_input_scale_b,
-    const int8_t* recurrent_to_forget_weight_ptr,
-    int32_t effective_recurrent_to_forget_scale_a,
-    int32_t effective_recurrent_to_forget_scale_b,
-    const int8_t* recurrent_to_cell_weight_ptr,
-    int32_t effective_recurrent_to_cell_scale_a,
-    int32_t effective_recurrent_to_cell_scale_b,
-    const int8_t* recurrent_to_output_weight_ptr,
-    int32_t effective_recurrent_to_output_scale_a,
-    int32_t effective_recurrent_to_output_scale_b,
-    const int16_t* cell_to_input_weight_ptr,
-    int32_t effective_cell_to_input_scale_a,
-    int32_t effective_cell_to_input_scale_b,
-    const int16_t* cell_to_forget_weight_ptr,
-    int32_t effective_cell_to_forget_scale_a,
-    int32_t effective_cell_to_forget_scale_b,
-    const int16_t* cell_to_output_weight_ptr,
-    int32_t effective_cell_to_output_scale_a,
-    int32_t effective_cell_to_output_scale_b,
-    const int8_t* projection_weight_ptr, int32_t effective_proj_scale_a,
-    int32_t effective_proj_scale_b, int32_t hidden_zp,
-    int32_t effective_hidden_scale_a, int32_t effective_hidden_scale_b,
-    const int16_t* layer_norm_input_weight_ptr,
-    int32_t layer_norm_input_scale_a, int32_t layer_norm_input_scale_b,
-    const int16_t* layer_norm_forget_weight_ptr,
-    int32_t layer_norm_forget_scale_a, int32_t layer_norm_forget_scale_b,
-    const int16_t* layer_norm_cell_weight_ptr, int32_t layer_norm_cell_scale_a,
-    int32_t layer_norm_cell_scale_b,
-    const int16_t* layer_norm_output_weight_ptr,
-    int32_t layer_norm_output_scale_a, int32_t layer_norm_output_scale_b,
-    const int32_t* input_gate_bias_ptr, const int32_t* forget_gate_bias_ptr,
-    const int32_t* cell_gate_bias_ptr, const int32_t* output_gate_bias_ptr,
-    int16_t quantized_cell_clip, int8_t quantized_proj_clip,
-    int32_t cell_state_scale, int32_t input_variance_guard,
-    int32_t forget_variance_guard, int32_t cell_variance_guard,
-    int32_t output_variance_guard,
-    const int32_t* input_to_forget_effective_bias,
-    const int32_t* recurrent_to_forget_effective_bias,
-    const int32_t* input_to_cell_effective_bias,
-    const int32_t* recurrent_to_cell_effective_bias,
-    const int32_t* input_to_output_effective_bias,
-    const int32_t* recurrent_to_output_effective_bias,
-    const int32_t* input_to_input_effective_bias,
-    const int32_t* recurrent_to_input_effective_bias,
-    const int32_t* projection_effective_bias, int n_batch, int n_cell,
-    int n_input, int n_output, int8_t* output_state_ptr,
-    int32_t output_state_zp, int16_t* cell_state_ptr, int8_t* output_ptr,
-    int16_t* scratch0, int16_t* scratch1, int16_t* scratch2, int16_t* scratch3,
-    int8_t* scratch4, int32_t* scratch5) {
-  // Make named scratch buffers for the different gates.
-  int16_t* input_gate_scratch = scratch0;
-  int16_t* forget_gate_scratch = scratch1;
-  int16_t* cell_gate_scratch = scratch2;
-  int16_t* output_gate_scratch = scratch3;
-
-  // Since we have already checked that weights are all there or none, we
-  // can check the existence of only one to the get the condition.
-  const bool use_cifg = (input_to_input_weight_ptr == nullptr);
-
-  // Check for nullptrs.
-  TFLITE_DCHECK(input_to_forget_effective_bias);
-  TFLITE_DCHECK(recurrent_to_forget_effective_bias);
-  TFLITE_DCHECK(input_to_cell_effective_bias);
-  TFLITE_DCHECK(recurrent_to_cell_effective_bias);
-  TFLITE_DCHECK(input_to_output_effective_bias);
-  TFLITE_DCHECK(recurrent_to_output_effective_bias);
-  if (!use_cifg) {
-    TFLITE_DCHECK(input_to_input_effective_bias);
-    TFLITE_DCHECK(recurrent_to_input_effective_bias);
-  }
-  const bool use_projection = (projection_weight_ptr != nullptr);
-  if (use_projection) {
-    TFLITE_DCHECK(projection_effective_bias);
-  }
-  if (!use_cifg) {
-    // Calculate the input gate. (If not CIFG.)
-    CalculateLstmGateInteger8x8_16(
-        input_ptr, input_to_input_weight_ptr, input_to_input_effective_bias,
-        effective_input_to_input_scale_a, effective_input_to_input_scale_b,
-        output_state_ptr, recurrent_to_input_weight_ptr,
-        recurrent_to_input_effective_bias, effective_recurrent_to_input_scale_a,
-        effective_recurrent_to_input_scale_b, cell_state_ptr,
-        cell_to_input_weight_ptr, effective_cell_to_input_scale_a,
-        effective_cell_to_input_scale_b, layer_norm_input_weight_ptr,
-        input_gate_bias_ptr, layer_norm_input_scale_a, layer_norm_input_scale_b,
-        input_variance_guard, n_batch, n_input, n_output, n_cell,
-        kTfLiteActSigmoid, input_gate_scratch, scratch5);
-  }
-  // Calculate the forget gate.
-  CalculateLstmGateInteger8x8_16(
-      input_ptr, input_to_forget_weight_ptr, input_to_forget_effective_bias,
-      effective_input_to_forget_scale_a, effective_input_to_forget_scale_b,
-      output_state_ptr, recurrent_to_forget_weight_ptr,
-      recurrent_to_forget_effective_bias, effective_recurrent_to_forget_scale_a,
-      effective_recurrent_to_forget_scale_b, cell_state_ptr,
-      cell_to_forget_weight_ptr, effective_cell_to_forget_scale_a,
-      effective_cell_to_forget_scale_b, layer_norm_forget_weight_ptr,
-      forget_gate_bias_ptr, layer_norm_forget_scale_a,
-      layer_norm_forget_scale_b, forget_variance_guard, n_batch, n_input,
-      n_output, n_cell, kTfLiteActSigmoid, forget_gate_scratch, scratch5);
-  // Calculate the cell update gate.
-  CalculateLstmGateInteger8x8_16(
-      input_ptr, input_to_cell_weight_ptr, input_to_cell_effective_bias,
-      effective_input_to_cell_scale_a, effective_input_to_cell_scale_b,
-      output_state_ptr, recurrent_to_cell_weight_ptr,
-      recurrent_to_cell_effective_bias, effective_recurrent_to_cell_scale_a,
-      effective_recurrent_to_cell_scale_b, cell_state_ptr,
-      /*cell_to_gate_weights=*/nullptr, /*cell_to_gate_scale_a=*/0,
-      /*cell_to_gate_scale_b=*/0, layer_norm_cell_weight_ptr,
-      cell_gate_bias_ptr, layer_norm_cell_scale_a, layer_norm_cell_scale_b,
-      cell_variance_guard, n_batch, n_input, n_output, n_cell, kTfLiteActTanh,
-      cell_gate_scratch, scratch5);
-  // Update the cell state.
-  UpdateLstmCellInteger(n_batch, n_cell, cell_state_ptr, cell_state_scale,
-                        input_gate_scratch, forget_gate_scratch,
-                        cell_gate_scratch, use_cifg, quantized_cell_clip);
-  // Calculate the output gate.
-  CalculateLstmGateInteger8x8_16(
-      input_ptr, input_to_output_weight_ptr, input_to_output_effective_bias,
-      effective_input_to_output_scale_a, effective_input_to_output_scale_b,
-      output_state_ptr, recurrent_to_output_weight_ptr,
-      recurrent_to_output_effective_bias, effective_recurrent_to_output_scale_a,
-      effective_recurrent_to_output_scale_b, cell_state_ptr,
-      cell_to_output_weight_ptr, effective_cell_to_output_scale_a,
-      effective_cell_to_output_scale_b, layer_norm_output_weight_ptr,
-      output_gate_bias_ptr, layer_norm_output_scale_a,
-      layer_norm_output_scale_b, output_variance_guard, n_batch, n_input,
-      n_output, n_cell, kTfLiteActSigmoid, output_gate_scratch, scratch5);
-  // Update the output state.
-  CalculateLstmOutputInteger8x8_16(
-      n_batch, n_cell, n_output, cell_state_ptr, cell_state_scale,
-      output_gate_scratch, effective_hidden_scale_a, effective_hidden_scale_b,
-      hidden_zp, projection_weight_ptr, effective_proj_scale_a,
-      effective_proj_scale_b, projection_effective_bias, output_state_zp,
-      quantized_proj_clip, output_state_ptr, scratch0, scratch4, scratch5);
-  // Copy output state to the output. Note that unlike float or hybrid, output
-  // is always contiguous.
-  std::memcpy(output_ptr, output_state_ptr,
-              n_batch * n_output * sizeof(int8_t));
-}
-
-// Fully quantized lstm kernel for 8 bit gate matmul output.
-//
-// Input tensor of size n_batch * n_input:
-//   input_ptr
-//
-// LSTM weights:
-// Quantized input weights of size 'n_cell * n_input':
-//   input_to_input_weight_ptr            - optional
-//   input_to_forget_weight_ptr           - optional
-//   input_to_cell_weight_ptr             - optional
-//   input_to_output_weight_ptr           - optional
-//
-// Quantized recurrent weights of size 'n_cell * n_output':
-//   recurrent_to_input_weight_ptr        - optional
-//   recurrent_to_forget_weights_ptr
-//   recurrent_to_cell_weights_ptr
-//   recurrent_to_input_weights_ptr
-//
-// Quantized peephole weights of size 'n_cell', representing diagonal matrices.
-//   cell_to_input_weights               - optional
-//   cell_to_cell_weights                - optional
-//   cell_to_output_weights              - optional
-//
-// Quantized projection weights of size 'n_output * n_cell'
-//   projection_weight_ptr                     - optional
-//
-// Weight scales (scalars) for each of the weights above.
-//   effective_input_to_input_scale_a    - optional
-//   effective_input_to_input_scale_b    - optional
-//   effective_input_to_forget_scale_a
-//   effective_input_to_forget_scale_b
-//   effective_input_to_cell_scale_a
-//   effective_input_to_cell_scale_b
-//   effective_input_to_output_scale_a
-//   effective_input_to_output_scale_b
-//   effective_recurrent_to_input_scale_a    - optional
-//   effective_recurrent_to_input_scale_b    - optional
-//   effective_recurrent_to_forget_scale_a
-//   effective_recurrent_to_forget_scale_b
-//   effective_recurrent_to_cell_scale_a
-//   effective_recurrent_to_cell_scale_b
-//   effective_recurrent_to_output_scale_a
-//   effective_recurrent_to_output_scale_b
-//   effective_proj_scale_a                  - optional
-//   effective_proj_scale_b                  - optional
-//
-// Gate biases of size 'n_cell':
-//   input_gate_bias_ptr                 - optional
-//   forget_gate_bias_ptr
-//   cell_gate_bias_ptr
-//   output_gate_bias_ptr
-//
-// Layer norm coefficients of size 'n_cell', representing diagonal matrices.
-//   layer_norm_input_weight_ptr    - optional
-//   layer_norm_forget_weight_ptr   - optional
-//   layer_norm_cell_weight_ptr     - optional
-//   layer_norm_output_weight_ptr   - optional
-//
-// Layer norm scales of size 'n_cell'.
-//   layer_norm_input_scale_a     - optional
-//   layer_norm_input_scale_b     - optional
-//   layer_norm_forget_scale_a    - optional
-//   layer_norm_forget_scale_b    - optional
-//   layer_norm_cell_scale_a      - optional
-//   layer_norm_cell_scale_b      - optional
-//   layer_norm_output_scale_a    - optional
-//   layer_norm_output_scale_b    - optional
-//
-// Scalar values:
-//   quantized_cell_clip: quantized clip value for cell.
-//   quantized_proj_clip: quantized clip value for projection.
-//   cell_state_scale: the power of two scale for cell state.
-//
-// Zero points:
-//   input_zp: zero point for input tensor.
-//   output_state_zp: zero point of output state.
-//   hidden_zp: zero point for hidden state.
-//
-// Temporary pre-allocated storage for the calculation. Each is of size n_cell *
-// n_batch.
-//   scratch0
-//   scratch1
-//   scratch2
-//   scratch3
-//   scratch4
-//   scratch5
-//   scratch6
-//   scratch7
-//
-// Outputs:
-//   output_state_ptr - size 'n_batch * n_output'
-//   cell_state_ptr   - size 'n_batch * n_cell'
-//   output_ptr       - size 'n_batch * n_output'
-//
-// Can move zero point calculation into Prepare() for better perfomance.
-// TODO(b/159947023): scratch5 is unused, remove.
-inline void LstmStepInteger8x8_8(
-    const int8_t* input_ptr, int32_t input_zp,
-    const int8_t* input_to_input_weight_ptr,
-    int32_t effective_input_to_input_scale_a,
-    int32_t effective_input_to_input_scale_b,
-    const int8_t* input_to_forget_weight_ptr,
-    int32_t effective_input_to_forget_scale_a,
-    int32_t effective_input_to_forget_scale_b,
-    const int8_t* input_to_cell_weight_ptr,
-    int32_t effective_input_to_cell_scale_a,
-    int32_t effective_input_to_cell_scale_b,
-    const int8_t* input_to_output_weight_ptr,
-    int32_t effective_input_to_output_scale_a,
-    int32_t effective_input_to_output_scale_b,
-    const int8_t* recurrent_to_input_weight_ptr,
-    int32_t effective_recurrent_to_input_scale_a,
-    int32_t effective_recurrent_to_input_scale_b,
-    const int8_t* recurrent_to_forget_weight_ptr,
-    int32_t effective_recurrent_to_forget_scale_a,
-    int32_t effective_recurrent_to_forget_scale_b,
-    const int8_t* recurrent_to_cell_weight_ptr,
-    int32_t effective_recurrent_to_cell_scale_a,
-    int32_t effective_recurrent_to_cell_scale_b,
-    const int8_t* recurrent_to_output_weight_ptr,
-    int32_t effective_recurrent_to_output_scale_a,
-    int32_t effective_recurrent_to_output_scale_b,
-    const int8_t* cell_to_input_weight_ptr,
-    int32_t effective_cell_to_input_scale_a,
-    int32_t effective_cell_to_input_scale_b,
-    const int8_t* cell_to_forget_weight_ptr,
-    int32_t effective_cell_to_forget_scale_a,
-    int32_t effective_cell_to_forget_scale_b,
-    const int8_t* cell_to_output_weight_ptr,
-    int32_t effective_cell_to_output_scale_a,
-    int32_t effective_cell_to_output_scale_b,
-    const int8_t* projection_weight_ptr, int32_t effective_proj_scale_a,
-    int32_t effective_proj_scale_b, const int16_t* layer_norm_input_weight_ptr,
-    int32_t layer_norm_input_scale_a, int32_t layer_norm_input_scale_b,
-    const int16_t* layer_norm_forget_weight_ptr,
-    int32_t layer_norm_forget_scale_a, int32_t layer_norm_forget_scale_b,
-    const int16_t* layer_norm_cell_weight_ptr, int32_t layer_norm_cell_scale_a,
-    int32_t layer_norm_cell_scale_b,
-    const int16_t* layer_norm_output_weight_ptr,
-    int32_t layer_norm_output_scale_a, int32_t layer_norm_output_scale_b,
-    const int32_t* input_gate_bias_ptr, const int32_t* forget_gate_bias_ptr,
-    const int32_t* cell_gate_bias_ptr, const int32_t* output_gate_bias_ptr,
-    const int32_t* projection_bias_ptr, const TfLiteLSTMParams* params,
-    const int32_t* intermediate_scale_a, const int32_t* intermediate_scale_b,
-    const int32_t* intermediate_zp, int16_t quantized_cell_clip,
-    int8_t quantized_proj_clip, int n_batch, int n_cell, int n_input,
-    int n_output, int output_batch_leading_dim, int8_t* output_state_ptr,
-    int32_t output_state_zp, int16_t* cell_state_ptr, int8_t* output_ptr,
-    int8_t* scratch0, int8_t* scratch1, int16_t* scratch2, int16_t* scratch3,
-    int16_t* scratch4, int16_t* scratch5, int16_t* scratch6,
-    int16_t* scratch7) {
-  // TODO(b/159066113): scratch5 is unused, remove.
-
-  // Make named scratch buffers for the different gates.
-  int16_t* forget_gate_scratch = scratch2;
-  int16_t* cell_gate_scratch = scratch3;
-  int16_t* output_gate_scratch = scratch4;
-  // no-CIFG is not supported here
-
-  // Calculate the forget gate.
-  CalculateLstmGateInteger8x8_8(
-      input_ptr, input_zp, input_to_forget_weight_ptr,
-      effective_input_to_forget_scale_a, effective_input_to_forget_scale_b,
-      intermediate_scale_a[2], intermediate_scale_b[2], intermediate_zp[4],
-      output_state_ptr, output_state_zp, recurrent_to_forget_weight_ptr,
-      effective_recurrent_to_forget_scale_a,
-      effective_recurrent_to_forget_scale_b, intermediate_scale_a[3],
-      intermediate_scale_b[3], intermediate_zp[5], layer_norm_forget_weight_ptr,
-      layer_norm_forget_scale_a, layer_norm_forget_scale_b,
-      forget_gate_bias_ptr, n_batch, n_input, n_output, n_cell,
-      kTfLiteActSigmoid, forget_gate_scratch, scratch0, scratch1);
-  // Calculate the cell update gate.
-  CalculateLstmGateInteger8x8_8(
-      input_ptr, input_zp, input_to_cell_weight_ptr,
-      effective_input_to_cell_scale_a, effective_input_to_cell_scale_b,
-      intermediate_scale_a[4], intermediate_scale_b[4], intermediate_zp[7],
-      output_state_ptr, output_state_zp, recurrent_to_cell_weight_ptr,
-      effective_recurrent_to_cell_scale_a, effective_recurrent_to_cell_scale_b,
-      intermediate_scale_a[5], intermediate_scale_b[5], intermediate_zp[8],
-      layer_norm_cell_weight_ptr, layer_norm_cell_scale_a,
-      layer_norm_cell_scale_b, cell_gate_bias_ptr, n_batch, n_input, n_output,
-      n_cell, kTfLiteActTanh, cell_gate_scratch, scratch0, scratch1);
-  // Update the cell state.
-  UpdateLstmCellInteger(n_batch, n_cell, cell_state_ptr,
-                        /*cell_state_scale=*/-15, /*input_gate=*/nullptr,
-                        forget_gate_scratch, cell_gate_scratch,
-                        /*use_cifg=*/true, quantized_cell_clip);
-  // Calculate the output gate.
-  CalculateLstmGateInteger8x8_8(
-      input_ptr, input_zp, input_to_output_weight_ptr,
-      effective_input_to_output_scale_a, effective_input_to_output_scale_b,
-      intermediate_scale_a[6], intermediate_scale_b[6], intermediate_zp[10],
-      output_state_ptr, output_state_zp, recurrent_to_output_weight_ptr,
-      effective_recurrent_to_output_scale_a,
-      effective_recurrent_to_output_scale_b, intermediate_scale_a[11],
-      intermediate_scale_b[7], intermediate_zp[7], layer_norm_output_weight_ptr,
-      layer_norm_output_scale_a, layer_norm_output_scale_b,
-      output_gate_bias_ptr, n_batch, n_input, n_output, n_cell,
-      kTfLiteActSigmoid, output_gate_scratch, scratch0, scratch1);
-  // Update the output state.
-  CalculateLstmOutputInteger8x8_8(
-      n_batch, n_cell, n_output, cell_state_ptr, output_gate_scratch,
-      projection_weight_ptr, effective_proj_scale_a, effective_proj_scale_b,
-      projection_bias_ptr, output_state_zp, quantized_proj_clip,
-      output_state_ptr, scratch2);
-  // Copy output state to the output. Note that unlike float or hybrid, output
-  // is always contigous.
-  std::memcpy(output_ptr, output_state_ptr,
-              n_batch * n_output * sizeof(int8_t));
-}
-
-}  // namespace
-
-TfLiteStatus EvalFloatLstm(
-    const TfLiteEvalTensor* input,
-    const TfLiteEvalTensor* input_to_input_weights,
-    const TfLiteEvalTensor* input_to_forget_weights,
-    const TfLiteEvalTensor* input_to_cell_weights,
-    const TfLiteEvalTensor* input_to_output_weights,
-    const TfLiteEvalTensor* recurrent_to_input_weights,
-    const TfLiteEvalTensor* recurrent_to_forget_weights,
-    const TfLiteEvalTensor* recurrent_to_cell_weights,
-    const TfLiteEvalTensor* recurrent_to_output_weights,
-    const TfLiteEvalTensor* cell_to_input_weights,
-    const TfLiteEvalTensor* cell_to_forget_weights,
-    const TfLiteEvalTensor* cell_to_output_weights,
-    const TfLiteEvalTensor* input_layer_norm_coefficients,
-    const TfLiteEvalTensor* forget_layer_norm_coefficients,
-    const TfLiteEvalTensor* cell_layer_norm_coefficients,
-    const TfLiteEvalTensor* output_layer_norm_coefficients,
-    const TfLiteEvalTensor* aux_input,
-    const TfLiteEvalTensor* aux_input_to_input_weights,
-    const TfLiteEvalTensor* aux_input_to_forget_weights,
-    const TfLiteEvalTensor* aux_input_to_cell_weights,
-    const TfLiteEvalTensor* aux_input_to_output_weights,
-    const TfLiteEvalTensor* input_gate_bias,
-    const TfLiteEvalTensor* forget_gate_bias,
-    const TfLiteEvalTensor* cell_gate_bias,
-    const TfLiteEvalTensor* output_gate_bias,
-    const TfLiteEvalTensor* projection_weights,
-    const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params,
-    bool forward_sequence, bool time_major, int output_offset,
-    float* scratch_buffer, TfLiteEvalTensor* output_state,
-    TfLiteEvalTensor* cell_state, TfLiteEvalTensor* output) {
-  TFLITE_DCHECK(input->dims->size >= 2 && input->dims->size <= 3);
-  int max_time, n_batch;
-  if (input->dims->size == 3) {
-    max_time = (time_major) ? input->dims->data[0] : input->dims->data[1];
-    n_batch = (time_major) ? input->dims->data[1] : input->dims->data[0];
-  } else {
-    max_time = 1;
-    n_batch = input->dims->data[0];
-  }
-  const int n_input = input->dims->data[input->dims->size - 1];
-  const int aux_input_size =
-      (aux_input) ? aux_input->dims->data[aux_input->dims->size - 1] : 0;
-
-  // n_cell and n_output will be the same size when there is no projection.
-  const int n_cell = input_to_output_weights->dims->data[0];
-  const int n_output = recurrent_to_output_weights->dims->data[1];
-
-  // Since we have already checked that weights are all there or none, we can
-  // check the existence of only one to the get the condition.
-  const bool use_cifg = (input_to_input_weights == nullptr);
-
-  // Index the scratch buffers pointers to the global scratch buffer.
-  float* input_gate_scratch = nullptr;
-  float* cell_gate_scratch = nullptr;
-  float* forget_gate_scratch = nullptr;
-  float* output_gate_scratch = nullptr;
-  if (use_cifg) {
-    cell_gate_scratch = scratch_buffer;
-    forget_gate_scratch = scratch_buffer + n_cell * n_batch;
-    output_gate_scratch = scratch_buffer + 2 * n_cell * n_batch;
-  } else {
-    input_gate_scratch = scratch_buffer;
-    cell_gate_scratch = scratch_buffer + n_cell * n_batch;
-    forget_gate_scratch = scratch_buffer + 2 * n_cell * n_batch;
-    output_gate_scratch = scratch_buffer + 3 * n_cell * n_batch;
-  }
-
-  const int output_batch_leading_dim =
-      output->dims->data[output->dims->size - 1];
-  if (time_major) {
-    // Loop through the sequence.
-    const int input_step = n_batch * n_input;
-    const int output_step = n_batch * output_batch_leading_dim;
-    for (int t = 0; t < max_time; t++) {
-      // If this is the forward_sequence, step forward, otherwise step
-      // backwards.
-      const int t_rel = forward_sequence ? t : max_time - t - 1;
-      const float* input_ptr =
-          tflite::micro::GetTensorData<float>(input) + t_rel * input_step;
-      const float* aux_input_ptr = nullptr;
-      if (aux_input) {
-        aux_input_ptr =
-            tflite::micro::GetTensorData<float>(aux_input) + t_rel * input_step;
-      }
-      float* output_ptr = tflite::micro::GetTensorData<float>(output) +
-                          t_rel * output_step + output_offset;
-
-      LstmStepFloat(
-          input_ptr,
-          input_to_input_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(input_to_input_weights),
-          input_to_forget_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(input_to_forget_weights),
-          input_to_cell_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(input_to_cell_weights),
-          input_to_output_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(input_to_output_weights),
-          aux_input_ptr,
-          aux_input_to_input_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(aux_input_to_input_weights),
-          aux_input_to_forget_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(
-                    aux_input_to_forget_weights),
-          aux_input_to_cell_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(aux_input_to_cell_weights),
-          aux_input_to_output_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(
-                    aux_input_to_output_weights),
-          recurrent_to_input_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(recurrent_to_input_weights),
-          recurrent_to_forget_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(
-                    recurrent_to_forget_weights),
-          recurrent_to_cell_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(recurrent_to_cell_weights),
-          recurrent_to_output_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(
-                    recurrent_to_output_weights),
-          cell_to_input_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(cell_to_input_weights),
-          cell_to_forget_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(cell_to_forget_weights),
-          cell_to_output_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(cell_to_output_weights),
-          input_layer_norm_coefficients == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(
-                    input_layer_norm_coefficients),
-          forget_layer_norm_coefficients == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(
-                    forget_layer_norm_coefficients),
-          cell_layer_norm_coefficients == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(
-                    cell_layer_norm_coefficients),
-          output_layer_norm_coefficients == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(
-                    output_layer_norm_coefficients),
-          input_gate_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(input_gate_bias),
-          forget_gate_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(forget_gate_bias),
-          cell_gate_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(cell_gate_bias),
-          output_gate_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(output_gate_bias),
-          projection_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(projection_weights),
-          projection_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(projection_bias),
-          params, n_batch, n_cell, n_input, aux_input_size, n_output,
-          output_batch_leading_dim,
-          tflite::micro::GetTensorData<float>(output_state),
-          tflite::micro::GetTensorData<float>(cell_state), input_gate_scratch,
-          forget_gate_scratch, cell_gate_scratch, output_gate_scratch,
-          output_ptr);
-    }
-  } else {
-    for (int b = 0; b < n_batch; b++) {
-      const int input_step = n_input;
-      const int output_step = output_batch_leading_dim;
-      for (int t = 0; t < max_time; t++) {
-        // If this is the forward_sequence, step forward, otherwise step
-        // backwards.
-        const int t_rel = forward_sequence ? t : max_time - t - 1;
-        const int time_offset = b * max_time + t_rel;
-        const float* input_ptr = tflite::micro::GetTensorData<float>(input) +
-                                 time_offset * input_step;
-        const float* aux_input_ptr = nullptr;
-        if (aux_input) {
-          aux_input_ptr = tflite::micro::GetTensorData<float>(aux_input) +
-                          time_offset * input_step;
-        }
-        float* output_ptr = tflite::micro::GetTensorData<float>(output) +
-                            time_offset * output_step + output_offset;
-
-        // Offset the {output,cell}_state pointers to the right batch.
-        float* output_state_ptr =
-            tflite::micro::GetTensorData<float>(output_state) +
-            b * output_batch_leading_dim;
-        float* cell_state_ptr =
-            tflite::micro::GetTensorData<float>(cell_state) + b * n_cell;
-        // Offset the scratch pointers to the right batch.
-        float* input_gate_scratch_ptr =
-            input_gate_scratch ? input_gate_scratch + b * n_cell : nullptr;
-        float* forget_gate_scratch_ptr = forget_gate_scratch + b * n_cell;
-        float* cell_gate_scratch_ptr = cell_gate_scratch + b * n_cell;
-        float* output_gate_scratch_ptr = output_gate_scratch + b * n_cell;
-
-        LstmStepFloat(
-            input_ptr,
-            input_to_input_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(input_to_input_weights),
-            input_to_forget_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(input_to_forget_weights),
-            input_to_cell_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(input_to_cell_weights),
-            input_to_output_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(input_to_output_weights),
-            aux_input_ptr,
-            aux_input_to_input_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      aux_input_to_input_weights),
-            aux_input_to_forget_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      aux_input_to_forget_weights),
-            aux_input_to_cell_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      aux_input_to_cell_weights),
-            aux_input_to_output_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      aux_input_to_output_weights),
-            recurrent_to_input_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      recurrent_to_input_weights),
-            recurrent_to_forget_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      recurrent_to_forget_weights),
-            recurrent_to_cell_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      recurrent_to_cell_weights),
-            recurrent_to_output_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      recurrent_to_output_weights),
-            cell_to_input_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(cell_to_input_weights),
-            cell_to_forget_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(cell_to_forget_weights),
-            cell_to_output_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(cell_to_output_weights),
-            input_layer_norm_coefficients == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      input_layer_norm_coefficients),
-            forget_layer_norm_coefficients == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      forget_layer_norm_coefficients),
-            cell_layer_norm_coefficients == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      cell_layer_norm_coefficients),
-            output_layer_norm_coefficients == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      output_layer_norm_coefficients),
-            input_gate_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(input_gate_bias),
-            forget_gate_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(forget_gate_bias),
-            cell_gate_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(cell_gate_bias),
-            output_gate_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(output_gate_bias),
-            projection_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(projection_weights),
-            projection_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(projection_bias),
-            params,
-            /*n_batch=*/1, n_cell, n_input, aux_input_size, n_output,
-            output_batch_leading_dim, output_state_ptr, cell_state_ptr,
-            input_gate_scratch_ptr, forget_gate_scratch_ptr,
-            cell_gate_scratch_ptr, output_gate_scratch_ptr, output_ptr);
-      }
-    }
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus EvalHybridLstm(
-    const HybridLstmScales* hybrid_lstm_scales, const TfLiteEvalTensor* input,
-    const TfLiteEvalTensor* input_to_input_weights,
-    const TfLiteEvalTensor* input_to_input_weights_ledger,
-    const TfLiteEvalTensor* input_to_forget_weights,
-    const TfLiteEvalTensor* input_to_forget_weights_ledger,
-    const TfLiteEvalTensor* input_to_cell_weights,
-    const TfLiteEvalTensor* input_to_cell_weights_ledger,
-    const TfLiteEvalTensor* input_to_output_weights,
-    const TfLiteEvalTensor* input_to_output_weights_ledger,
-    const TfLiteEvalTensor* recurrent_to_input_weights,
-    const TfLiteEvalTensor* recurrent_to_input_weights_ledger,
-    const TfLiteEvalTensor* recurrent_to_forget_weights,
-    const TfLiteEvalTensor* recurrent_to_forget_weights_ledger,
-    const TfLiteEvalTensor* recurrent_to_cell_weights,
-    const TfLiteEvalTensor* recurrent_to_cell_weights_ledger,
-    const TfLiteEvalTensor* recurrent_to_output_weights,
-    const TfLiteEvalTensor* recurrent_to_output_weights_ledger,
-    const TfLiteEvalTensor* cell_to_input_weights,
-    const TfLiteEvalTensor* cell_to_forget_weights,
-    const TfLiteEvalTensor* cell_to_output_weights,
-    const TfLiteEvalTensor* input_layer_norm_coefficients,
-    const TfLiteEvalTensor* forget_layer_norm_coefficients,
-    const TfLiteEvalTensor* cell_layer_norm_coefficients,
-    const TfLiteEvalTensor* output_layer_norm_coefficients,
-    const TfLiteEvalTensor* aux_input,
-    const TfLiteEvalTensor* aux_input_to_input_weights,
-    const TfLiteEvalTensor* aux_input_to_forget_weights,
-    const TfLiteEvalTensor* aux_input_to_cell_weights,
-    const TfLiteEvalTensor* aux_input_to_output_weights,
-    const TfLiteEvalTensor* input_gate_bias,
-    const TfLiteEvalTensor* forget_gate_bias,
-    const TfLiteEvalTensor* cell_gate_bias,
-    const TfLiteEvalTensor* output_gate_bias,
-    const TfLiteEvalTensor* projection_weights,
-    const TfLiteEvalTensor* projection_weights_ledger,
-    const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params,
-    bool forward_sequence, bool time_major, int output_offset,
-    float* scratch_buffer, float* input_sf, float* aux_input_sf,
-    float* output_state_sf, float* prod_scaling_factors,
-    float* recovered_cell_weights, int8_t* input_quantized,
-    int8_t* aux_input_quantized, int8_t* output_state_quantized,
-    int8_t* cell_state_quantized, float* scales, TfLiteEvalTensor* output_state,
-    TfLiteEvalTensor* cell_state, int32_t* output_scratch_buffer,
-    TfLiteEvalTensor* output, int32_t* input_zp, int32_t* aux_input_zp,
-    int32_t* output_state_zp, int32_t* row_sums, int row_sums_size,
-    bool* compute_row_sums) {
-  TFLITE_DCHECK(input->dims->size >= 2 && input->dims->size <= 3);
-  const int n_input = input->dims->data[input->dims->size - 1];
-  int max_time, n_batch;
-  if (input->dims->size == 2) {
-    max_time = 1;
-    n_batch = input->dims->data[0];
-  } else {
-    max_time = (time_major) ? input->dims->data[0] : input->dims->data[1];
-    n_batch = (time_major) ? input->dims->data[1] : input->dims->data[0];
-  }
-  const int aux_input_size =
-      (aux_input) ? aux_input->dims->data[aux_input->dims->size - 1] : 0;
-  // n_cell and n_output will be the same size when there is no projection.
-  const int n_cell = input_to_output_weights->dims->data[0];
-  const int n_output = recurrent_to_output_weights->dims->data[1];
-
-  // Since we have already checked that weights are all there or none, we can
-  // check the existence of only one to get the condition.
-  const bool use_cifg = (input_to_input_weights == nullptr);
-
-  float* input_gate_scratch = nullptr;
-  float* cell_gate_scratch = nullptr;
-  float* forget_gate_scratch = nullptr;
-  float* output_gate_scratch = nullptr;
-  if (use_cifg) {
-    cell_gate_scratch = scratch_buffer;
-    forget_gate_scratch = scratch_buffer + n_cell * n_batch;
-    output_gate_scratch = scratch_buffer + 2 * n_cell * n_batch;
-  } else {
-    input_gate_scratch = scratch_buffer;
-    cell_gate_scratch = scratch_buffer + n_cell * n_batch;
-    forget_gate_scratch = scratch_buffer + 2 * n_cell * n_batch;
-    output_gate_scratch = scratch_buffer + 3 * n_cell * n_batch;
-  }
-
-  const int output_batch_leading_dim =
-      output->dims->data[output->dims->size - 1];
-
-  int32_t* input_zp_ptr = nullptr;
-  int32_t* aux_input_zp_ptr = nullptr;
-  int32_t* output_state_zp_ptr = nullptr;
-  int32_t* row_sums_ptr = nullptr;
-  if (params->asymmetric_quantize_inputs) {
-    input_zp_ptr = input_zp;
-    aux_input_zp_ptr = aux_input_zp;
-    output_state_zp_ptr = output_state_zp;
-    row_sums_ptr = row_sums;
-  }
-
-  if (time_major) {
-    // Feed the sequence into the LSTM step-by-step.
-    const int input_step = n_batch * n_input;
-    const int output_step = n_batch * output_batch_leading_dim;
-    for (int t = 0; t < max_time; t++) {
-      // If this is the forward_sequence, step forward, otherwise step
-      // backwards.
-      const int t_rel = forward_sequence ? t : max_time - t - 1;
-      const float* input_ptr =
-          tflite::micro::GetTensorData<float>(input) + t_rel * input_step;
-      const float* aux_input_ptr = nullptr;
-      if (aux_input) {
-        aux_input_ptr =
-            tflite::micro::GetTensorData<float>(aux_input) + t_rel * input_step;
-      }
-      float* output_ptr = tflite::micro::GetTensorData<float>(output) +
-                          t_rel * output_step + output_offset;
-      LstmStepHybrid(
-          input_ptr,
-          input_to_input_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(input_to_input_weights),
-          input_to_input_weights_ledger == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<uint8_t>(
-                    input_to_input_weights_ledger),
-          hybrid_lstm_scales->input_to_input_weights_scale,
-          input_to_forget_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(input_to_forget_weights),
-          input_to_forget_weights_ledger == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<uint8_t>(
-                    input_to_forget_weights_ledger),
-          hybrid_lstm_scales->input_to_forget_weights_scale,
-          input_to_cell_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(input_to_cell_weights),
-          input_to_cell_weights_ledger == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<uint8_t>(
-                    input_to_cell_weights_ledger),
-          hybrid_lstm_scales->input_to_cell_weights_scale,
-          input_to_output_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(input_to_output_weights),
-          input_to_output_weights_ledger == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<uint8_t>(
-                    input_to_output_weights_ledger),
-          hybrid_lstm_scales->input_to_output_weights_scale, aux_input_ptr,
-          aux_input_to_input_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(
-                    aux_input_to_input_weights),
-          hybrid_lstm_scales->aux_input_to_input_weights_scale,
-          aux_input_to_forget_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(
-                    aux_input_to_forget_weights),
-          hybrid_lstm_scales->aux_input_to_forget_weights_scale,
-          aux_input_to_cell_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(aux_input_to_cell_weights),
-          hybrid_lstm_scales->aux_input_to_cell_weights_scale,
-          aux_input_to_output_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(
-                    aux_input_to_output_weights),
-          hybrid_lstm_scales->aux_input_to_output_weights_scale,
-          recurrent_to_input_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(
-                    recurrent_to_input_weights),
-          recurrent_to_input_weights_ledger == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<uint8_t>(
-                    recurrent_to_input_weights_ledger),
-          hybrid_lstm_scales->recurrent_to_input_weights_scale,
-          recurrent_to_forget_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(
-                    recurrent_to_forget_weights),
-          recurrent_to_forget_weights_ledger == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<uint8_t>(
-                    recurrent_to_forget_weights_ledger),
-          hybrid_lstm_scales->recurrent_to_forget_weights_scale,
-          recurrent_to_cell_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(recurrent_to_cell_weights),
-          recurrent_to_cell_weights_ledger == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<uint8_t>(
-                    recurrent_to_cell_weights_ledger),
-          hybrid_lstm_scales->recurrent_to_cell_weights_scale,
-          recurrent_to_output_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(
-                    recurrent_to_output_weights),
-          recurrent_to_output_weights_ledger == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<uint8_t>(
-                    recurrent_to_output_weights_ledger),
-          hybrid_lstm_scales->recurrent_to_output_weights_scale,
-          cell_to_input_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(cell_to_input_weights),
-          hybrid_lstm_scales->cell_to_input_weights_scale,
-          cell_to_forget_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(cell_to_forget_weights),
-          hybrid_lstm_scales->cell_to_forget_weights_scale,
-          cell_to_output_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(cell_to_output_weights),
-          hybrid_lstm_scales->cell_to_output_weights_scale,
-          input_layer_norm_coefficients == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(
-                    input_layer_norm_coefficients),
-          forget_layer_norm_coefficients == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(
-                    forget_layer_norm_coefficients),
-          cell_layer_norm_coefficients == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(
-                    cell_layer_norm_coefficients),
-          output_layer_norm_coefficients == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(
-                    output_layer_norm_coefficients),
-          input_gate_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(input_gate_bias),
-          forget_gate_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(forget_gate_bias),
-          cell_gate_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(cell_gate_bias),
-          output_gate_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(output_gate_bias),
-          projection_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(projection_weights),
-          projection_weights_ledger == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<uint8_t>(
-                    projection_weights_ledger),
-          hybrid_lstm_scales->projection_weights_scale,
-          projection_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<float>(projection_bias),
-          params, n_batch, n_cell, n_input, aux_input_size, n_output,
-          output_batch_leading_dim, input_gate_scratch, forget_gate_scratch,
-          cell_gate_scratch, output_gate_scratch, scales, input_sf,
-          aux_input_sf, output_state_sf, prod_scaling_factors,
-          recovered_cell_weights, input_quantized, aux_input_quantized,
-          output_state_quantized, cell_state_quantized,
-          tflite::micro::GetTensorData<float>(output_state),
-          tflite::micro::GetTensorData<float>(cell_state),
-          output_scratch_buffer, output_ptr, input_zp_ptr, aux_input_zp_ptr,
-          output_state_zp_ptr, row_sums_ptr, row_sums_size, compute_row_sums,
-          params->asymmetric_quantize_inputs);
-    }
-  } else {
-    for (int b = 0; b < n_batch; b++) {
-      const int input_step = n_input;
-      const int output_step = output_batch_leading_dim;
-      for (int t = 0; t < max_time; t++) {
-        // If this is the forward_sequence, step forward, otherwise step
-        // backwards.
-        const int t_rel = forward_sequence ? t : max_time - t - 1;
-        const int time_offset = b * max_time + t_rel;
-        const float* input_ptr = tflite::micro::GetTensorData<float>(input) +
-                                 time_offset * input_step;
-        const float* aux_input_ptr = nullptr;
-        if (aux_input) {
-          aux_input_ptr = tflite::micro::GetTensorData<float>(aux_input) +
-                          time_offset * input_step;
-        }
-        float* output_ptr = tflite::micro::GetTensorData<float>(output) +
-                            time_offset * output_step + output_offset;
-
-        // Offset the {output,cell}_state pointers to the right batch.
-        float* output_state_ptr =
-            tflite::micro::GetTensorData<float>(output_state) +
-            b * output_batch_leading_dim;
-        float* cell_state_ptr =
-            tflite::micro::GetTensorData<float>(cell_state) + b * n_cell;
-        // Offset the scratch pointers to the right batch.
-        float* input_gate_scratch_ptr =
-            input_gate_scratch ? input_gate_scratch + b * n_cell : nullptr;
-        float* forget_gate_scratch_ptr = forget_gate_scratch + b * n_cell;
-        float* cell_gate_scratch_ptr = cell_gate_scratch + b * n_cell;
-        float* output_gate_scratch_ptr = output_gate_scratch + b * n_cell;
-
-        LstmStepHybrid(
-            input_ptr,
-            input_to_input_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(input_to_input_weights),
-            input_to_input_weights_ledger == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<uint8_t>(
-                      input_to_input_weights_ledger),
-            hybrid_lstm_scales->input_to_input_weights_scale,
-            input_to_forget_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(input_to_forget_weights),
-            input_to_forget_weights_ledger == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<uint8_t>(
-                      input_to_forget_weights_ledger),
-            hybrid_lstm_scales->input_to_forget_weights_scale,
-            input_to_cell_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(input_to_cell_weights),
-            input_to_cell_weights_ledger == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<uint8_t>(
-                      input_to_cell_weights_ledger),
-            hybrid_lstm_scales->input_to_cell_weights_scale,
-            input_to_output_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(input_to_output_weights),
-            input_to_output_weights_ledger == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<uint8_t>(
-                      input_to_output_weights_ledger),
-            hybrid_lstm_scales->input_to_output_weights_scale, aux_input_ptr,
-            aux_input_to_input_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(
-                      aux_input_to_input_weights),
-            hybrid_lstm_scales->aux_input_to_input_weights_scale,
-            aux_input_to_forget_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(
-                      aux_input_to_forget_weights),
-            hybrid_lstm_scales->aux_input_to_forget_weights_scale,
-            aux_input_to_cell_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(
-                      aux_input_to_cell_weights),
-            hybrid_lstm_scales->aux_input_to_cell_weights_scale,
-            aux_input_to_output_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(
-                      aux_input_to_output_weights),
-            hybrid_lstm_scales->aux_input_to_output_weights_scale,
-            recurrent_to_input_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(
-                      recurrent_to_input_weights),
-            recurrent_to_input_weights_ledger == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<uint8_t>(
-                      recurrent_to_input_weights_ledger),
-            hybrid_lstm_scales->recurrent_to_input_weights_scale,
-            recurrent_to_forget_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(
-                      recurrent_to_forget_weights),
-            recurrent_to_forget_weights_ledger == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<uint8_t>(
-                      recurrent_to_forget_weights_ledger),
-            hybrid_lstm_scales->recurrent_to_forget_weights_scale,
-            recurrent_to_cell_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(
-                      recurrent_to_cell_weights),
-            recurrent_to_cell_weights_ledger == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<uint8_t>(
-                      recurrent_to_cell_weights_ledger),
-            hybrid_lstm_scales->recurrent_to_cell_weights_scale,
-            recurrent_to_output_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(
-                      recurrent_to_output_weights),
-            recurrent_to_output_weights_ledger == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<uint8_t>(
-                      recurrent_to_output_weights_ledger),
-            hybrid_lstm_scales->recurrent_to_output_weights_scale,
-            cell_to_input_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(cell_to_input_weights),
-            hybrid_lstm_scales->cell_to_input_weights_scale,
-            cell_to_forget_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(cell_to_forget_weights),
-            hybrid_lstm_scales->cell_to_forget_weights_scale,
-            cell_to_output_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(cell_to_output_weights),
-            hybrid_lstm_scales->cell_to_output_weights_scale,
-            input_layer_norm_coefficients == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      input_layer_norm_coefficients),
-            forget_layer_norm_coefficients == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      forget_layer_norm_coefficients),
-            cell_layer_norm_coefficients == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      cell_layer_norm_coefficients),
-            output_layer_norm_coefficients == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(
-                      output_layer_norm_coefficients),
-            input_gate_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(input_gate_bias),
-            forget_gate_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(forget_gate_bias),
-            cell_gate_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(cell_gate_bias),
-            output_gate_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(output_gate_bias),
-            projection_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(projection_weights),
-            projection_weights_ledger == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<uint8_t>(
-                      projection_weights_ledger),
-            hybrid_lstm_scales->projection_weights_scale,
-            projection_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<float>(projection_bias),
-            params,
-            /*n_batch=*/1, n_cell, n_input, aux_input_size, n_output,
-            output_batch_leading_dim, input_gate_scratch_ptr,
-            forget_gate_scratch_ptr, cell_gate_scratch_ptr,
-            output_gate_scratch_ptr, scales, input_sf, aux_input_sf,
-            output_state_sf, prod_scaling_factors, recovered_cell_weights,
-            input_quantized, aux_input_quantized, output_state_quantized,
-            cell_state_quantized, output_state_ptr, cell_state_ptr,
-            output_scratch_buffer, output_ptr, input_zp_ptr, aux_input_zp_ptr,
-            output_state_zp_ptr, row_sums_ptr, row_sums_size, compute_row_sums,
-            params->asymmetric_quantize_inputs);
-      }
-    }
-  }
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus EvalInteger8x8_16Lstm(
-    const TfLiteEvalTensor* input,
-    const TfLiteEvalTensor* input_to_input_weights,
-    const TfLiteEvalTensor* input_to_forget_weights,
-    const TfLiteEvalTensor* input_to_cell_weights,
-    const TfLiteEvalTensor* input_to_output_weights,
-    const TfLiteEvalTensor* recurrent_to_input_weights,
-    const TfLiteEvalTensor* recurrent_to_forget_weights,
-    const TfLiteEvalTensor* recurrent_to_cell_weights,
-    const TfLiteEvalTensor* recurrent_to_output_weights,
-    const TfLiteEvalTensor* cell_to_input_weights,
-    const TfLiteEvalTensor* cell_to_forget_weights,
-    const TfLiteEvalTensor* cell_to_output_weights,
-    const TfLiteEvalTensor* input_layer_norm_coefficients,
-    const TfLiteEvalTensor* forget_layer_norm_coefficients,
-    const TfLiteEvalTensor* cell_layer_norm_coefficients,
-    const TfLiteEvalTensor* output_layer_norm_coefficients,
-    const TfLiteEvalTensor* input_gate_bias,
-    const TfLiteEvalTensor* forget_gate_bias,
-    const TfLiteEvalTensor* cell_gate_bias,
-    const TfLiteEvalTensor* output_gate_bias,
-    const TfLiteEvalTensor* projection_weights,
-    const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params,
-    bool forward_sequence, bool time_major,
-    const IntegerLstmParameter* integer_lstm_param, int32_t output_state_zp,
-    TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state,
-    TfLiteEvalTensor* output, int16_t* scratch0, int16_t* scratch1,
-    int16_t* scratch2, int16_t* scratch3, int8_t* scratch4, int32_t* scratch5) {
-  TFLITE_DCHECK(input->dims->size >= 2 && input->dims->size <= 3);
-  const int n_input = input->dims->data[input->dims->size - 1];
-  int max_time, n_batch;
-  if (input->dims->size == 2) {
-    max_time = 1;
-    n_batch = input->dims->data[0];
-  } else {
-    max_time = (time_major) ? input->dims->data[0] : input->dims->data[1];
-    n_batch = (time_major) ? input->dims->data[1] : input->dims->data[0];
-  }
-
-  // n_cell and n_output will be the same size when there is no projection.
-  const int n_cell = input_to_output_weights->dims->data[0];
-  const int n_output = recurrent_to_output_weights->dims->data[1];
-
-  // Get params for time/batch/sequence.
-  const int output_batch_leading_dim =
-      output->dims->data[output->dims->size - 1];
-
-  if (time_major) {
-    const int input_step = n_batch * n_input;
-    const int output_step = n_batch * output_batch_leading_dim;
-    for (int t = 0; t < max_time; t++) {
-      const int t_rel = t;
-      int8_t* output_ptr =
-          tflite::micro::GetTensorData<int8_t>(output) + t_rel * output_step;
-      const int8_t* input_ptr =
-          tflite::micro::GetTensorData<int8_t>(input) + t_rel * input_step;
-      LstmStepInteger8x8_16(
-          input_ptr,
-          input_to_input_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(input_to_input_weights),
-          integer_lstm_param->effective_input_to_input_scale_a,
-          integer_lstm_param->effective_input_to_input_scale_b,
-          input_to_forget_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(input_to_forget_weights),
-          integer_lstm_param->effective_input_to_forget_scale_a,
-          integer_lstm_param->effective_input_to_forget_scale_b,
-          input_to_cell_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(input_to_cell_weights),
-          integer_lstm_param->effective_input_to_cell_scale_a,
-          integer_lstm_param->effective_input_to_cell_scale_b,
-          input_to_output_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(input_to_output_weights),
-          integer_lstm_param->effective_input_to_output_scale_a,
-          integer_lstm_param->effective_input_to_output_scale_b,
-          recurrent_to_input_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(
-                    recurrent_to_input_weights),
-          integer_lstm_param->effective_recurrent_to_input_scale_a,
-          integer_lstm_param->effective_recurrent_to_input_scale_b,
-          recurrent_to_forget_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(
-                    recurrent_to_forget_weights),
-          integer_lstm_param->effective_recurrent_to_forget_scale_a,
-          integer_lstm_param->effective_recurrent_to_forget_scale_b,
-          recurrent_to_cell_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(recurrent_to_cell_weights),
-          integer_lstm_param->effective_recurrent_to_cell_scale_a,
-          integer_lstm_param->effective_recurrent_to_cell_scale_b,
-          recurrent_to_output_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(
-                    recurrent_to_output_weights),
-          integer_lstm_param->effective_recurrent_to_output_scale_a,
-          integer_lstm_param->effective_recurrent_to_output_scale_b,
-          cell_to_input_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int16_t>(cell_to_input_weights),
-          integer_lstm_param->effective_cell_to_input_scale_a,
-          integer_lstm_param->effective_cell_to_input_scale_b,
-          cell_to_forget_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int16_t>(cell_to_forget_weights),
-          integer_lstm_param->effective_cell_to_forget_scale_a,
-          integer_lstm_param->effective_cell_to_forget_scale_b,
-          cell_to_output_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int16_t>(cell_to_output_weights),
-          integer_lstm_param->effective_cell_to_output_scale_a,
-          integer_lstm_param->effective_cell_to_output_scale_b,
-          projection_weights == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int8_t>(projection_weights),
-          integer_lstm_param->effective_proj_scale_a,
-          integer_lstm_param->effective_proj_scale_b,
-          integer_lstm_param->hidden_zp,
-          integer_lstm_param->effective_hidden_scale_a,
-          integer_lstm_param->effective_hidden_scale_b,
-          input_layer_norm_coefficients == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int16_t>(
-                    input_layer_norm_coefficients),
-          integer_lstm_param->layer_norm_input_scale_a,
-          integer_lstm_param->layer_norm_input_scale_b,
-          forget_layer_norm_coefficients == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int16_t>(
-                    forget_layer_norm_coefficients),
-          integer_lstm_param->layer_norm_forget_scale_a,
-          integer_lstm_param->layer_norm_forget_scale_b,
-          cell_layer_norm_coefficients == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int16_t>(
-                    cell_layer_norm_coefficients),
-          integer_lstm_param->layer_norm_cell_scale_a,
-          integer_lstm_param->layer_norm_cell_scale_b,
-          output_layer_norm_coefficients == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int16_t>(
-                    output_layer_norm_coefficients),
-          integer_lstm_param->layer_norm_output_scale_a,
-          integer_lstm_param->layer_norm_output_scale_b,
-          input_gate_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int32_t>(input_gate_bias),
-          forget_gate_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int32_t>(forget_gate_bias),
-          cell_gate_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int32_t>(cell_gate_bias),
-          output_gate_bias == nullptr
-              ? nullptr
-              : tflite::micro::GetTensorData<int32_t>(output_gate_bias),
-          integer_lstm_param->quantized_cell_clip,
-          integer_lstm_param->quantized_proj_clip,
-          integer_lstm_param->cell_scale,
-          integer_lstm_param->input_variance_guard,
-          integer_lstm_param->forget_variance_guard,
-          integer_lstm_param->cell_variance_guard,
-          integer_lstm_param->output_variance_guard,
-          integer_lstm_param->input_to_forget_effective_bias,
-          integer_lstm_param->recurrent_to_forget_effective_bias,
-          integer_lstm_param->input_to_cell_effective_bias,
-          integer_lstm_param->recurrent_to_cell_effective_bias,
-          integer_lstm_param->input_to_output_effective_bias,
-          integer_lstm_param->recurrent_to_output_effective_bias,
-          integer_lstm_param->input_to_input_effective_bias,
-          integer_lstm_param->recurrent_to_input_effective_bias,
-          integer_lstm_param->projection_effective_bias, n_batch, n_cell,
-          n_input, n_output, tflite::micro::GetTensorData<int8_t>(output_state),
-          output_state_zp, tflite::micro::GetTensorData<int16_t>(cell_state),
-          output_ptr, scratch0, scratch1, scratch2, scratch3, scratch4,
-          scratch5);
-    }
-  } else {
-    for (int b = 0; b < n_batch; b++) {
-      const int input_step = n_input;
-      const int output_step = output_batch_leading_dim;
-      for (int t = 0; t < max_time; t++) {
-        // If this is the forward_sequence, step forward, otherwise step
-        // backwards.
-        const int t_rel = forward_sequence ? t : max_time - t - 1;
-        const int time_offset = b * max_time + t_rel;
-        const int8_t* input_ptr = tflite::micro::GetTensorData<int8_t>(input) +
-                                  time_offset * input_step;
-        int8_t* output_ptr = tflite::micro::GetTensorData<int8_t>(output) +
-                             time_offset * output_step;
-
-        // Offset the {output,cell}_state pointers to the right batch.
-        int8_t* output_state_ptr =
-            tflite::micro::GetTensorData<int8_t>(output_state) +
-            b * output_batch_leading_dim;
-        int16_t* cell_state_ptr =
-            tflite::micro::GetTensorData<int16_t>(cell_state) + b * n_cell;
-
-        LstmStepInteger8x8_16(
-            input_ptr,
-            input_to_input_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(input_to_input_weights),
-            integer_lstm_param->effective_input_to_input_scale_a,
-            integer_lstm_param->effective_input_to_input_scale_b,
-            input_to_forget_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(input_to_forget_weights),
-            integer_lstm_param->effective_input_to_forget_scale_a,
-            integer_lstm_param->effective_input_to_forget_scale_b,
-            input_to_cell_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(input_to_cell_weights),
-            integer_lstm_param->effective_input_to_cell_scale_a,
-            integer_lstm_param->effective_input_to_cell_scale_b,
-            input_to_output_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(input_to_output_weights),
-            integer_lstm_param->effective_input_to_output_scale_a,
-            integer_lstm_param->effective_input_to_output_scale_b,
-            recurrent_to_input_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(
-                      recurrent_to_input_weights),
-            integer_lstm_param->effective_recurrent_to_input_scale_a,
-            integer_lstm_param->effective_recurrent_to_input_scale_b,
-            recurrent_to_forget_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(
-                      recurrent_to_forget_weights),
-            integer_lstm_param->effective_recurrent_to_forget_scale_a,
-            integer_lstm_param->effective_recurrent_to_forget_scale_b,
-            recurrent_to_cell_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(
-                      recurrent_to_cell_weights),
-            integer_lstm_param->effective_recurrent_to_cell_scale_a,
-            integer_lstm_param->effective_recurrent_to_cell_scale_b,
-            recurrent_to_output_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(
-                      recurrent_to_output_weights),
-            integer_lstm_param->effective_recurrent_to_output_scale_a,
-            integer_lstm_param->effective_recurrent_to_output_scale_b,
-            cell_to_input_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int16_t>(cell_to_input_weights),
-            integer_lstm_param->effective_cell_to_input_scale_a,
-            integer_lstm_param->effective_cell_to_input_scale_b,
-            cell_to_forget_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int16_t>(cell_to_forget_weights),
-            integer_lstm_param->effective_cell_to_forget_scale_a,
-            integer_lstm_param->effective_cell_to_forget_scale_b,
-            cell_to_output_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int16_t>(cell_to_output_weights),
-            integer_lstm_param->effective_cell_to_output_scale_a,
-            integer_lstm_param->effective_cell_to_output_scale_b,
-            projection_weights == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int8_t>(projection_weights),
-            integer_lstm_param->effective_proj_scale_a,
-            integer_lstm_param->effective_proj_scale_b,
-            integer_lstm_param->hidden_zp,
-            integer_lstm_param->effective_hidden_scale_a,
-            integer_lstm_param->effective_hidden_scale_b,
-            input_layer_norm_coefficients == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int16_t>(
-                      input_layer_norm_coefficients),
-            integer_lstm_param->layer_norm_input_scale_a,
-            integer_lstm_param->layer_norm_input_scale_b,
-            forget_layer_norm_coefficients == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int16_t>(
-                      forget_layer_norm_coefficients),
-            integer_lstm_param->layer_norm_forget_scale_a,
-            integer_lstm_param->layer_norm_forget_scale_b,
-            cell_layer_norm_coefficients == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int16_t>(
-                      cell_layer_norm_coefficients),
-            integer_lstm_param->layer_norm_cell_scale_a,
-            integer_lstm_param->layer_norm_cell_scale_b,
-            output_layer_norm_coefficients == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int16_t>(
-                      output_layer_norm_coefficients),
-            integer_lstm_param->layer_norm_output_scale_a,
-            integer_lstm_param->layer_norm_output_scale_b,
-            input_gate_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int32_t>(input_gate_bias),
-            forget_gate_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int32_t>(forget_gate_bias),
-            cell_gate_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int32_t>(cell_gate_bias),
-            output_gate_bias == nullptr
-                ? nullptr
-                : tflite::micro::GetTensorData<int32_t>(output_gate_bias),
-            integer_lstm_param->quantized_cell_clip,
-            integer_lstm_param->quantized_proj_clip,
-            integer_lstm_param->cell_scale,
-            integer_lstm_param->input_variance_guard,
-            integer_lstm_param->forget_variance_guard,
-            integer_lstm_param->cell_variance_guard,
-            integer_lstm_param->output_variance_guard,
-            integer_lstm_param->input_to_forget_effective_bias,
-            integer_lstm_param->recurrent_to_forget_effective_bias,
-            integer_lstm_param->input_to_cell_effective_bias,
-            integer_lstm_param->recurrent_to_cell_effective_bias,
-            integer_lstm_param->input_to_output_effective_bias,
-            integer_lstm_param->recurrent_to_output_effective_bias,
-            integer_lstm_param->input_to_input_effective_bias,
-            integer_lstm_param->recurrent_to_input_effective_bias,
-            integer_lstm_param->projection_effective_bias, /*n_batch=*/1,
-            n_cell, n_input, n_output, output_state_ptr, output_state_zp,
-            cell_state_ptr, output_ptr, scratch0, scratch1, scratch2, scratch3,
-            scratch4, scratch5);
-      }
-    }
-  }
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus EvalInteger8x8_8Lstm(
-    const TfLiteEvalTensor* input,
-    const TfLiteEvalTensor* input_to_input_weights,
-    const TfLiteEvalTensor* input_to_forget_weights,
-    const TfLiteEvalTensor* input_to_cell_weights,
-    const TfLiteEvalTensor* input_to_output_weights,
-    const TfLiteEvalTensor* recurrent_to_input_weights,
-    const TfLiteEvalTensor* recurrent_to_forget_weights,
-    const TfLiteEvalTensor* recurrent_to_cell_weights,
-    const TfLiteEvalTensor* recurrent_to_output_weights,
-    const TfLiteEvalTensor* cell_to_input_weights,
-    const TfLiteEvalTensor* cell_to_forget_weights,
-    const TfLiteEvalTensor* cell_to_output_weights,
-    const TfLiteEvalTensor* input_layer_norm_coefficients,
-    const TfLiteEvalTensor* forget_layer_norm_coefficients,
-    const TfLiteEvalTensor* cell_layer_norm_coefficients,
-    const TfLiteEvalTensor* output_layer_norm_coefficients,
-    const TfLiteEvalTensor* input_gate_bias,
-    const TfLiteEvalTensor* forget_gate_bias,
-    const TfLiteEvalTensor* cell_gate_bias,
-    const TfLiteEvalTensor* output_gate_bias,
-    const TfLiteEvalTensor* projection_weights,
-    const TfLiteEvalTensor* projection_bias, const TfLiteLSTMParams* params,
-    TfLiteEvalTensor* output_state, TfLiteEvalTensor* cell_state,
-    TfLiteEvalTensor* output, const IntegerLstmParameter* integer_lstm_param,
-    int32_t input_zp, int32_t output_state_zp, int8_t* scratch0,
-    int8_t* scratch1, int16_t* scratch2, int16_t* scratch3, int16_t* scratch4,
-    int16_t* scratch5, int16_t* scratch6, int16_t* scratch7) {
-  TFLITE_DCHECK(input->dims->size >= 2 && input->dims->size <= 3);
-  const int n_input = input->dims->data[input->dims->size - 1];
-  int max_time, n_batch;
-  if (input->dims->size == 2) {
-    max_time = 1;
-    n_batch = input->dims->data[0];
-  } else {
-    max_time = input->dims->data[0];
-    n_batch = input->dims->data[1];
-  }
-
-  // n_cell and n_output will be the same size when there is no projection.
-  const int n_cell = input_to_output_weights->dims->data[0];
-  const int n_output = recurrent_to_output_weights->dims->data[1];
-
-  // Get params for time/batch/sequence.
-  const int output_batch_leading_dim =
-      output->dims->data[output->dims->size - 1];
-  const int input_step = n_batch * n_input;
-  const int output_step = n_batch * output_batch_leading_dim;
-
-  for (int t = 0; t < max_time; t++) {
-    const int t_rel = t;
-    int8_t* output_ptr =
-        tflite::micro::GetTensorData<int8_t>(output) + t_rel * output_step;
-    // Input can be int8 asymmetric or int16 symmetric.
-    const int8_t* input_ptr =
-        tflite::micro::GetTensorData<int8_t>(input) + t_rel * input_step;
-    LstmStepInteger8x8_8(
-        input_ptr, input_zp,
-
-        input_to_input_weights == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int8_t>(input_to_input_weights),
-        integer_lstm_param->effective_input_to_input_scale_a,
-        integer_lstm_param->effective_input_to_input_scale_b,
-
-        input_to_forget_weights == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int8_t>(input_to_forget_weights),
-        integer_lstm_param->effective_input_to_forget_scale_a,
-        integer_lstm_param->effective_input_to_forget_scale_b,
-
-        input_to_cell_weights == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int8_t>(input_to_cell_weights),
-        integer_lstm_param->effective_input_to_cell_scale_a,
-        integer_lstm_param->effective_input_to_cell_scale_b,
-
-        input_to_output_weights == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int8_t>(input_to_output_weights),
-        integer_lstm_param->effective_input_to_output_scale_a,
-        integer_lstm_param->effective_input_to_output_scale_b,
-
-        recurrent_to_input_weights == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int8_t>(recurrent_to_input_weights),
-        integer_lstm_param->effective_recurrent_to_input_scale_a,
-        integer_lstm_param->effective_recurrent_to_input_scale_b,
-
-        recurrent_to_forget_weights == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int8_t>(recurrent_to_forget_weights),
-        integer_lstm_param->effective_recurrent_to_forget_scale_a,
-        integer_lstm_param->effective_recurrent_to_forget_scale_b,
-
-        recurrent_to_cell_weights == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int8_t>(recurrent_to_cell_weights),
-        integer_lstm_param->effective_recurrent_to_cell_scale_a,
-        integer_lstm_param->effective_recurrent_to_cell_scale_b,
-
-        recurrent_to_output_weights == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int8_t>(recurrent_to_output_weights),
-        integer_lstm_param->effective_recurrent_to_output_scale_a,
-        integer_lstm_param->effective_recurrent_to_output_scale_b,
-
-        cell_to_input_weights == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int8_t>(cell_to_input_weights),
-        integer_lstm_param->effective_cell_to_input_scale_a,
-        integer_lstm_param->effective_cell_to_input_scale_b,
-
-        cell_to_forget_weights == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int8_t>(cell_to_forget_weights),
-        integer_lstm_param->effective_cell_to_forget_scale_a,
-        integer_lstm_param->effective_cell_to_forget_scale_b,
-
-        cell_to_output_weights == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int8_t>(cell_to_output_weights),
-        integer_lstm_param->effective_cell_to_output_scale_a,
-        integer_lstm_param->effective_cell_to_output_scale_b,
-
-        projection_weights == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int8_t>(projection_weights),
-        integer_lstm_param->effective_proj_scale_a,
-        integer_lstm_param->effective_proj_scale_b,
-
-        input_layer_norm_coefficients == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int16_t>(
-                  input_layer_norm_coefficients),
-        integer_lstm_param->layer_norm_input_scale_a,
-        integer_lstm_param->layer_norm_input_scale_b,
-
-        forget_layer_norm_coefficients == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int16_t>(
-                  forget_layer_norm_coefficients),
-        integer_lstm_param->layer_norm_forget_scale_a,
-        integer_lstm_param->layer_norm_forget_scale_b,
-
-        cell_layer_norm_coefficients == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int16_t>(
-                  cell_layer_norm_coefficients),
-        integer_lstm_param->layer_norm_cell_scale_a,
-        integer_lstm_param->layer_norm_cell_scale_b,
-
-        output_layer_norm_coefficients == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int16_t>(
-                  output_layer_norm_coefficients),
-        integer_lstm_param->layer_norm_output_scale_a,
-        integer_lstm_param->layer_norm_output_scale_b,
-
-        input_gate_bias == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int32_t>(input_gate_bias),
-        forget_gate_bias == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int32_t>(forget_gate_bias),
-        cell_gate_bias == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int32_t>(cell_gate_bias),
-        output_gate_bias == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int32_t>(output_gate_bias),
-        projection_bias == nullptr
-            ? nullptr
-            : tflite::micro::GetTensorData<int32_t>(projection_bias),
-
-        params, integer_lstm_param->intermediate_scale_a,
-        integer_lstm_param->intermediate_scale_b,
-        integer_lstm_param->intermediate_zp,
-        integer_lstm_param->quantized_cell_clip,
-        integer_lstm_param->quantized_proj_clip, n_batch, n_cell, n_input,
-        n_output, output_batch_leading_dim,
-        tflite::micro::GetTensorData<int8_t>(output_state), output_state_zp,
-        tflite::micro::GetTensorData<int16_t>(cell_state), output_ptr, scratch0,
-        scratch1, scratch2, scratch3, scratch4, scratch5, scratch6, scratch7);
-  }
-
-  return kTfLiteOk;
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/maximum_minimum.cc

@@ -1,131 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/maximum_minimum.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace maximum_minimum {
-namespace {
-
-// This file has a reference implementation of TFMaximum/TFMinimum.
-enum KernelType {
-  kReference,
-};
-
-constexpr int kInputTensor1 = 0;
-constexpr int kInputTensor2 = 1;
-constexpr int kOutputTensor = 0;
-
-struct OpContext {
-  OpContext(TfLiteContext* context, TfLiteNode* node) {
-    input1 = tflite::micro::GetEvalInput(context, node, kInputTensor1);
-    input2 = tflite::micro::GetEvalInput(context, node, kInputTensor2);
-    output = tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  }
-  const TfLiteEvalTensor* input1;
-  const TfLiteEvalTensor* input2;
-  TfLiteEvalTensor* output;
-};
-
-struct MaximumOp {
-  template <typename data_type>
-  static data_type op(data_type el1, data_type el2) {
-    return el1 > el2 ? el1 : el2;
-  }
-};
-
-struct MinimumOp {
-  template <typename data_type>
-  static data_type op(data_type el1, data_type el2) {
-    return el1 < el2 ? el1 : el2;
-  }
-};
-
-}  // namespace
-
-template <typename data_type, typename op_type>
-void TFLiteOperation(TfLiteContext* context, TfLiteNode* node,
-                     const OpContext& op_context) {
-  reference_ops::MaximumMinimumBroadcastSlow(
-      tflite::micro::GetTensorShape(op_context.input1),
-      tflite::micro::GetTensorData<data_type>(op_context.input1),
-      tflite::micro::GetTensorShape(op_context.input2),
-      tflite::micro::GetTensorData<data_type>(op_context.input2),
-      tflite::micro::GetTensorShape(op_context.output),
-      tflite::micro::GetTensorData<data_type>(op_context.output),
-      op_type::template op<data_type>);
-}
-
-template <KernelType kernel_type, typename OpType>
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  OpContext op_context(context, node);
-
-  if (kernel_type == kReference) {
-    switch (op_context.output->type) {
-      case kTfLiteFloat32:
-        TFLiteOperation<float, OpType>(context, node, op_context);
-        break;
-      case kTfLiteInt8:
-        TFLiteOperation<int8_t, OpType>(context, node, op_context);
-        break;
-      case kTfLiteInt32:
-        TFLiteOperation<int32_t, OpType>(context, node, op_context);
-        break;
-      case kTfLiteInt64:
-        TFLiteOperation<int64_t, OpType>(context, node, op_context);
-        break;
-      default:
-        MicroPrintf("Type %s (%d) is not supported by Maximum/Minimum.",
-                    TfLiteTypeGetName(op_context.output->type),
-                    op_context.output->type);
-        return kTfLiteError;
-    }
-  } else {
-    MicroPrintf("Kernel type not supported by Maximum/Minimum.");
-    return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace maximum_minimum
-
-TfLiteRegistration Register_MAXIMUM() {
-  return tflite::micro::RegisterOp(
-      nullptr, nullptr,
-      maximum_minimum::Eval<maximum_minimum::kReference,
-                            maximum_minimum::MaximumOp>);
-}
-
-TfLiteRegistration Register_MINIMUM() {
-  return tflite::micro::RegisterOp(
-      nullptr, nullptr,
-      maximum_minimum::Eval<maximum_minimum::kReference,
-                            maximum_minimum::MinimumOp>);
-}
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_ops.h

@@ -1,135 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_
-#define TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_
-
-#include "tensorflow/lite/c/common.h"
-
-// Forward declaration of all micro op kernel registration methods. These
-// registrations are included with the standard `BuiltinOpResolver`.
-//
-// This header is particularly useful in cases where only a subset of ops are
-// needed. In such cases, the client can selectively add only the registrations
-// their model requires, using a custom `(Micro)MutableOpResolver`. Selective
-// registration in turn allows the linker to strip unused kernels.
-
-namespace tflite {
-
-// TFLM is incrementally moving towards a flat tflite namespace
-// (https://abseil.io/tips/130). Any new ops (or cleanup of existing ops should
-// have their Register function declarations in the tflite namespace.
-
-TfLiteRegistration Register_ADD();
-TfLiteRegistration Register_ADD_N();
-TfLiteRegistration Register_ASSIGN_VARIABLE();
-TfLiteRegistration Register_AVERAGE_POOL_2D();
-TfLiteRegistration Register_BATCH_TO_SPACE_ND();
-TfLiteRegistration Register_BROADCAST_ARGS();
-TfLiteRegistration Register_BROADCAST_TO();
-TfLiteRegistration Register_CALL_ONCE();
-TfLiteRegistration Register_CAST();
-// TODO(b/160234179): Change custom OPs to also return by value.
-TfLiteRegistration* Register_CIRCULAR_BUFFER();
-TfLiteRegistration Register_CUMSUM();
-TfLiteRegistration Register_DEPTH_TO_SPACE();
-TfLiteRegistration Register_DEPTHWISE_CONV_2D();
-TfLiteRegistration Register_DEQUANTIZE();
-TfLiteRegistration Register_DIV();
-TfLiteRegistration Register_ELU();
-TfLiteRegistration Register_EXP();
-TfLiteRegistration Register_EXPAND_DIMS();
-TfLiteRegistration Register_FILL();
-TfLiteRegistration Register_FLOOR_DIV();
-TfLiteRegistration Register_FLOOR_MOD();
-TfLiteRegistration Register_GATHER();
-TfLiteRegistration Register_GATHER_ND();
-TfLiteRegistration Register_HARD_SWISH();
-TfLiteRegistration Register_IF();
-TfLiteRegistration Register_L2_POOL_2D();
-TfLiteRegistration Register_LEAKY_RELU();
-TfLiteRegistration Register_LOG_SOFTMAX();
-TfLiteRegistration Register_LOGICAL_AND();
-TfLiteRegistration Register_LOGICAL_OR();
-TfLiteRegistration Register_LOGISTIC();
-TfLiteRegistration Register_MAX_POOL_2D();
-TfLiteRegistration Register_MIRROR_PAD();
-TfLiteRegistration Register_PRELU();
-TfLiteRegistration Register_MUL();
-TfLiteRegistration Register_QUANTIZE();
-TfLiteRegistration Register_READ_VARIABLE();
-TfLiteRegistration Register_RELU();
-TfLiteRegistration Register_RELU6();
-TfLiteRegistration Register_RESIZE_BILINEAR();
-TfLiteRegistration Register_SELECT_V2();
-TfLiteRegistration Register_SHAPE();
-TfLiteRegistration Register_SLICE();
-TfLiteRegistration Register_SPACE_TO_BATCH_ND();
-TfLiteRegistration Register_SPACE_TO_DEPTH();
-TfLiteRegistration Register_SQUARED_DIFFERENCE();
-TfLiteRegistration Register_SQUEEZE();
-TfLiteRegistration Register_SUB();
-TfLiteRegistration Register_SUM();
-TfLiteRegistration Register_SVDF();
-TfLiteRegistration Register_TRANSPOSE();
-TfLiteRegistration Register_TRANSPOSE_CONV();
-// TODO(b/230666079): resolve conflict with xtensa implementation
-TfLiteRegistration Register_UNIDIRECTIONAL_SEQUENCE_LSTM();
-TfLiteRegistration Register_VAR_HANDLE();
-TfLiteRegistration Register_WHILE();
-TfLiteRegistration Register_ZEROS_LIKE();
-
-namespace ops {
-namespace micro {
-
-TfLiteRegistration Register_ABS();
-TfLiteRegistration Register_ARG_MAX();
-TfLiteRegistration Register_ARG_MIN();
-TfLiteRegistration Register_CEIL();
-TfLiteRegistration Register_CONCATENATION();
-TfLiteRegistration Register_COS();
-TfLiteRegistration Register_EQUAL();
-TfLiteRegistration Register_FLOOR();
-TfLiteRegistration Register_GREATER();
-TfLiteRegistration Register_GREATER_EQUAL();
-TfLiteRegistration Register_LESS();
-TfLiteRegistration Register_LESS_EQUAL();
-TfLiteRegistration Register_LOG();
-TfLiteRegistration Register_LOGICAL_NOT();
-TfLiteRegistration Register_MAXIMUM();
-TfLiteRegistration Register_MINIMUM();
-TfLiteRegistration Register_NEG();
-TfLiteRegistration Register_NOT_EQUAL();
-TfLiteRegistration Register_PACK();
-TfLiteRegistration Register_PAD();
-TfLiteRegistration Register_PADV2();
-TfLiteRegistration Register_RESHAPE();
-TfLiteRegistration Register_RESIZE_NEAREST_NEIGHBOR();
-TfLiteRegistration Register_ROUND();
-TfLiteRegistration Register_RSQRT();
-TfLiteRegistration Register_SIN();
-TfLiteRegistration Register_SPLIT();
-TfLiteRegistration Register_SPLIT_V();
-TfLiteRegistration Register_SQRT();
-TfLiteRegistration Register_SQUARE();
-TfLiteRegistration Register_STRIDED_SLICE();
-TfLiteRegistration Register_UNPACK();
-TfLiteRegistration Register_L2_NORMALIZATION();
-TfLiteRegistration Register_TANH();
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_MICRO_KERNELS_MICRO_OPS_H_

code/components/tflite-lib/tensorflow/lite/micro/kernels/micro_tensor_utils.cc

@@ -1,809 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/micro/kernels/micro_tensor_utils.h"
-
-#include <algorithm>
-#include <cmath>
-#include <cstdint>
-#include <cstring>
-#include <limits>
-#include <utility>
-
-#include "fixedpoint/fixedpoint.h"  // from @gemmlowp
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/kernels/internal/cppmath.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-
-namespace tflite {
-namespace micro_tensor_utils {
-
-namespace {
-const int32_t kInt16Max = std::numeric_limits<int16_t>::max();
-const int32_t kInt16Min = std::numeric_limits<int16_t>::min();
-}  // namespace
-
-void PortableSymmetricQuantizeFloats(const float* values, const int size,
-                                     int8_t* quantized_values, float* min_value,
-                                     float* max_value, float* scaling_factor) {
-  auto minmax = std::minmax_element(values, values + size);
-  *min_value = *minmax.first;
-  *max_value = *minmax.second;
-
-  PortableSymmetricQuantizeFloats(values, size, quantized_values, *min_value,
-                                  *max_value, scaling_factor);
-}
-
-void PortableSymmetricQuantizeFloats(const float* values, const int size,
-                                     int8_t* quantized_values, float min_value,
-                                     float max_value, float* scaling_factor) {
-  const int32_t kScale = 127;
-  const float range = std::max(std::abs(min_value), std::abs(max_value));
-  if (range == 0) {
-    memset(quantized_values, 0, size * sizeof(int8_t));
-    *scaling_factor = 1;
-    return;
-  }
-  *scaling_factor = range / kScale;
-  const float scaling_factor_inv = kScale / range;
-  for (int i = 0; i < size; ++i) {
-    const int32_t quantized_value =
-        static_cast<int32_t>(TfLiteRound(values[i] * scaling_factor_inv));
-    // Clamp: just in case some odd numeric offset.
-    quantized_values[i] = static_cast<int8_t>(
-        std::min(kScale, std::max(-kScale, quantized_value)));
-  }
-}
-
-void PortableAsymmetricQuantizeFloats(const float* values, const int size,
-                                      int8_t* quantized_values,
-                                      float* scaling_factor, int32_t* offset) {
-  const int32_t kMinScale = -128;
-  const int32_t kMaxScale = 127;
-  const double qmin_double = kMinScale;
-  const double qmax_double = kMaxScale;
-  const auto minmax = std::minmax_element(values, values + size);
-  const double rmin = static_cast<double>(std::min(0.0f, *minmax.first));
-  const double rmax = static_cast<double>(std::max(0.0f, *minmax.second));
-  if (rmin == rmax) {
-    memset(quantized_values, 0, size * sizeof(int8_t));
-    *scaling_factor = 1;
-    *offset = 0;
-    return;
-  } else {
-    double scale = (rmax - rmin) / (qmax_double - qmin_double);
-    const double zero_point_from_min = qmin_double - rmin / scale;
-    const double zero_point_from_max = qmax_double - rmax / scale;
-    const double zero_point_from_min_error =
-        std::abs(qmin_double) + std::abs(rmin / scale);
-    const double zero_point_from_max_error =
-        std::abs(qmax_double) + std::abs(rmax / scale);
-    const double zero_point_double =
-        zero_point_from_min_error < zero_point_from_max_error
-            ? zero_point_from_min
-            : zero_point_from_max;
-    int8_t nudged_zero_point = 0;
-    if (zero_point_double <= qmin_double) {
-      nudged_zero_point = kMinScale;
-    } else if (zero_point_double >= qmax_double) {
-      nudged_zero_point = kMaxScale;
-    } else {
-      nudged_zero_point = static_cast<int8_t>(round(zero_point_double));
-    }
-    *scaling_factor = scale;
-    *offset = nudged_zero_point;
-  }
-  const float scaling_factor_inv = 1.0f / *scaling_factor;
-  for (int i = 0; i < size; ++i) {
-    const int32_t quantized_value = static_cast<int32_t>(
-        TfLiteRound(*offset + values[i] * scaling_factor_inv));
-    quantized_values[i] =
-        std::min(kMaxScale, std::max(kMinScale, quantized_value));
-  }
-}
-
-void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix,
-                                                 int m_rows, int m_cols,
-                                                 const float* vector,
-                                                 int n_batch, float* result) {
-  float* result_in_batch = result;
-  for (int b = 0; b < n_batch; b++) {
-    const float* matrix_ptr = matrix;
-    for (int r = 0; r < m_rows; r++) {
-      float dot_prod = 0.0f;
-      const float* vector_in_batch = vector + b * m_cols;
-      for (int c = 0; c < m_cols; c++) {
-        dot_prod += *matrix_ptr++ * *vector_in_batch++;
-      }
-      *result_in_batch += dot_prod;
-      ++result_in_batch;
-    }
-  }
-}
-
-void PortableMatrixBatchVectorMultiplyAccumulate(
-    const int8_t* __restrict__ matrix, const int m_rows, const int m_cols,
-    const int8_t* __restrict__ vectors, const float* scaling_factors,
-    int n_batch, float* __restrict__ result) {
-  for (int batch = 0; batch < n_batch; ++batch, vectors += m_cols) {
-    const float batch_scaling_factor = scaling_factors[batch];
-    // Get the address of the first row.
-    const int8_t* row_ptr = matrix;
-    for (int row = 0; row < m_rows; ++row) {
-      // Initialize the dot product sum for the row to 0.
-      int32_t dotprod = 0;
-      // TODO(b/230666277): remove this
-#if defined(__GNUC__)
-      // Prefetch the row to cache.
-      __builtin_prefetch(row_ptr, 0 /* prefetch for read */,
-                         3 /* temporal locality */);
-#endif
-      for (int col = 0; col < m_cols; ++col, ++row_ptr) {
-        dotprod += (*row_ptr) * (vectors[col]);
-      }  // for col
-      *result += dotprod * batch_scaling_factor;
-      ++result;
-    }  // for row
-  }    // for batch
-}
-
-void PortableMatrixBatchVectorMultiplyAccumulate(
-    const int8_t* __restrict__ matrix, const int m_rows, const int m_cols,
-    const int8_t* __restrict__ vectors, const float* scaling_factors,
-    int n_batch, float* __restrict__ result, const float* per_channel_scale,
-    const int32_t* input_offset, int32_t* scratch, int32_t* row_sums,
-    bool* compute_row_sums, CpuBackendContext* context) {
-  if (input_offset == nullptr) {
-    PortableMatrixBatchVectorMultiplyAccumulate(
-        matrix, m_rows, m_cols, vectors, scaling_factors, n_batch, result);
-    return;
-  }
-  if (!compute_row_sums || *compute_row_sums) {
-    PortableReductionSumVector(matrix, row_sums, m_rows, m_cols);
-    if (compute_row_sums) {
-      *compute_row_sums = false;
-    }
-  }
-
-  for (int batch = 0; batch < n_batch; ++batch, vectors += m_cols) {
-    const float batch_scaling_factor = scaling_factors[batch];
-    const int32_t batch_offset = input_offset[batch];
-    const int8_t* row_ptr = matrix;
-    for (int row = 0; row < m_rows; ++row) {
-      int32_t dotprod = 0;
-      float scale = batch_scaling_factor;
-      if (per_channel_scale) {
-        scale *= per_channel_scale[row];
-      }
-#if defined(__GNUC__)
-      // Prefetch the row to cache.
-      __builtin_prefetch(row_ptr, 0 /* prefetch for read */,
-                         3 /* temporal locality */);
-#endif
-      for (int col = 0; col < m_cols; ++col, ++row_ptr) {
-        dotprod += (*row_ptr) * vectors[col];
-      }  // for col
-      dotprod -= row_sums[row] * batch_offset;
-      *result += dotprod * scale;
-      ++result;
-    }  // for row
-  }    // for batch
-}
-
-void PortableSparseMatrixBatchVectorMultiplyAccumulate1x4(
-    const float* __restrict__ matrix, const int32_t* __restrict__ segments,
-    const int32_t* __restrict__ indices, int m_rows, int m_cols,
-    const float* __restrict__ vector, int n_batch, float* __restrict__ result) {
-  const int kBlockSize = 4;
-  TFLITE_DCHECK_EQ(m_cols % kBlockSize, 0);
-  for (int batch = 0; batch < n_batch; batch++) {
-    const float* matrix_ptr = matrix;
-    for (int row = 0; row < m_rows; row++) {
-      float dot_prod = 0.0f;
-      const float* vector_in_batch = vector + batch * m_cols;
-      for (int i = segments[row]; i < segments[row + 1]; i++) {
-        const int block_start_index = indices[i] * kBlockSize;
-        const float* vector_block_in_batch_ptr =
-            vector_in_batch + block_start_index;
-        for (int c = 0; c < kBlockSize; c++) {
-          dot_prod += *matrix_ptr++ * *vector_block_in_batch_ptr++;
-        }
-      }
-      result[batch * m_rows + row] += dot_prod;
-    }
-  }
-}
-
-void PortableSparseMatrixBatchVectorMultiplyAccumulate1x16(
-    const int8_t* __restrict__ matrix, const int32_t* __restrict__ segments,
-    const int32_t* __restrict__ indices, int m_rows, int m_cols,
-    const int8_t* __restrict__ vector, const int32_t* __restrict__ bias_vector,
-    int n_batch, const int32_t input_offset, const int32_t output_multiplier,
-    const int32_t output_shift, const int32_t output_offset,
-    const int32_t output_activation_min, const int32_t output_activation_max,
-    int8_t* __restrict__ result) {
-  const int kBlockSize = 16;
-  TFLITE_DCHECK_EQ(m_cols % kBlockSize, 0);
-  for (int batch = 0; batch < n_batch; ++batch) {
-    const int8_t* matrix_ptr = matrix;
-    for (int row = 0; row < m_rows; ++row) {
-      int32_t dot_prod = 0;
-      const int8_t* vector_in_batch = vector + batch * m_cols;
-      for (int i = segments[row]; i < segments[row + 1]; ++i) {
-        const int block_start_index = indices[i] * kBlockSize;
-        const int8_t* vector_block_in_batch_ptr =
-            vector_in_batch + block_start_index;
-        for (int c = 0; c < kBlockSize; c++) {
-          dot_prod += *matrix_ptr * *vector_block_in_batch_ptr++;
-          dot_prod += *matrix_ptr++ * input_offset;
-        }
-      }
-      const int32_t bias_value = bias_vector != nullptr ? bias_vector[row] : 0;
-      dot_prod = MultiplyByQuantizedMultiplier(dot_prod + bias_value,
-                                               output_multiplier, output_shift);
-      dot_prod += output_offset;
-      result[batch * m_rows + row] =
-          static_cast<int8_t>(ActivationFunctionWithMinMax(
-              dot_prod, output_activation_min, output_activation_max));
-    }
-  }
-}
-
-void PortableSparseMatrixBatchVectorMultiplyAccumulate(
-    const float* __restrict__ matrix, const uint8_t* __restrict__ ledger,
-    int m_rows, int m_cols, const float* __restrict__ vector, int n_batch,
-    float* __restrict__ result) {
-  const int kBlockSize = 16;
-  TFLITE_DCHECK_EQ(  // NOLINT
-      m_cols % kBlockSize, 0);
-  for (int batch = 0; batch < n_batch; batch++) {
-    const float* matrix_ptr = matrix;
-    const uint8_t* ledger_ptr = ledger;
-    for (int row = 0; row < m_rows; row++) {
-      float dot_prod = 0.0f;
-      int num_nonzero_blocks = *ledger_ptr++;
-      if (num_nonzero_blocks > 0) {
-        const float* vector_in_batch = vector + batch * m_cols;
-        for (int i = 0; i < num_nonzero_blocks; i++) {
-          const int block_start_index = *ledger_ptr++ * kBlockSize;
-          const float* vector_block_in_batch_ptr =
-              vector_in_batch + block_start_index;
-          for (int c = 0; c < kBlockSize; c++) {
-            dot_prod += *matrix_ptr++ * *vector_block_in_batch_ptr++;
-          }
-        }
-      }
-      result[batch * m_rows + row] += dot_prod;
-    }
-  }
-}
-
-void PortableSparseMatrixBatchVectorMultiplyAccumulate(
-    const int8_t* __restrict__ matrix, const uint8_t* ledger, const int m_rows,
-    const int m_cols, const int8_t* __restrict__ vectors,
-    const float* scaling_factors, int n_batch, float* __restrict__ result) {
-  static const int kBlockSize = 16;
-  TFLITE_DCHECK_EQ(  // NOLINT
-      m_cols % kBlockSize, 0);
-  for (int batch = 0; batch < n_batch; ++batch, vectors += m_cols) {
-    const float batch_scaling_factor = scaling_factors[batch];
-    const uint8_t* ledger_ptr = ledger;
-    // Get the address of the first row.
-    const int8_t* row_ptr = matrix;
-    for (int row = 0; row < m_rows; ++row) {
-      // Initialize the dot product sum for the row to 0.
-      int32_t dotprod = 0;
-#if defined(__GNUC__)
-      // Prefetch the row to cache.
-      __builtin_prefetch(row_ptr, 0 /* prefetch for read */,
-                         3 /* temporal locality */);
-#endif
-      int num_nonzero_blocks = *ledger_ptr++;
-      for (int i = 0; i < num_nonzero_blocks; i++) {
-        const int block_start_index = *ledger_ptr++ * kBlockSize;
-        const int8_t* vector_block_ptr = vectors + block_start_index;
-        for (int c = 0; c < kBlockSize; c++) {
-          dotprod += (*row_ptr++) * (*vector_block_ptr++);
-        }  // for block
-      }    // for num_nonzero_blocks
-      result[batch * m_rows + row] += dotprod * batch_scaling_factor;
-    }  // for row
-  }    // for batch
-}
-
-template <typename T>
-void PortableMatrixBatchVectorMultiplyAccumulateImpl(
-    const int8_t* input, const int32_t* bias,
-    const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift,
-    int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp,
-    T* output) {
-  const int16_t output_max = std::numeric_limits<T>::max();
-  const int16_t output_min = std::numeric_limits<T>::min();
-  for (int batch = 0; batch < n_batch; ++batch) {
-    for (int row = 0; row < n_output; ++row) {
-      int32_t acc = bias[row];
-      for (int col = 0; col < n_input; ++col) {
-        int8_t input_val = input[batch * n_input + col];
-        int8_t weights_val = input_to_gate_weights[row * n_input + col];
-        acc += input_val * weights_val;
-      }
-      acc = MultiplyByQuantizedMultiplier(acc, multiplier, shift);
-      acc += output_zp;
-      acc += output[batch * n_output + row];
-      if (acc > output_max) {
-        acc = output_max;
-      }
-      if (acc < output_min) {
-        acc = output_min;
-      }
-      output[batch * n_output + row] = static_cast<T>(acc);
-    }
-  }
-}
-
-void PortableMatrixBatchVectorMultiplyAccumulate(
-    const int8_t* input, const int32_t* bias,
-    const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift,
-    int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp,
-    int32_t* scratch, int16_t* output, CpuBackendContext* context) {
-  PortableMatrixBatchVectorMultiplyAccumulateImpl(
-      input, bias, input_to_gate_weights, multiplier, shift, n_batch, n_input,
-      n_output, output_zp, output);
-}
-
-void PortableMatrixBatchVectorMultiplyAccumulate(
-    const int8_t* input, const int32_t* bias,
-    const int8_t* input_to_gate_weights, int32_t multiplier, int32_t shift,
-    int32_t n_batch, int32_t n_input, int32_t n_output, int32_t output_zp,
-    int32_t* scratch, int8_t* output, CpuBackendContext* context) {
-  PortableMatrixBatchVectorMultiplyAccumulateImpl(
-      input, bias, input_to_gate_weights, multiplier, shift, n_batch, n_input,
-      n_output, output_zp, output);
-}
-
-void PortableMatrixBatchVectorMultiply(const int8_t* input,
-                                       int32_t input_zeropoint,
-                                       const int8_t* input_to_gate_weights,
-                                       int32_t input_to_gate_effective_scale_a,
-                                       int32_t input_to_gate_effective_scale_b,
-                                       int32_t n_batch, int32_t n_input,
-                                       int32_t n_cell, int8_t* gate_output,
-                                       int8_t gate_output_zp) {
-  const int32_t int8_max = std::numeric_limits<int8_t>::max();
-  const int32_t int8_min = std::numeric_limits<int8_t>::min();
-  for (int batch = 0; batch < n_batch; ++batch) {
-    for (int row = 0; row < n_cell; ++row) {
-      int32_t acc = 0;
-      for (int col = 0; col < n_input; ++col) {
-        int32_t input_val = input[batch * n_input + col];
-        int8_t weights_val = input_to_gate_weights[row * n_input + col];
-        acc += (input_val - input_zeropoint) * weights_val;
-      }
-      acc = MultiplyByQuantizedMultiplier(acc, input_to_gate_effective_scale_a,
-                                          input_to_gate_effective_scale_b);
-      acc += gate_output_zp;
-      if (acc > int8_max) {
-        acc = int8_max;
-      }
-      if (acc < int8_min) {
-        acc = int8_min;
-      }
-      gate_output[batch * n_cell + row] = static_cast<int8_t>(acc);
-    }
-  }
-}
-
-void PortableMatrixBatchVectorMultiply(
-    const int16_t* hidden, const int8_t* hidden_to_output_weights,
-    int32_t proj_effective_scale_a, int32_t proj_effective_scale_b,
-    const int32_t* gate_bias, int32_t n_batch, int32_t n_hidden,
-    int32_t n_output, int32_t output_zp, int8_t* proj_output) {
-  const int16_t int8_max = std::numeric_limits<int8_t>::max();
-  const int16_t int8_min = std::numeric_limits<int8_t>::min();
-  for (int batch = 0; batch < n_batch; ++batch) {
-    for (int row = 0; row < n_output; ++row) {
-      int64_t acc = gate_bias[row];
-      for (int col = 0; col < n_hidden; ++col) {
-        int16_t input_val = hidden[batch * n_hidden + col];
-        int8_t weights_val = hidden_to_output_weights[row * n_hidden + col];
-        int64_t curr = acc;
-        acc += input_val * weights_val;
-        if (input_val * weights_val > 0 && acc < curr) {
-          acc = std::numeric_limits<int32_t>::max();
-        }
-        if (input_val * weights_val < 0 && acc > curr) {
-          acc = std::numeric_limits<int32_t>::min();
-        }
-      }
-      acc = MultiplyByQuantizedMultiplier(acc, proj_effective_scale_a,
-                                          proj_effective_scale_b);
-      acc += output_zp;
-      if (acc > int8_max) {
-        acc = int8_max;
-      }
-      if (acc < int8_min) {
-        acc = int8_min;
-      }
-      proj_output[batch * n_output + row] = acc;
-    }
-  }
-}
-
-void PortableApplyLayerNorm(const int16_t* input,
-                            const int16_t* layer_norm_weights,
-                            const int32_t* bias, int32_t layer_norm_scale_a,
-                            int32_t layer_norm_scale_b, int32_t variance_limit,
-                            int n_batch, int n_input, int16_t* output) {
-  // The square of std::pow(2, 10), which is the extra factor that makes sure
-  // normalized values has enough resolution.
-  static const int kTwoToPower20 = 1 << 20;
-  for (int i = 0; i < n_batch; ++i) {
-    int64_t sum = 0;
-    int64_t sum_sq = 0;
-    for (int j = 0; j < n_input; ++j) {
-      const int32_t index = i * n_input + j;
-      int32_t val = static_cast<int32_t>(input[index]);
-      sum += val;
-      sum_sq += val * val;
-    }
-    int32_t mean =
-        static_cast<int32_t>(static_cast<int64_t>(sum) * 1024 / n_input);
-    // TODO(b/173994730): Avoids overflow but only works for POT n_input.
-    int32_t temp = kTwoToPower20 / n_input;
-    int64_t variance =
-        sum_sq * temp - static_cast<int64_t>(mean) * static_cast<int64_t>(mean);
-    int32_t variance2 = static_cast<int32_t>(variance / kTwoToPower20);
-    if (variance2 < 1) {
-      variance2 = variance_limit;
-    }
-    int32_t stddev_inverse_a;
-    int stddev_inverse_b;
-    GetInvSqrtQuantizedMultiplierExp(variance2, /*reverse_shift*/ -1,
-                                     &stddev_inverse_a, &stddev_inverse_b);
-
-    for (int j = 0; j < n_input; ++j) {
-      const int32_t index = i * n_input + j;
-      int32_t val = static_cast<int32_t>(input[index]);
-      int32_t shifted = 1024 * val - mean;
-      int32_t rescaled = MultiplyByQuantizedMultiplier(
-          shifted, stddev_inverse_a, stddev_inverse_b);
-      int64_t val3 = rescaled * layer_norm_weights[j] + bias[j];
-      int32_t val4 =
-          static_cast<int32_t>((val3 > 0 ? val3 + 512 : val3 - 512) / 1024);
-      int32_t val5 = MultiplyByQuantizedMultiplier(val4, layer_norm_scale_a,
-                                                   layer_norm_scale_b + 12);
-      val5 = std::min(std::max(kInt16Min, val5), kInt16Max);
-      output[index] = static_cast<int16_t>(val5);
-    }
-  }
-}
-
-void PortableApplyLayerNormFloat(const int16_t* input,
-                                 const int16_t* layer_norm_weights,
-                                 int32_t layer_norm_scale_a,
-                                 int32_t layer_norm_scale_b,
-                                 const int32_t* bias, int n_batch, int n_input,
-                                 int16_t* output) {
-  const int32_t int16_max = std::numeric_limits<int16_t>::max();
-  const int32_t int16_min = std::numeric_limits<int16_t>::min();
-  const float layer_norm_scale =
-      layer_norm_scale_a *
-      std::pow(2.0, static_cast<double>(layer_norm_scale_b - 31));
-  const float bias_scale =
-      static_cast<float>(std::pow(2.0, -10)) * layer_norm_scale;
-
-  for (int batch = 0; batch < n_batch; ++batch) {
-    float sum = 0.0f;
-    float sum_sq = 0.0f;
-    for (int i = 0; i < n_input; ++i) {
-      const int index = batch * n_input + i;
-      const float value = static_cast<float>(input[index]);
-      sum += value;
-      sum_sq += value * value;
-    }
-    const float mean = sum / n_input;
-    float stddev_inv = 0.0f;
-    const float variance = sum_sq / n_input - mean * mean;
-    if (variance == 0) {
-      stddev_inv = 1.0f / std::sqrt(1e-8f);
-    } else {
-      stddev_inv = 1.0f / std::sqrt(variance);
-    }
-    for (int i = 0; i < n_input; ++i) {
-      const int index = batch * n_input + i;
-      const float normalized_value =
-          (static_cast<float>(input[index]) - mean) * stddev_inv;
-      const float weighted_normalized_value =
-          normalized_value * layer_norm_weights[i] * layer_norm_scale +
-          bias[i] * bias_scale;
-      const int32_t quant_output = static_cast<int32_t>(round(
-          weighted_normalized_value * static_cast<float>(std::pow(2, 12))));
-      output[index] = std::min(int16_max, std::max(int16_min, quant_output));
-    }
-  }
-}
-
-void PortableMatrixScalarMultiplyAccumulate(const int8_t* matrix,
-                                            int32_t scalar, int32_t n_row,
-                                            int32_t n_col, int32_t* output) {
-  for (int i = 0; i < n_row; ++i) {
-    int32_t row_sum = 0;
-    for (int j = 0; j < n_col; ++j) {
-      row_sum += *matrix++;
-    }
-    output[i] += row_sum * scalar;
-  }
-}
-
-void PortableApplySigmoid(const int16_t* input, int32_t n_batch,
-                          int32_t n_input, int16_t* output) {
-  for (int batch = 0; batch < n_batch; ++batch) {
-    for (int c = 0; c < n_input; c++) {
-      using F3 = gemmlowp::FixedPoint<std::int16_t, 3>;
-      using F0 = gemmlowp::FixedPoint<std::int16_t, 0>;
-      const int index = batch * n_input + c;
-      F3 sigmoid_input = F3::FromRaw(input[index]);
-      F0 sigmoid_output = gemmlowp::logistic(sigmoid_input);
-      output[index] = sigmoid_output.raw();
-    }
-  }
-}
-
-void PortableApplySigmoidFloat(const int16_t* input, int32_t n_batch,
-                               int32_t n_input, int16_t* output) {
-  const int32_t int16_max = std::numeric_limits<int16_t>::max();
-  const int32_t int16_min = std::numeric_limits<int16_t>::min();
-  for (int batch = 0; batch < n_batch; ++batch) {
-    for (int i = 0; i < n_input; ++i) {
-      const int index = batch * n_input + i;
-      const float float_input =
-          input[index] * static_cast<float>(std::pow(2, -12));
-      const float float_output = 1.0f / (1.0f + std::exp(-float_input));
-      const int32_t quant_output = static_cast<int32_t>(
-          float_output * static_cast<float>(std::pow(2, 15)));
-      const int32_t quant_output_clamped =
-          std::min(int16_max, std::max(int16_min, quant_output));
-      output[index] = static_cast<int16_t>(quant_output_clamped);
-    }
-  }
-}
-
-template <int IntegerBits>
-void PortableApplyTanhImpl(const int16_t* input, int32_t n_batch,
-                           int32_t n_input, int16_t* output) {
-  using FX = gemmlowp::FixedPoint<std::int16_t, IntegerBits>;
-  using F0 = gemmlowp::FixedPoint<std::int16_t, 0>;
-  for (int batch = 0; batch < n_batch; ++batch) {
-    for (int i = 0; i < n_input; ++i) {
-      const int index = batch * n_input + i;
-      FX tanh_input = FX::FromRaw(input[index]);
-      F0 tanh_output = gemmlowp::tanh(tanh_input);
-      output[index] = tanh_output.raw();
-    }
-  }
-}
-
-void PortableApplyTanh(int32_t integer_bits, const int16_t* input,
-                       int32_t n_batch, int32_t n_input, int16_t* output) {
-  if (integer_bits > 6) {
-    TFLITE_ASSERT_FALSE;
-  }
-#define DISPATCH_TANH(i)                                       \
-  case i:                                                      \
-    PortableApplyTanhImpl<i>(input, n_batch, n_input, output); \
-    break;
-  switch (integer_bits) {
-    DISPATCH_TANH(0);
-    DISPATCH_TANH(1);
-    DISPATCH_TANH(2);
-    DISPATCH_TANH(3);
-    DISPATCH_TANH(4);
-    DISPATCH_TANH(5);
-    DISPATCH_TANH(6);
-    default:
-      return;
-  }
-#undef DISPATCH_TANH
-}
-
-void PortableApplyTanhFloat(const int16_t* input, int32_t n_batch,
-                            int32_t n_input, int32_t integer_bits,
-                            int16_t* output) {
-  const int32_t int16_max = std::numeric_limits<int16_t>::max();
-  const int32_t int16_min = std::numeric_limits<int16_t>::min();
-  const double two = 2.0;
-  for (int batch = 0; batch < n_batch; ++batch) {
-    for (int i = 0; i < n_input; ++i) {
-      const int index = batch * n_input + i;
-      const float float_input =
-          input[index] * std::pow(two, static_cast<double>(integer_bits));
-      const float float_output = std::tanh(float_input);
-      const int32_t quant_output = static_cast<int32_t>(
-          float_output * static_cast<float>(std::pow(2, 15)));
-      const int32_t quant_output_clamped =
-          std::min(int16_max, std::max(int16_min, quant_output));
-      output[index] = static_cast<int16_t>(quant_output_clamped);
-    }
-  }
-}
-
-void PortableCwiseMul(const int16_t* input_1, const int16_t* input_2,
-                      int n_batch, int n_input, int shift, int16_t* output) {
-  for (int batch = 0; batch < n_batch; ++batch) {
-    for (int i = 0; i < n_input; ++i) {
-      const int index = batch * n_input + i;
-      const int16_t a = input_1[index];
-      const int16_t b = input_2[index];
-      const int32_t value = static_cast<int32_t>(a) * static_cast<int32_t>(b);
-      output[index] =
-          static_cast<int16_t>(gemmlowp::RoundingDivideByPOT(value, shift));
-    }
-  }
-}
-
-void PortableCwiseMul(const int16_t* input_1, const int16_t* input_2,
-                      int32_t multiplier, int32_t shift, int32_t n_batch,
-                      int32_t n_input, int32_t output_zp, int8_t* output) {
-  for (int batch = 0; batch < n_batch; ++batch) {
-    for (int i = 0; i < n_input; ++i) {
-      const int index = batch * n_input + i;
-      const int16_t a = input_1[index];
-      const int16_t b = input_2[index];
-      int32_t value = static_cast<int32_t>(a) * static_cast<int32_t>(b);
-      value = MultiplyByQuantizedMultiplier(value, multiplier, shift);
-      value += output_zp;
-      value = std::min(std::max(static_cast<int32_t>(-128), value),
-                       static_cast<int32_t>(127));
-
-      output[index] = static_cast<int8_t>(value);
-    }
-  }
-}
-
-void PortableCwiseAdd(const int16_t* input_1, const int16_t* input_2,
-                      int n_batch, int n_input, int16_t* output) {
-  for (int batch = 0; batch < n_batch; ++batch) {
-    for (int i = 0; i < n_input; ++i) {
-      const int index = batch * n_input + i;
-      int32_t sum = input_1[index] + input_2[index];
-      const int32_t sum_clamped = std::min(kInt16Max, std::max(kInt16Min, sum));
-      output[index] = static_cast<int16_t>(sum_clamped);
-    }
-  }
-}
-
-float PortableVectorVectorDotProduct(const float* vector1, const float* vector2,
-                                     int v_size) {
-  float result = 0.0;
-  for (int v = 0; v < v_size; v++) {
-    result += *vector1++ * *vector2++;
-  }
-  return result;
-}
-
-namespace {
-inline int32_t VectorVectorDotProduct(const int16_t* vector1,
-                                      const int16_t* vector2, int v_size) {
-  int32_t result = 0;
-  for (int v = 0; v < v_size; v++) {
-    result += *vector1++ * *vector2++;
-  }
-  return result;
-}
-}  // namespace
-
-void PortableBatchVectorBatchVectorDotProduct(const int16_t* vector1,
-                                              const int16_t* vector2,
-                                              int v_size, int n_batch,
-                                              int32_t* result) {
-  for (int b = 0; b < n_batch; b++) {
-    result[b] = VectorVectorDotProduct(vector1, vector2, v_size);
-    vector1 += v_size;
-    vector2 += v_size;
-  }
-}
-
-void PortableVectorBatchVectorCwiseProductAccumulate(
-    const int16_t* vector, int v_size, const int16_t* batch_vector, int n_batch,
-    int32_t multiplier, int shift, int16_t* result) {
-  for (int b = 0; b < n_batch; b++) {
-    for (int v = 0; v < v_size; v++) {
-      int32_t prod = vector[v] * *batch_vector++;
-      prod = MultiplyByQuantizedMultiplier(prod, multiplier, shift);
-      int32_t output = prod + *result;
-      output = std::max(std::min(static_cast<int32_t>(32767), output),
-                        static_cast<int32_t>(-32768));
-      *result++ = output;
-    }
-  }
-}
-
-void PortableSub1Vector(const float* vector, int v_size, float* result) {
-  for (int v = 0; v < v_size; v++) {
-    *result++ = 1.0f - *vector++;
-  }
-}
-
-void PortableSub1Vector(const int16_t* vector, int v_size, int16_t* result) {
-  static const int16_t kOne = 32767;
-  for (int v = 0; v < v_size; v++) {
-    *result++ = kOne - *vector++;
-  }
-}
-
-void PortableVectorScalarMultiply(const int8_t* vector, const int v_size,
-                                  const float scale, float* result) {
-  for (int v = 0; v < v_size; ++v) {
-    *result++ = scale * *vector++;
-  }
-}
-
-void PortableMeanStddevNormalization(const float* __restrict__ input_vector,
-                                     float* __restrict__ output_vector,
-                                     int v_size, int n_batch) {
-  for (int batch = 0; batch < n_batch; ++batch) {
-    float sum = 0.0f;
-    for (int i = 0; i < v_size; ++i) {
-      sum += input_vector[i];
-    }
-    const float mean = sum / v_size;
-    float sum_diff_sq = 0.0f;
-    for (int i = 0; i < v_size; ++i) {
-      const float diff = input_vector[i] - mean;
-      sum_diff_sq += diff * diff;
-    }
-    const float variance = sum_diff_sq / v_size;
-    constexpr float kNormalizationConstant = 1e-8f;
-    const float stddev_inv =
-        1.0f / std::sqrt(variance + kNormalizationConstant);
-    for (int i = 0; i < v_size; ++i) {
-      output_vector[i] = (input_vector[i] - mean) * stddev_inv;
-    }
-    input_vector += v_size;
-    output_vector += v_size;
-  }
-}
-
-void PortableTwoGateSaturatingAdd(const int8_t* input, int8_t input_zp,
-                                  const int8_t* recurrent, int8_t recurrent_zp,
-                                  int32_t input_effective_scale_a,
-                                  int32_t input_effective_scale_b,
-                                  int32_t recurrent_effective_scale_a,
-                                  int32_t recurrent_effective_scale_b,
-                                  int32_t n_batch, int32_t n_cell,
-                                  int16_t* output) {
-  const int32_t int16_max = std::numeric_limits<int16_t>::max();
-  const int32_t int16_min = std::numeric_limits<int16_t>::min();
-  for (int i = 0; i < n_batch * n_cell; ++i) {
-    int32_t x = static_cast<int32_t>(input[i]) - static_cast<int32_t>(input_zp);
-    int32_t h =
-        static_cast<int32_t>(recurrent[i]) - static_cast<int32_t>(recurrent_zp);
-    int32_t x_scaled = MultiplyByQuantizedMultiplier(x, input_effective_scale_a,
-                                                     input_effective_scale_b);
-    int32_t h_scaled = MultiplyByQuantizedMultiplier(
-        h, recurrent_effective_scale_a, recurrent_effective_scale_b);
-    int32_t y = h_scaled + x_scaled;
-    if (y > int16_max) {
-      y = int16_max;
-    }
-    if (y < int16_min) {
-      y = int16_min;
-    }
-    output[i] = static_cast<int16_t>(y);
-  }
-}
-
-}  // namespace micro_tensor_utils
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/mul.h

@@ -1,74 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_KERNELS_MUL_H_
-#define TENSORFLOW_LITE_MICRO_KERNELS_MUL_H_
-
-#include <cstdint>
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-
-namespace tflite {
-
-extern const int kMulInput1Tensor;
-extern const int kMulInput2Tensor;
-extern const int kMulOutputTensor;
-
-struct OpDataMul {
-  int32_t input1_zero_point;
-  int32_t input2_zero_point;
-
-  int32_t output_activation_min;
-  int32_t output_activation_max;
-  int32_t output_zero_point;
-  int32_t output_multiplier;
-  int output_shift;
-
-  float output_activation_min_f32;
-  float output_activation_max_f32;
-};
-
-void* MulInit(TfLiteContext* context, const char* buffer, size_t length);
-
-TfLiteStatus CalculateOpDataMul(TfLiteContext* context, TfLiteNode* node,
-                                TfLiteMulParams* params, OpDataMul* data);
-
-TfLiteStatus MulPrepare(TfLiteContext* context, TfLiteNode* node);
-
-void EvalMulQuantizedReference(TfLiteContext* context, TfLiteNode* node,
-                               const OpDataMul* data,
-                               const TfLiteEvalTensor* input1,
-                               const TfLiteEvalTensor* input2,
-                               TfLiteEvalTensor* output);
-
-void EvalMulFloatReference(TfLiteContext* context, TfLiteNode* node,
-                           TfLiteMulParams* params, const OpDataMul* data,
-                           const TfLiteEvalTensor* input1,
-                           const TfLiteEvalTensor* input2,
-                           TfLiteEvalTensor* output);
-
-// Generic must define registration function.
-TfLiteRegistration Register_MUL();
-
-#if defined(CMSIS_NN)
-TfLiteRegistration Register_MUL_INT8();
-#else
-// Fallback registration
-inline TfLiteRegistration Register_MUL_INT8() { return Register_MUL(); }
-#endif
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_MICRO_KERNELS_MUL_H_

code/components/tflite-lib/tensorflow/lite/micro/kernels/neg.cc

@@ -1,59 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/neg.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace neg {
-
-constexpr int kInputTensor = 0;
-constexpr int kOutputTensor = 0;
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  switch (input->type) {
-    // TODO(wangtz): handle for kTfLiteInt8
-    case kTfLiteFloat32:
-      reference_ops::Negate(tflite::micro::GetTensorShape(input),
-                            tflite::micro::GetTensorData<float>(input),
-                            tflite::micro::GetTensorShape(output),
-                            tflite::micro::GetTensorData<float>(output));
-      break;
-    default:
-      MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
-                  input->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace neg
-
-TfLiteRegistration Register_NEG() {
-  return tflite::micro::RegisterOp(nullptr, nullptr, neg::Eval);
-}
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/pack.cc

@@ -1,116 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace pack {
-namespace {
-
-constexpr int kOutputTensor = 0;
-
-template <typename T>
-TfLiteStatus PackImpl(TfLiteContext* context, TfLiteNode* node,
-                      TfLiteEvalTensor* output, int values_count, int axis) {
-  const TfLiteEvalTensor* input0 =
-      tflite::micro::GetEvalInput(context, node, 0);
-
-  const int dimensions = output->dims->size;
-  const TfLiteIntArray* input_dims = input0->dims;
-  const TfLiteIntArray* output_dims = output->dims;
-
-  if (axis < 0) {
-    axis += dimensions;
-  }
-
-  int outer_size = 1;
-  for (int i = 0; i < axis; ++i) {
-    outer_size *= output_dims->data[i];
-  }
-  int copy_size = 1;
-  for (int i = axis + 1; i < dimensions; ++i) {
-    copy_size *= output_dims->data[i];
-  }
-  int input_size = 1;
-  for (int i = 0; i < input_dims->size; ++i) {
-    input_size *= input_dims->data[i];
-  }
-  TFLITE_DCHECK_EQ(input_size, copy_size * outer_size);
-
-  T* output_data = tflite::micro::GetTensorData<T>(output);
-
-  for (int i = 0; i < values_count; ++i) {
-    const TfLiteEvalTensor* t = tflite::micro::GetEvalInput(context, node, i);
-    const T* input_data = tflite::micro::GetTensorData<T>(t);
-    for (int k = 0; k < outer_size; ++k) {
-      const T* input_ptr = input_data + copy_size * k;
-      int loc = k * values_count * copy_size + i * copy_size;
-      T* output_ptr = output_data + loc;
-      for (int j = 0; j < copy_size; ++j) output_ptr[j] = input_ptr[j];
-    }
-  }
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLitePackParams* data =
-      reinterpret_cast<TfLitePackParams*>(node->builtin_data);
-
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  switch (output->type) {
-    case kTfLiteFloat32: {
-      return PackImpl<float>(context, node, output, data->values_count,
-                             data->axis);
-    }
-    case kTfLiteInt8: {
-      return PackImpl<int8_t>(context, node, output, data->values_count,
-                              data->axis);
-    }
-    case kTfLiteInt32: {
-      return PackImpl<int32_t>(context, node, output, data->values_count,
-                               data->axis);
-    }
-    case kTfLiteInt64: {
-      return PackImpl<int64_t>(context, node, output, data->values_count,
-                               data->axis);
-    }
-    default: {
-      MicroPrintf("Type '%s' is not supported by pack.",
-                  TfLiteTypeGetName(output->type));
-      return kTfLiteError;
-    }
-  }
-
-  return kTfLiteOk;
-}
-
-}  // namespace
-}  // namespace pack
-
-TfLiteRegistration Register_PACK() {
-  return tflite::micro::RegisterOp(nullptr, nullptr, pack::Eval);
-}
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/pad.cc

@@ -1,236 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/kernels/internal/reference/pad.h"
-
-#include <string.h>
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/portable_tensor.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace pad {
-namespace {
-
-struct OpData {
-  PadParams params;
-  int32_t output_zero_point;
-};
-
-}  // namespace
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(OpData));
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TFLITE_DCHECK(node->user_data != nullptr);
-  OpData* data = static_cast<OpData*>(node->user_data);
-
-  TF_LITE_ENSURE(context, NumInputs(node) == 2 || NumInputs(node) == 3);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, /*index=*/0);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* paddings =
-      micro_context->AllocateTempInputTensor(node, /*index=*/1);
-  TF_LITE_ENSURE(context, paddings != nullptr);
-  TfLiteTensor* constant_values =
-      NumInputs(node) == 3
-          ? micro_context->AllocateTempInputTensor(node, /*index=*/2)
-          : nullptr;
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, /*index=*/0);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  TF_LITE_ENSURE_EQ(context, input->type, output->type);
-
-  // Current implementations rely on the inputs being <= 4D.
-  TF_LITE_ENSURE(context, NumDimensions(input) <=
-                              reference_ops::PadKernelMaxDimensionCount());
-
-  if (constant_values != nullptr) {
-    TF_LITE_ENSURE_EQ(context, input->type, constant_values->type);
-    // Ensure that constant_values is a scalar.
-    TF_LITE_ENSURE_EQ(context, NumElements(constant_values), 1);
-  }
-
-  // There must be a pair of paddings for each output dimension.
-  TF_LITE_ENSURE_EQ(context, GetTensorShape(paddings).FlatSize(),
-                    output->dims->size * 2);
-
-  // On Micro, outputs must be properly sized by the converter.
-  // NOTE: This data is only available because the paddings buffer is stored in
-  // the flatbuffer:
-  TF_LITE_ENSURE(context, IsConstantTensor(paddings));
-  const int32_t* paddings_data = GetTensorData<int32_t>(paddings);
-  for (int i = 0; i < output->dims->size; i++) {
-    int output_dim = output->dims->data[i];
-    int expected_dim =
-        input->dims->data[i] + paddings_data[i * 2] + paddings_data[i * 2 + 1];
-    TF_LITE_ENSURE_EQ(context, output_dim, expected_dim);
-  }
-
-  // Calculate OpData:
-  data->params.resizing_category = ResizingCategory::kGenericResize;
-  const int paddings_total = GetTensorShape(paddings).FlatSize();
-  if (paddings_total == 8 && (paddings_data[0] == 0 && paddings_data[1] == 0) &&
-      (paddings_data[6] == 0 && paddings_data[7] == 0)) {
-    data->params.resizing_category = ResizingCategory::kImageStyle;
-  }
-
-  const int num_input_dimensions = NumDimensions(input);
-  data->params.left_padding_count = num_input_dimensions;
-  data->params.right_padding_count = num_input_dimensions;
-
-  for (int idx = num_input_dimensions - 1; idx >= 0; --idx) {
-    data->params.left_padding[idx] = paddings_data[idx * 2];
-    data->params.right_padding[idx] = paddings_data[idx * 2 + 1];
-  }
-
-  if (input->type == kTfLiteInt8) {
-    if (constant_values == nullptr) {
-      // Quantized Pad requires that 0 is represented in the quantized
-      // range.
-      TF_LITE_ENSURE(context, output->params.zero_point >=
-                                  std::numeric_limits<int8_t>::min());
-      TF_LITE_ENSURE(context, output->params.zero_point <=
-                                  std::numeric_limits<int8_t>::max());
-    } else {
-      // Quantized Pad requires that 'constant_values' is represented in the
-      // same quantized range as the input and output tensors.
-      TF_LITE_ENSURE_EQ(context, output->params.zero_point,
-                        constant_values->params.zero_point);
-      TF_LITE_ENSURE_EQ(context, static_cast<double>(output->params.scale),
-                        static_cast<double>(constant_values->params.scale));
-    }
-    data->output_zero_point = output->params.zero_point;
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(paddings);
-  if (constant_values != nullptr) {
-    micro_context->DeallocateTempTfLiteTensor(constant_values);
-  }
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const OpData* data = static_cast<const OpData*>(node->user_data);
-
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, /*index=*/0);
-  const TfLiteEvalTensor* constant_values =
-      NumInputs(node) == 3
-          ? tflite::micro::GetEvalInput(context, node, /*index=*/2)
-          : nullptr;
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, /*index=*/0);
-
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      float pad_value =
-          constant_values == nullptr
-              ? 0.f
-              : *tflite::micro::GetTensorData<float>(constant_values);
-      if (data->params.resizing_category == ResizingCategory::kImageStyle) {
-        reference_ops::PadImageStyle(
-            data->params, tflite::micro::GetTensorShape(input),
-            tflite::micro::GetTensorData<float>(input), &pad_value,
-            tflite::micro::GetTensorShape(output),
-            tflite::micro::GetTensorData<float>(output));
-      } else {
-        reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
-                           tflite::micro::GetTensorData<float>(input),
-                           &pad_value, tflite::micro::GetTensorShape(output),
-                           tflite::micro::GetTensorData<float>(output));
-      }
-    } break;
-    case kTfLiteInt8: {
-      int8_t pad_value;
-      if (constant_values == nullptr) {
-        pad_value = static_cast<uint8_t>(data->output_zero_point);
-      } else {
-        pad_value = *tflite::micro::GetTensorData<int8_t>(constant_values);
-      }
-      if (data->params.resizing_category == ResizingCategory::kImageStyle) {
-        reference_ops::PadImageStyle(
-            data->params, tflite::micro::GetTensorShape(input),
-            tflite::micro::GetTensorData<int8_t>(input), &pad_value,
-            tflite::micro::GetTensorShape(output),
-            tflite::micro::GetTensorData<int8_t>(output));
-      } else {
-        reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
-                           tflite::micro::GetTensorData<int8_t>(input),
-                           &pad_value, tflite::micro::GetTensorShape(output),
-                           tflite::micro::GetTensorData<int8_t>(output));
-      }
-    } break;
-    case kTfLiteInt16: {
-      int16_t pad_value =
-          constant_values == nullptr
-              ? 0
-              : *tflite::micro::GetTensorData<int16_t>(constant_values);
-      reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
-                         tflite::micro::GetTensorData<int16_t>(input),
-                         &pad_value, tflite::micro::GetTensorShape(output),
-                         tflite::micro::GetTensorData<int16_t>(output));
-    } break;
-    case kTfLiteInt32: {
-      int32_t pad_value =
-          constant_values == nullptr
-              ? 0
-              : *tflite::micro::GetTensorData<int32_t>(constant_values);
-      reference_ops::Pad(data->params, tflite::micro::GetTensorShape(input),
-                         tflite::micro::GetTensorData<int32_t>(input),
-                         &pad_value, tflite::micro::GetTensorShape(output),
-                         tflite::micro::GetTensorData<int32_t>(output));
-    } break;
-    default:
-
-      MicroPrintf("Type %s not currently supported by Pad.",
-                  TfLiteTypeGetName(input->type));
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace pad
-
-TfLiteRegistration Register_PAD() {
-  return tflite::micro::RegisterOp(pad::Init, pad::Prepare, pad::Eval);
-}
-
-// Also register Pad as PadV2.
-TfLiteRegistration Register_PADV2() {
-  return tflite::micro::RegisterOp(pad::Init, pad::Prepare, pad::Eval);
-}
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/pooling.cc

@@ -1,98 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/kernels/internal/reference/pooling.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/pooling.h"
-
-namespace tflite {
-
-namespace {
-
-TfLiteStatus AverageEval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-  auto* params = reinterpret_cast<TfLitePoolParams*>(node->builtin_data);
-
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const OpDataPooling* data =
-      static_cast<const OpDataPooling*>(node->user_data);
-
-  const TfLiteEvalTensor* input =
-      micro::GetEvalInput(context, node, kPoolingInputTensor);
-  TfLiteEvalTensor* output =
-      micro::GetEvalOutput(context, node, kPoolingOutputTensor);
-
-  // Inputs and outputs share the same type, guaranteed by the converter.
-  switch (input->type) {
-    case kTfLiteFloat32:
-      AveragePoolingEvalFloat(context, node, params, data, input, output);
-      break;
-    case kTfLiteInt8:
-      AveragePoolingEvalQuantized(context, node, params, data, input, output);
-      break;
-    default:
-      MicroPrintf("Input type %s is not currently supported",
-                  TfLiteTypeGetName(input->type));
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus MaxEval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-  auto* params = reinterpret_cast<TfLitePoolParams*>(node->builtin_data);
-
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const OpDataPooling* data =
-      static_cast<const OpDataPooling*>(node->user_data);
-
-  const TfLiteEvalTensor* input =
-      micro::GetEvalInput(context, node, kPoolingInputTensor);
-  TfLiteEvalTensor* output =
-      micro::GetEvalOutput(context, node, kPoolingOutputTensor);
-
-  switch (input->type) {
-    case kTfLiteFloat32:
-      MaxPoolingEvalFloat(context, node, params, data, input, output);
-      break;
-    case kTfLiteInt8:
-      MaxPoolingEvalQuantized(context, node, params, data, input, output);
-      break;
-    default:
-      MicroPrintf("Type %s not currently supported.",
-                  TfLiteTypeGetName(input->type));
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(OpDataPooling));
-}
-
-}  // namespace
-
-TfLiteRegistration Register_AVERAGE_POOL_2D() {
-  return tflite::micro::RegisterOp(Init, PoolingPrepare, AverageEval);
-}
-
-TfLiteRegistration Register_MAX_POOL_2D() {
-  return tflite::micro::RegisterOp(Init, PoolingPrepare, MaxEval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/pooling.h

@@ -1,85 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_KERNELS_POOLING_H_
-#define TENSORFLOW_LITE_MICRO_KERNELS_POOLING_H_
-
-#include <cstdint>
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/micro/kernels/micro_ops.h"
-
-namespace tflite {
-
-extern const int kPoolingInputTensor;
-extern const int kPoolingOutputTensor;
-
-struct OpDataPooling {
-  TfLitePaddingValues padding;
-  int32_t activation_min;
-  int32_t activation_max;
-  float activation_min_f32;
-  float activation_max_f32;
-};
-
-TfLiteStatus CalculateOpDataPooling(const TfLiteContext* context,
-                                    const TfLitePoolParams* params,
-                                    const TfLiteTensor* input,
-                                    const TfLiteTensor* output,
-                                    OpDataPooling* data);
-
-TfLiteStatus PoolingPrepare(TfLiteContext* context, TfLiteNode* node);
-
-void AveragePoolingEvalFloat(const TfLiteContext* context,
-                             const TfLiteNode* node,
-                             const TfLitePoolParams* params,
-                             const OpDataPooling* data,
-                             const TfLiteEvalTensor* input,
-                             TfLiteEvalTensor* output);
-
-void AveragePoolingEvalQuantized(TfLiteContext* context, const TfLiteNode* node,
-                                 const TfLitePoolParams* params,
-                                 const OpDataPooling* data,
-                                 const TfLiteEvalTensor* input,
-                                 TfLiteEvalTensor* output);
-
-void MaxPoolingEvalFloat(TfLiteContext* context, TfLiteNode* node,
-                         TfLitePoolParams* params, const OpDataPooling* data,
-                         const TfLiteEvalTensor* input,
-                         TfLiteEvalTensor* output);
-
-void MaxPoolingEvalQuantized(TfLiteContext* context, TfLiteNode* node,
-                             TfLitePoolParams* params,
-                             const OpDataPooling* data,
-                             const TfLiteEvalTensor* input,
-                             TfLiteEvalTensor* output);
-
-#if defined(CMSIS_NN)
-TfLiteRegistration Register_AVERAGE_POOL_2D_INT8();
-
-TfLiteRegistration Register_MAX_POOL_2D_INT8();
-#else
-inline TfLiteRegistration Register_AVERAGE_POOL_2D_INT8() {
-  return tflite::Register_AVERAGE_POOL_2D();
-}
-
-inline TfLiteRegistration Register_MAX_POOL_2D_INT8() {
-  return tflite::Register_MAX_POOL_2D();
-}
-#endif
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_MICRO_KERNELS_POOLING_H_

code/components/tflite-lib/tensorflow/lite/micro/kernels/prelu.cc

@@ -1,74 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/prelu.h"
-
-#include <cstdint>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/prelu.h"
-
-namespace tflite {
-
-void* PreluInit(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(PreluParams));
-}
-
-TfLiteStatus PreluEval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const PreluParams& params =
-      *(static_cast<const PreluParams*>(node->user_data));
-
-  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
-  const TfLiteEvalTensor* alpha = tflite::micro::GetEvalInput(context, node, 1);
-  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
-
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      BroadcastPrelu4DSlowFloat(tflite::micro::GetTensorShape(input),
-                                tflite::micro::GetTensorData<float>(input),
-                                tflite::micro::GetTensorShape(alpha),
-                                tflite::micro::GetTensorData<float>(alpha),
-                                tflite::micro::GetTensorShape(output),
-                                tflite::micro::GetTensorData<float>(output));
-      return kTfLiteOk;
-    } break;
-    case kTfLiteInt8: {
-      reference_ops::BroadcastPrelu4DSlow(
-          params, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(alpha),
-          tflite::micro::GetTensorData<int8_t>(alpha),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int8_t>(output));
-      return kTfLiteOk;
-    } break;
-    default:
-      MicroPrintf("Only float32 and uint8_t are supported currently, got %d.",
-                  TfLiteTypeGetName(input->type));
-      return kTfLiteError;
-  }
-}
-
-TfLiteRegistration Register_PRELU() {
-  return tflite::micro::RegisterOp(PreluInit, PreluPrepare, PreluEval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/reduce_common.cc

@@ -1,374 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/reference/integer_ops/mean.h"
-#include "tensorflow/lite/kernels/internal/reference/reduce.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/reduce.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-
-namespace tflite {
-
-const int kMaxNumberOfAxis = 5;
-const int kMaxNumberOfReducedAxis = 2;
-
-TfLiteStatus PrepareSimple(TfLiteContext* context, TfLiteNode* node,
-                           int32_t* multiplier, int* shift) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  // Inputs Tensor (dtype depends on quantization):
-  // [0] = Input
-  // [1] = Axis
-  TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0);
-
-  // Outputs Tensor (dtype depends on quantization):
-  // [0] = Output
-
-  // Validate number of inputs and outputs
-  TF_LITE_ENSURE_EQ(context, node->inputs->size, 2);
-  TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
-
-  // Validate axis type
-  TfLiteTensor* axis = micro_context->AllocateTempInputTensor(node, 1);
-  TF_LITE_ENSURE(context, axis != nullptr);
-  TF_LITE_ENSURE_TYPES_EQ(context, axis->type, kTfLiteInt32);
-
-  if (input->type == kTfLiteInt8) {
-    TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0);
-    const double real_multiplier = static_cast<double>(input->params.scale) /
-                                   static_cast<double>(output->params.scale);
-    QuantizeMultiplier(real_multiplier, multiplier, shift);
-    micro_context->DeallocateTempTfLiteTensor(output);
-  }
-  micro_context->DeallocateTempTfLiteTensor(axis);
-  micro_context->DeallocateTempTfLiteTensor(input);
-  return kTfLiteOk;
-}
-
-TfLiteStatus PrepareMaxHelper(TfLiteContext* context, TfLiteNode* node,
-                              OpDataReduce* op_data) {
-  TF_LITE_ENSURE_OK(context, PrepareSimple(context, node, &op_data->multiplier,
-                                           &op_data->shift));
-
-  MicroContext* micro_context = GetMicroContext(context);
-  TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0);
-  TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0);
-  TfLiteTensor* axis = micro_context->AllocateTempInputTensor(node, 1);
-
-  op_data->input_scale = input->params.scale;
-  op_data->output_scale = output->params.scale;
-  op_data->num_output_elements = NumElements(output);
-
-  context->RequestScratchBufferInArena(context, sizeof(int) * input->dims->size,
-                                       &op_data->temp_buffer_idx);
-  context->RequestScratchBufferInArena(
-      context, sizeof(int) * static_cast<int>(ElementCount(*axis->dims)),
-      &op_data->resolved_axis_idx);
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  micro_context->DeallocateTempTfLiteTensor(axis);
-  return kTfLiteOk;
-}
-
-TfLiteStatus PrepareMeanOrSumHelper(TfLiteContext* context, TfLiteNode* node,
-                                    OpDataReduce* op_data) {
-  MicroContext* micro_context = GetMicroContext(context);
-  TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0);
-  TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0);
-  if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) {
-    const double real_multiplier = static_cast<double>(input->params.scale) /
-                                   static_cast<double>(output->params.scale);
-    QuantizeMultiplier(real_multiplier, &op_data->multiplier, &op_data->shift);
-  }
-
-  int output_size = NumElements(output);
-  if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) {
-    context->RequestScratchBufferInArena(context, output_size * sizeof(int32_t),
-                                         &op_data->temp_buffer_idx);
-    op_data->input_zp = input->params.zero_point;
-    op_data->input_scale = input->params.scale;
-    op_data->output_zp = output->params.zero_point;
-    op_data->output_scale = output->params.scale;
-  }
-
-  TF_LITE_ENSURE_OK(
-      context,
-      PrepareSimple(context, node, &(op_data->multiplier), &(op_data->shift)));
-  // TODO(b/144955155): Support uint8_t(b/144955155) and int8_t(b/144955018)
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-void ResolveAxis(const int* axis_data, int axis_count,
-                 tflite::MeanParams* op_params) {
-  int i = 0;
-  for (; i < axis_count; ++i) {
-    op_params->axis[i] = static_cast<int16_t>(axis_data[i]);
-  }
-  for (; i < 4; ++i) {
-    op_params->axis[i] = 1;
-  }
-  op_params->axis_count = axis_count;
-}
-
-TfLiteStatus EvalMeanHelper(TfLiteContext* context, TfLiteNode* node,
-                            OpDataReduce* op_data) {
-  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
-  const TfLiteEvalTensor* axis = tflite::micro::GetEvalInput(context, node, 1);
-  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
-  TfLiteReducerParams* params =
-      reinterpret_cast<TfLiteReducerParams*>(node->builtin_data);
-
-  int num_axis = static_cast<int>(ElementCount(*axis->dims));
-  int temp_index[kMaxNumberOfAxis];
-  int resolved_axis[kMaxNumberOfReducedAxis];
-
-  tflite::MeanParams op_params;
-  ResolveAxis(tflite::micro::GetTensorData<int>(axis), num_axis, &op_params);
-
-  // Special case mean implementation exists for 4D mean across axes 1 and 2.
-  bool special_case_4d_axes_1_and_2 =
-      input->dims->size == 4 && op_params.axis_count == 2 &&
-      ((op_params.axis[0] == 1 && op_params.axis[1] == 2) ||
-       (op_params.axis[0] == 2 && op_params.axis[1] == 1));
-
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      // Defer to specialized implementation for 4D Mean across axes 1 & 2.
-      if (params->keep_dims && special_case_4d_axes_1_and_2) {
-        reference_ops::Mean(op_params, tflite::micro::GetTensorShape(input),
-                            tflite::micro::GetTensorData<float>(input),
-                            tflite::micro::GetTensorShape(output),
-                            tflite::micro::GetTensorData<float>(output));
-      } else {
-        TF_LITE_ENSURE(
-            context,
-            reference_ops::Mean(
-                tflite::micro::GetTensorData<float>(input), input->dims->data,
-                input->dims->size, tflite::micro::GetTensorData<float>(output),
-                output->dims->data, output->dims->size,
-                tflite::micro::GetTensorData<int>(axis), num_axis,
-                params->keep_dims, temp_index, resolved_axis,
-                tflite::micro::GetTensorData<float>(output)));
-      }
-    } break;
-    case kTfLiteInt8: {
-      // Defer to specialized implementation for 4D Mean across axes 1 & 2.
-      if (params->keep_dims && special_case_4d_axes_1_and_2) {
-        reference_integer_ops::Mean(
-            op_params, op_data->multiplier, op_data->shift,
-            tflite::micro::GetTensorShape(input),
-            tflite::micro::GetTensorData<int8_t>(input), op_data->input_zp,
-            tflite::micro::GetTensorShape(output),
-            tflite::micro::GetTensorData<int8_t>(output), op_data->output_zp);
-      } else if (op_data->input_zp == op_data->output_zp &&
-                 op_data->input_scale == op_data->output_scale) {
-        int32_t* temp_buffer = static_cast<int32_t*>(
-            context->GetScratchBuffer(context, op_data->temp_buffer_idx));
-        TF_LITE_ENSURE(
-            context,
-            reference_ops::Mean(
-                tflite::micro::GetTensorData<int8_t>(input), input->dims->data,
-                input->dims->size, tflite::micro::GetTensorData<int8_t>(output),
-                output->dims->data, output->dims->size,
-                tflite::micro::GetTensorData<int>(axis), num_axis,
-                params->keep_dims, temp_index, resolved_axis, temp_buffer));
-      } else {
-        int32_t* temp_buffer = static_cast<int32_t*>(
-            context->GetScratchBuffer(context, op_data->temp_buffer_idx));
-        TF_LITE_ENSURE(
-            context,
-            reference_ops::QuantizedMeanOrSum(
-                tflite::micro::GetTensorData<int8_t>(input), op_data->input_zp,
-                op_data->input_scale, input->dims->data, input->dims->size,
-                tflite::micro::GetTensorData<int8_t>(output),
-                op_data->output_zp, op_data->output_scale, output->dims->data,
-                output->dims->size, tflite::micro::GetTensorData<int>(axis),
-                num_axis, params->keep_dims, temp_index, resolved_axis,
-                temp_buffer, false));
-      }
-    } break;
-    case kTfLiteInt16: {
-      // Defer to specialized implementation for 4D Mean across axes 1 & 2.
-      if (params->keep_dims && special_case_4d_axes_1_and_2) {
-        reference_integer_ops::Mean(
-            op_params, op_data->multiplier, op_data->shift,
-            tflite::micro::GetTensorShape(input),
-            tflite::micro::GetTensorData<int16_t>(input), op_data->input_zp,
-            tflite::micro::GetTensorShape(output),
-            tflite::micro::GetTensorData<int16_t>(output), op_data->output_zp);
-      } else if (op_data->input_zp == op_data->output_zp &&
-                 op_data->input_scale == op_data->output_scale) {
-        int32_t* temp_buffer = static_cast<int32_t*>(
-            context->GetScratchBuffer(context, op_data->temp_buffer_idx));
-        TF_LITE_ENSURE(
-            context,
-            reference_ops::Mean(tflite::micro::GetTensorData<int16_t>(input),
-                                input->dims->data, input->dims->size,
-                                tflite::micro::GetTensorData<int16_t>(output),
-                                output->dims->data, output->dims->size,
-                                tflite::micro::GetTensorData<int>(axis),
-                                num_axis, params->keep_dims, temp_index,
-                                resolved_axis, temp_buffer));
-      } else {
-        int32_t* temp_buffer = static_cast<int32_t*>(
-            context->GetScratchBuffer(context, op_data->temp_buffer_idx));
-        TF_LITE_ENSURE(
-            context,
-            reference_ops::QuantizedMeanOrSum(
-                tflite::micro::GetTensorData<int16_t>(input), op_data->input_zp,
-                op_data->input_scale, input->dims->data, input->dims->size,
-                tflite::micro::GetTensorData<int16_t>(output),
-                op_data->output_zp, op_data->output_scale, output->dims->data,
-                output->dims->size, tflite::micro::GetTensorData<int>(axis),
-                num_axis, params->keep_dims, temp_index, resolved_axis,
-                temp_buffer, false));
-      }
-    } break;
-    default:
-      TF_LITE_ENSURE_MSG(context, false,
-                         "Currently, only float32, int8 or int16 input type "
-                         "is supported.");
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus EvalMaxHelper(TfLiteContext* context, TfLiteNode* node,
-                           OpDataReduce* op_data) {
-  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
-  const TfLiteEvalTensor* axis = tflite::micro::GetEvalInput(context, node, 1);
-  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-  TfLiteReducerParams* params =
-      static_cast<TfLiteReducerParams*>(node->builtin_data);
-
-  // Interpret an axis tensor with null dimensions as a scalar
-  int num_axis = static_cast<int>(ElementCount(*axis->dims));
-  int* temp_buffer = static_cast<int*>(
-      context->GetScratchBuffer(context, op_data->temp_buffer_idx));
-  int* resolved_axis = static_cast<int*>(
-      context->GetScratchBuffer(context, op_data->resolved_axis_idx));
-  switch (input->type) {
-    case kTfLiteFloat32:
-      TF_LITE_ENSURE(
-          context,
-          reference_ops::ReduceGeneric<float>(
-              tflite::micro::GetTensorData<float>(input), input->dims->data,
-              input->dims->size, tflite::micro::GetTensorData<float>(output),
-              output->dims->data, output->dims->size,
-              tflite::micro::GetTensorData<int>(axis), num_axis,
-              params->keep_dims, temp_buffer, resolved_axis,
-              std::numeric_limits<float>::lowest(),
-              [](const float current, const float in) -> float {
-                return (in > current) ? in : current;
-              }));
-      break;
-    case kTfLiteInt8:
-      TF_LITE_ENSURE_EQ(context, static_cast<double>(op_data->input_scale),
-                        static_cast<double>(op_data->output_scale));
-      TF_LITE_ENSURE_EQ(context, op_data->input_zp, op_data->output_zp);
-      TF_LITE_ENSURE(
-          context,
-          reference_ops::ReduceGeneric<int8_t>(
-              tflite::micro::GetTensorData<int8_t>(input), input->dims->data,
-              input->dims->size, tflite::micro::GetTensorData<int8_t>(output),
-              output->dims->data, output->dims->size,
-              tflite::micro::GetTensorData<int>(axis), num_axis,
-              params->keep_dims, temp_buffer, resolved_axis,
-              std::numeric_limits<int8_t>::lowest(),
-              [](const int8_t current, const int8_t in) -> int8_t {
-                return (in > current) ? in : current;
-              }));
-      break;
-    default:
-      MicroPrintf("Only float32 and int8 types are supported.");
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus EvalSumHelper(TfLiteContext* context, TfLiteNode* node,
-                           OpDataReduce* op_data) {
-  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
-  const TfLiteEvalTensor* axis = tflite::micro::GetEvalInput(context, node, 1);
-  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-  TfLiteReducerParams* params =
-      static_cast<TfLiteReducerParams*>(node->builtin_data);
-
-  // Interpret an axis tensor with null dimensions as a scalar.
-  int num_axis = static_cast<int>(ElementCount(*axis->dims));
-  int temp_index[kMaxNumberOfAxis];
-  int resolved_axis[kMaxNumberOfReducedAxis];
-
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      TF_LITE_ENSURE(
-          context,
-          reference_ops::ReduceGeneric<float>(
-              tflite::micro::GetTensorData<float>(input), input->dims->data,
-              input->dims->size, tflite::micro::GetTensorData<float>(output),
-              output->dims->data, output->dims->size,
-              tflite::micro::GetTensorData<int>(axis), num_axis,
-              params->keep_dims, temp_index, resolved_axis, /*init_value=*/0.f,
-              [](const float current, const float in) -> float {
-                return in + current;
-              }));
-    } break;
-    case kTfLiteInt8: {
-      int32_t* temp_buffer = static_cast<int32_t*>(
-          context->GetScratchBuffer(context, op_data->temp_buffer_idx));
-      TF_LITE_ENSURE(
-          context,
-          reference_ops::QuantizedMeanOrSum(
-              tflite::micro::GetTensorData<int8_t>(input), op_data->input_zp,
-              op_data->input_scale, input->dims->data, input->dims->size,
-              tflite::micro::GetTensorData<int8_t>(output), op_data->output_zp,
-              op_data->output_scale, output->dims->data, output->dims->size,
-              tflite::micro::GetTensorData<int>(axis), num_axis,
-              params->keep_dims, temp_index, resolved_axis, temp_buffer,
-              /*compute_sum=*/true));
-    } break;
-    case kTfLiteInt16: {
-      int32_t* temp_buffer = static_cast<int32_t*>(
-          context->GetScratchBuffer(context, op_data->temp_buffer_idx));
-      TF_LITE_ENSURE(
-          context,
-          reference_ops::QuantizedMeanOrSum(
-              tflite::micro::GetTensorData<int16_t>(input), op_data->input_zp,
-              op_data->input_scale, input->dims->data, input->dims->size,
-              tflite::micro::GetTensorData<int16_t>(output), op_data->output_zp,
-              op_data->output_scale, output->dims->data, output->dims->size,
-              tflite::micro::GetTensorData<int>(axis), num_axis,
-              params->keep_dims, temp_index, resolved_axis, temp_buffer,
-              /*compute_sum=*/true));
-    } break;
-    default:
-      MicroPrintf("Only float32, int8, and int16 types are supported.");
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/resize_bilinear.cc

@@ -1,115 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/kernels/internal/reference/resize_bilinear.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-
-namespace tflite {
-namespace {
-
-constexpr int kInputTensor = 0;
-constexpr int kSizeTensor = 1;
-constexpr int kOutputTensor = 0;
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TfLiteTensor* size =
-      micro_context->AllocateTempInputTensor(node, kSizeTensor);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-
-  TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
-  TF_LITE_ENSURE_EQ(context, NumDimensions(size), 1);
-
-  TF_LITE_ENSURE_EQ(context, size->type, kTfLiteInt32);
-  output->type = input->type;
-
-  TF_LITE_ENSURE_MSG(context, IsConstantTensor(size),
-                     "Non constant size tensor not supported");
-
-  // Ensure params are valid.
-  auto* params =
-      reinterpret_cast<TfLiteResizeBilinearParams*>(node->builtin_data);
-  if (params->half_pixel_centers && params->align_corners) {
-    MicroPrintf("If half_pixel_centers is True, align_corners must be False.");
-    return kTfLiteError;
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(size);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  auto* params =
-      reinterpret_cast<TfLiteResizeBilinearParams*>(node->builtin_data);
-
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  const TfLiteEvalTensor* size =
-      tflite::micro::GetEvalInput(context, node, kSizeTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  if (output->type == kTfLiteFloat32) {
-    tflite::ResizeBilinearParams op_params;
-    op_params.align_corners = params->align_corners;
-    op_params.half_pixel_centers = params->half_pixel_centers;
-    reference_ops::ResizeBilinear(op_params,
-                                  tflite::micro::GetTensorShape(input),
-                                  tflite::micro::GetTensorData<float>(input),
-                                  tflite::micro::GetTensorShape(size),
-                                  tflite::micro::GetTensorData<int32_t>(size),
-                                  tflite::micro::GetTensorShape(output),
-                                  tflite::micro::GetTensorData<float>(output));
-  } else if (output->type == kTfLiteInt8) {
-    tflite::ResizeBilinearParams op_params;
-    op_params.align_corners = params->align_corners;
-    op_params.half_pixel_centers = params->half_pixel_centers;
-    reference_ops::ResizeBilinearInteger(
-        op_params, tflite::micro::GetTensorShape(input),
-        tflite::micro::GetTensorData<int8_t>(input),
-        tflite::micro::GetTensorShape(size),
-        tflite::micro::GetTensorData<int32_t>(size),
-        tflite::micro::GetTensorShape(output),
-        tflite::micro::GetTensorData<int8_t>(output));
-  } else {
-    MicroPrintf("Output type is %d, requires float or int8.", output->type);
-    return kTfLiteError;
-  }
-
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_RESIZE_BILINEAR() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/resize_nearest_neighbor.cc

@@ -1,125 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/resize_nearest_neighbor.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace resize_nearest_neighbor {
-
-constexpr int kInputTensor = 0;
-constexpr int kSizeTensor = 1;
-constexpr int kOutputTensor = 0;
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TfLiteTensor* size =
-      micro_context->AllocateTempInputTensor(node, kSizeTensor);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-
-  // Our current implementations rely on the input being 4D,
-  // and the size being 1D tensor with exactly 2 elements.
-  TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
-  TF_LITE_ENSURE_EQ(context, NumDimensions(size), 1);
-  TF_LITE_ENSURE_EQ(context, size->type, kTfLiteInt32);
-  TF_LITE_ENSURE_EQ(context, size->dims->data[0], 2);
-
-  output->type = input->type;
-
-  if (!IsConstantTensor(size)) {
-    MicroPrintf("Dynamic tensors are unsupported in tfmicro.");
-    return kTfLiteError;
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(size);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  auto* params =
-      reinterpret_cast<TfLiteResizeNearestNeighborParams*>(node->builtin_data);
-
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  const TfLiteEvalTensor* size =
-      tflite::micro::GetEvalInput(context, node, kSizeTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  tflite::ResizeNearestNeighborParams op_params;
-  op_params.align_corners = params->align_corners;
-  op_params.half_pixel_centers = false;
-
-  if (output->type == kTfLiteFloat32) {
-    reference_ops::ResizeNearestNeighbor(
-        op_params, tflite::micro::GetTensorShape(input),
-        tflite::micro::GetTensorData<int32_t>(input),
-        tflite::micro::GetTensorShape(size),
-        tflite::micro::GetTensorData<int32_t>(size),
-        tflite::micro::GetTensorShape(output),
-        tflite::micro::GetTensorData<int32_t>(output));
-  } else if (output->type == kTfLiteInt8) {
-    reference_ops::ResizeNearestNeighbor(
-        op_params, tflite::micro::GetTensorShape(input),
-        tflite::micro::GetTensorData<int8_t>(input),
-        tflite::micro::GetTensorShape(size),
-        tflite::micro::GetTensorData<int32_t>(size),
-        tflite::micro::GetTensorShape(output),
-        tflite::micro::GetTensorData<int8_t>(output));
-  } else if (output->type == kTfLiteInt16) {
-    reference_ops::ResizeNearestNeighbor(
-        op_params, tflite::micro::GetTensorShape(input),
-        tflite::micro::GetTensorData<int16_t>(input),
-        tflite::micro::GetTensorShape(size),
-        tflite::micro::GetTensorData<int32_t>(size),
-        tflite::micro::GetTensorShape(output),
-        tflite::micro::GetTensorData<int16_t>(output));
-  } else {
-    MicroPrintf("Output tensor type %s (%d) not supported.",
-                TfLiteTypeGetName(output->type), output->type);
-
-    return kTfLiteError;
-  }
-
-  return kTfLiteOk;
-}
-}  // namespace resize_nearest_neighbor
-
-TfLiteRegistration Register_RESIZE_NEAREST_NEIGHBOR() {
-  return tflite::micro::RegisterOp(nullptr, resize_nearest_neighbor::Prepare,
-                                   resize_nearest_neighbor::Eval);
-}
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/select.cc

@@ -1,196 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/kernels/internal/reference/select.h"
-
-#include <stddef.h>
-#include <stdint.h>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-
-constexpr int kInputTensorCondition = 0;
-constexpr int kInputTensorX = 1;
-constexpr int kInputTensorY = 2;
-constexpr int kOutputTensor = 0;
-
-struct OpData {
-  bool requires_broadcast;
-  // True if input condition is scalar or input condition has rank one and
-  // matches the first dimension of other inputs.
-  bool has_low_rank_input_condition;
-};
-
-void* SelectInit(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  auto* data = static_cast<OpData*>(
-      context->AllocatePersistentBuffer(context, sizeof(OpData)));
-  data->requires_broadcast = false;
-  data->has_low_rank_input_condition = false;
-  return data;
-}
-
-TfLiteStatus CheckBroadcastShape(TfLiteContext* context,
-                                 const TfLiteTensor* input1,
-                                 const TfLiteTensor* input2,
-                                 const TfLiteTensor* input3,
-                                 const TfLiteIntArray* output_shape) {
-  const int dims1 = NumDimensions(input1);
-  const int dims2 = NumDimensions(input2);
-  const int dims3 = NumDimensions(input3);
-  const int out_dims = std::max(std::max(dims1, dims2), dims3);
-  TF_LITE_ENSURE_EQ(context, out_dims, output_shape->size);
-
-  for (int i = 0; i < out_dims; ++i) {
-    const int d1 = i >= dims1 ? 1 : SizeOfDimension(input1, dims1 - i - 1);
-    const int d2 = i >= dims2 ? 1 : SizeOfDimension(input2, dims2 - i - 1);
-    const int d3 = i >= dims3 ? 1 : SizeOfDimension(input3, dims3 - i - 1);
-    const int min_value = std::min(std::min(d1, d2), d3);
-    int max_value = std::max(std::max(d1, d2), d3);
-    // If one dimention is 0, others must be 0 or 1.
-    if (min_value == 0) max_value = 0;
-    if (!(d1 == 1 || d1 == max_value) || !(d2 == 1 || d2 == max_value) ||
-        !(d3 == 1 || d3 == max_value)) {
-      MicroPrintf("Given shapes are not broadcastable.");
-      return kTfLiteError;
-    }
-    TF_LITE_ENSURE_EQ(context, output_shape->data[out_dims - i - 1], max_value);
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus SelectPrepare(TfLiteContext* context, TfLiteNode* node) {
-  OpData* data = reinterpret_cast<OpData*>(node->user_data);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  MicroContext* micro_context = GetMicroContext(context);
-  TfLiteTensor* input_condition =
-      micro_context->AllocateTempInputTensor(node, kInputTensorCondition);
-
-  TfLiteTensor* input_x =
-      micro_context->AllocateTempInputTensor(node, kInputTensorX);
-
-  TfLiteTensor* input_y =
-      micro_context->AllocateTempInputTensor(node, kInputTensorY);
-
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-
-  // Input must be bool.
-  TF_LITE_ENSURE_TYPES_EQ(context, input_condition->type, kTfLiteBool);
-  TF_LITE_ENSURE_TYPES_EQ(context, input_x->type, input_y->type);
-  output->type = input_x->type;
-
-  // Respect the original output shape when there are mixed shapes to represent
-  // a scalar data.
-  if (GetTensorShape(input_condition).FlatSize() == 1 &&
-      GetTensorShape(input_x).FlatSize() == 1 &&
-      GetTensorShape(input_y).FlatSize() == 1 &&
-      GetTensorShape(output).FlatSize() == 1) {
-    return kTfLiteOk;
-  }
-
-  bool same_shape = HaveSameShapes(input_condition, input_x) &&
-                    HaveSameShapes(input_x, input_y);
-  if (!same_shape) {
-    TF_LITE_ENSURE_OK(
-        context, CheckBroadcastShape(context, input_condition, input_x, input_y,
-                                     output->dims));
-    data->requires_broadcast = true;
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(input_condition);
-  micro_context->DeallocateTempTfLiteTensor(input_x);
-  micro_context->DeallocateTempTfLiteTensor(input_y);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus SelectEval(TfLiteContext* context, TfLiteNode* node) {
-  OpData* data = static_cast<OpData*>(node->user_data);
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TfLiteTensor* input_condition =
-      micro_context->AllocateTempInputTensor(node, kInputTensorCondition);
-
-  TfLiteTensor* input_x =
-      micro_context->AllocateTempInputTensor(node, kInputTensorX);
-
-  TfLiteTensor* input_y =
-      micro_context->AllocateTempInputTensor(node, kInputTensorY);
-
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-
-#define TF_LITE_SELECT(type, op)                                           \
-  reference_ops::op(GetTensorShape(input_condition),                       \
-                    GetTensorData<bool>(input_condition),                  \
-                    GetTensorShape(input_x), GetTensorData<type>(input_x), \
-                    GetTensorShape(input_y), GetTensorData<type>(input_y), \
-                    GetTensorShape(output), GetTensorData<type>(output));
-
-#define TF_LITE_SWITCH(type, op)                                     \
-  switch (type) {                                                    \
-    case kTfLiteFloat32:                                             \
-      TF_LITE_SELECT(float, op);                                     \
-      break;                                                         \
-    case kTfLiteInt8:                                                \
-      TF_LITE_SELECT(int8_t, op);                                    \
-      break;                                                         \
-    case kTfLiteInt16:                                               \
-      TF_LITE_SELECT(int16_t, op);                                   \
-      break;                                                         \
-    default:                                                         \
-      MicroPrintf("Does not support type other than %s, but got %s", \
-                  "int8|int16|float32", TfLiteTypeGetName(type));    \
-      return kTfLiteError;                                           \
-  }
-
-  if (data->has_low_rank_input_condition) {
-    MicroPrintf("Not yet implemented.");
-    return kTfLiteError;
-  } else if (data->requires_broadcast) {
-    TF_LITE_SWITCH(input_x->type, BroadcastSelect5DSlow);
-  } else {
-    TF_LITE_SWITCH(input_x->type, Select);
-  }
-
-#undef TF_LITE_SELECT
-#undef TF_LITE_SWITCH
-  micro_context->DeallocateTempTfLiteTensor(input_condition);
-  micro_context->DeallocateTempTfLiteTensor(input_x);
-  micro_context->DeallocateTempTfLiteTensor(input_y);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-// SelectV2 op selects values of 'x' if the corresponding value of 'condition'
-// is true or the value of 'y' if false. There are valid condition input sizes:
-//
-// 1. Either the same shape (in which case the select is elementwise), or
-// 2. Broadcastable shapes between 'condition', 'x' and 'y'.
-TfLiteRegistration Register_SELECT_V2() {
-  return tflite::micro::RegisterOp(tflite::SelectInit, tflite::SelectPrepare,
-                                   tflite::SelectEval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/shape.cc

@@ -1,66 +0,0 @@
-/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/memory_helpers.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-
-namespace tflite {
-
-namespace {
-constexpr int kInputTensor = 0;
-constexpr int kOutputTensor = 0;
-
-void ExtractShape(const TfLiteEvalTensor* input, int32_t* output_data) {
-  for (int i = 0; i < input->dims->size; ++i) {
-    output_data[i] = input->dims->data[i];
-  }
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  if (output->type != kTfLiteInt32) {
-    MicroPrintf("Output type %s (%d) not supported.",
-                TfLiteTypeGetName(output->type), output->type);
-    return kTfLiteError;
-  } else {
-    ExtractShape(input, tflite::micro::GetTensorData<int32_t>(output));
-  }
-
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_SHAPE() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/slice.cc

@@ -1,157 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/slice.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-
-namespace tflite {
-
-namespace {
-
-constexpr int kInputTensor = 0;
-constexpr int kBeginTensor = 1;
-constexpr int kSizeTensor = 2;
-constexpr int kOutputTensor = 0;
-
-const int kMaxDim = 5;
-
-template <typename T>
-void GetBeginAndSizeVectors(int dimensions, const TfLiteEvalTensor* begin,
-                            const TfLiteEvalTensor* size, int32_t* begins,
-                            int32_t* sizes) {
-  int offset = kMaxDim - dimensions;
-  for (int idx = 0; idx < dimensions; ++idx) {
-    begins[offset + idx] = tflite::micro::GetTensorData<T>(begin)[idx];
-    sizes[offset + idx] = tflite::micro::GetTensorData<T>(size)[idx];
-  }
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TFLITE_DCHECK(input != nullptr);
-  TfLiteTensor* begin =
-      micro_context->AllocateTempInputTensor(node, kBeginTensor);
-  TFLITE_DCHECK(begin != nullptr);
-  TfLiteTensor* size =
-      micro_context->AllocateTempInputTensor(node, kSizeTensor);
-  TFLITE_DCHECK(size != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TFLITE_DCHECK(output != nullptr);
-
-  // Ensure validity of input tensor and its dimension.
-  TFLITE_DCHECK(input->type == output->type);
-  TFLITE_DCHECK(begin->type == size->type);
-  TFLITE_DCHECK(begin->type == kTfLiteInt32 || begin->type == kTfLiteInt64);
-  TFLITE_DCHECK(size->type == kTfLiteInt32 || size->type == kTfLiteInt64);
-  TFLITE_DCHECK(NumDimensions(begin) == 1);
-  TFLITE_DCHECK(NumDimensions(size) == 1);
-  TFLITE_DCHECK(NumElements(begin) == NumElements(size));
-  TFLITE_DCHECK(NumDimensions(input) <= kMaxDim);
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(begin);
-  micro_context->DeallocateTempTfLiteTensor(size);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  const TfLiteEvalTensor* begin =
-      tflite::micro::GetEvalInput(context, node, kBeginTensor);
-  const TfLiteEvalTensor* size =
-      tflite::micro::GetEvalInput(context, node, kSizeTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  tflite::SliceParams op_params;
-  op_params.begin_count = kMaxDim;
-  op_params.size_count = kMaxDim;
-  for (int i = 0; i < kMaxDim; ++i) {
-    op_params.begin[i] = 0;
-    op_params.size[i] = 1;
-  }
-
-  if (begin->type == kTfLiteInt32) {
-    GetBeginAndSizeVectors<int32_t>(input->dims->size, begin, size,
-                                    op_params.begin, op_params.size);
-  } else if (begin->type == kTfLiteInt64) {
-    GetBeginAndSizeVectors<int64_t>(input->dims->size, begin, size,
-                                    op_params.begin, op_params.size);
-  } else {
-    MicroPrintf("Begin tensor type %s (%d) not supported.",
-                TfLiteTypeGetName(input->type), input->type);
-    return kTfLiteError;
-  }
-
-  switch (input->type) {
-    case kTfLiteFloat32:
-      reference_ops::Slice<float>(op_params,
-                                  tflite::micro::GetTensorShape(input),
-                                  tflite::micro::GetTensorData<float>(input),
-                                  tflite::micro::GetTensorShape(output),
-                                  tflite::micro::GetTensorData<float>(output));
-      break;
-    case kTfLiteInt32:
-      reference_ops::Slice<int32_t>(
-          op_params, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int32_t>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int32_t>(output));
-      break;
-    case kTfLiteInt8:
-      reference_ops::Slice<int8_t>(
-          op_params, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int8_t>(output));
-      break;
-    case kTfLiteInt16:
-      reference_ops::Slice<int16_t>(
-          op_params, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int16_t>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int16_t>(output));
-      break;
-    default:
-      MicroPrintf("Input tensor type %s (%d) not supported.",
-                  TfLiteTypeGetName(input->type), input->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_SLICE() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/softmax.cc

@@ -1,89 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/kernels/softmax.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/reference/softmax.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-void SoftmaxQuantized(const TfLiteEvalTensor* input, TfLiteEvalTensor* output,
-                      const SoftmaxParams& op_data) {
-  if (input->type == kTfLiteInt8) {
-    if (output->type == kTfLiteInt16) {
-      tflite::reference_ops::Softmax(
-          op_data, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int16_t>(output));
-    } else {
-      tflite::reference_ops::Softmax(
-          op_data, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int8_t>(output));
-    }
-  } else {
-    tflite::reference_ops::SoftmaxInt16(
-        op_data, tflite::micro::GetTensorShape(input),
-        tflite::micro::GetTensorData<int16_t>(input),
-        tflite::micro::GetTensorShape(output),
-        tflite::micro::GetTensorData<int16_t>(output));
-  }
-}
-
-TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
-  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
-
-  TFLITE_DCHECK(node->user_data != nullptr);
-  SoftmaxParams op_data = *static_cast<SoftmaxParams*>(node->user_data);
-
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      tflite::reference_ops::Softmax(
-          op_data, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<float>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<float>(output));
-      return kTfLiteOk;
-    }
-    case kTfLiteInt8:
-    case kTfLiteInt16: {
-      SoftmaxQuantized(input, output, op_data);
-      return kTfLiteOk;
-    }
-    default:
-      MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
-                  input->type);
-      return kTfLiteError;
-  }
-}
-}  // namespace
-
-TfLiteRegistration Register_SOFTMAX() {
-  return tflite::micro::RegisterOp(SoftmaxInit, SoftmaxPrepare, SoftmaxEval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/space_to_batch_nd.cc

@@ -1,120 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/space_to_batch_nd.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-
-namespace tflite {
-
-namespace {
-
-constexpr int kInputTensor = 0;
-constexpr int kBlockShapeTensor = 1;
-constexpr int kCropsTensor = 2;
-constexpr int kOutputTensor = 0;
-
-// Currently, only 3D NHC and 4D NHWC input/output op_context are supported.
-// In case of 3D input, it will be extended to 3D NHWC by adding W=1.
-// The 4D array need to have exactly 2 spatial dimensions.
-// TODO(b/149952582): Support arbitrary dimension in SpaceToBatchND.
-const int kInputOutputMinDimensionNum = 3;
-const int kInputOutputMaxDimensionNum = 4;
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(SpaceToBatchParams));
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, input != nullptr && output != nullptr);
-
-  TF_LITE_ENSURE(context, NumDimensions(input) >= kInputOutputMinDimensionNum);
-  TF_LITE_ENSURE(context, NumDimensions(output) >= kInputOutputMinDimensionNum);
-  TF_LITE_ENSURE(context, NumDimensions(input) <= kInputOutputMaxDimensionNum);
-  TF_LITE_ENSURE(context, NumDimensions(output) <= kInputOutputMaxDimensionNum);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const SpaceToBatchParams& params =
-      *(static_cast<const SpaceToBatchParams*>(node->user_data));
-
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  const TfLiteEvalTensor* block_shape =
-      tflite::micro::GetEvalInput(context, node, kBlockShapeTensor);
-  const TfLiteEvalTensor* crops =
-      tflite::micro::GetEvalInput(context, node, kCropsTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  switch (input->type) {  // Already know in/out types are same.
-    case kTfLiteFloat32:
-      reference_ops::SpaceToBatchND(
-          params, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<float>(input),
-          tflite::micro::GetTensorShape(block_shape),
-          tflite::micro::GetTensorData<int32_t>(block_shape),
-          tflite::micro::GetTensorShape(crops),
-          tflite::micro::GetTensorData<int32_t>(crops),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<float>(output));
-      break;
-    case kTfLiteInt8:
-      reference_ops::SpaceToBatchND(
-          params, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(block_shape),
-          tflite::micro::GetTensorData<int32_t>(block_shape),
-          tflite::micro::GetTensorShape(crops),
-          tflite::micro::GetTensorData<int32_t>(crops),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int8_t>(output));
-      break;
-    default:
-      MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
-                  input->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace.
-
-TfLiteRegistration Register_SPACE_TO_BATCH_ND() {
-  return tflite::micro::RegisterOp(Init, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/space_to_depth.cc

@@ -1,126 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/kernels/internal/reference/space_to_depth.h"
-
-#include <stdint.h>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-
-namespace {
-
-constexpr int kInputTensor = 0;
-constexpr int kOutputTensor = 0;
-constexpr int kBatchRank = 0;
-constexpr int kHeightRank = 1;
-constexpr int kWidthRank = 2;
-constexpr int kDepthRank = 3;
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  auto* params =
-      reinterpret_cast<TfLiteSpaceToDepthParams*>(node->builtin_data);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
-
-  auto data_type = output->type;
-  TF_LITE_ENSURE(context,
-                 data_type == kTfLiteFloat32 || data_type == kTfLiteInt8);
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-
-  const int block_size = params->block_size;
-  const int input_height = input->dims->data[kHeightRank];
-  const int input_width = input->dims->data[kWidthRank];
-  int output_height = input_height / block_size;
-  int output_width = input_width / block_size;
-
-  TF_LITE_ENSURE_EQ(context, input_height, output_height * block_size);
-  TF_LITE_ENSURE_EQ(context, input_width, output_width * block_size);
-
-  // Relocate dims to the persistent storage arena before changing them,
-  // otherwise we'd be modifying temporary copies made by the interpreters each
-  // time they process the layer.
-  TfLiteEvalTensor* output_eval =
-      micro::GetEvalOutput(context, node, kOutputTensor);
-  TF_LITE_ENSURE_OK(context, micro::CreateWritableTensorDimsWithCopy(
-                                 context, output, output_eval));
-
-  output->dims->data[kBatchRank] = input->dims->data[kBatchRank];
-  output->dims->data[kHeightRank] = output_height;
-  output->dims->data[kWidthRank] = output_width;
-  output->dims->data[kDepthRank] =
-      input->dims->data[kDepthRank] * block_size * block_size;
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  auto* params =
-      reinterpret_cast<TfLiteSpaceToDepthParams*>(node->builtin_data);
-
-  const TfLiteEvalTensor* input =
-      micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output = micro::GetEvalOutput(context, node, kOutputTensor);
-
-  SpaceToDepthParams op_params;
-  op_params.block_size = params->block_size;
-
-  switch (input->type) {  // Already know in/out types are same.
-    case kTfLiteFloat32:
-      reference_ops::SpaceToDepth(op_params, micro::GetTensorShape(input),
-                                  micro::GetTensorData<float>(input),
-                                  micro::GetTensorShape(output),
-                                  micro::GetTensorData<float>(output));
-      break;
-    case kTfLiteInt8:
-      reference_ops::SpaceToDepth(op_params, micro::GetTensorShape(input),
-                                  micro::GetTensorData<int8_t>(input),
-                                  micro::GetTensorShape(output),
-                                  micro::GetTensorData<int8_t>(output));
-      break;
-    default:
-      MicroPrintf("SPACE_TO_DEPTH only supports FLOAT32 and INT8, got %s.",
-                  TfLiteTypeGetName(input->type));
-      return kTfLiteError;
-  }
-
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_SPACE_TO_DEPTH() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/split_v.cc

@@ -1,129 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace split_v {
-
-template <typename T>
-TfLiteStatus SplitImpl(TfLiteContext* context, TfLiteNode* node,
-                       const TfLiteEvalTensor* input, int axis_value) {
-  const TfLiteIntArray* input_dims = input->dims;
-  const TfLiteEvalTensor* output0 =
-      tflite::micro::GetEvalOutput(context, node, 0);
-
-  const int split_dimensions = input_dims->size;
-
-  TFLITE_DCHECK_LT(axis_value, split_dimensions);
-  TFLITE_DCHECK_EQ(output0->dims->size, split_dimensions);
-
-  int64_t split_size = 0;
-  const int output_count = NumOutputs(node);
-  for (int i = 0; i < output_count; i++) {
-    split_size +=
-        tflite::micro::GetEvalOutput(context, node, i)->dims->data[axis_value];
-  }
-  TFLITE_DCHECK_EQ(split_size, input_dims->data[axis_value]);
-  int64_t outer_size = 1;
-  for (int i = 0; i < axis_value; ++i) {
-    outer_size *= input_dims->data[i];
-  }
-
-  int64_t base_inner_size = 1;
-  for (int i = axis_value + 1; i < split_dimensions; ++i) {
-    base_inner_size *= input_dims->data[i];
-  }
-
-  const T* input_ptr = tflite::micro::GetTensorData<T>(input);
-  for (int k = 0; k < outer_size; ++k) {
-    for (int i = 0; i < output_count; ++i) {
-      TfLiteEvalTensor* output_tensor =
-          tflite::micro::GetEvalOutput(context, node, i);
-      T* output_data = tflite::micro::GetTensorData<T>(output_tensor);
-      const int copy_size =
-          output_tensor->dims->data[axis_value] * base_inner_size;
-      T* output_ptr = output_data + k * copy_size;
-      for (int j = 0; j < copy_size; ++j) output_ptr[j] = input_ptr[j];
-      input_ptr += copy_size;
-    }
-  }
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 3);
-
-  MicroContext* micro_context = GetMicroContext(context);
-  // Dynamic output tensors are needed if axis tensor is not constant.
-  // But Micro doesn't support dynamic memory allocation, so we only support
-  // constant axis tensor for now.
-  TfLiteTensor* axis = micro_context->AllocateTempInputTensor(node, 2);
-  TF_LITE_ENSURE_MSG(context, IsConstantTensor(axis),
-                     "Non constant axis tensor not supported");
-  micro_context->DeallocateTempTfLiteTensor(axis);
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
-  const TfLiteEvalTensor* axis = tflite::micro::GetEvalInput(context, node, 2);
-
-  int axis_value = tflite::micro::GetTensorData<int32_t>(axis)[0];
-  if (axis_value < 0) {
-    axis_value += input->dims->size;
-  }
-
-  TF_LITE_ENSURE(context, axis_value >= 0);
-  TF_LITE_ENSURE(context, axis_value < input->dims->size);
-
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      return SplitImpl<float>(context, node, input, axis_value);
-    }
-    case kTfLiteInt8: {
-      return SplitImpl<int8_t>(context, node, input, axis_value);
-    }
-    case kTfLiteInt16: {
-      return SplitImpl<int16_t>(context, node, input, axis_value);
-    }
-    case kTfLiteInt32: {
-      return SplitImpl<int32_t>(context, node, input, axis_value);
-    }
-    default:
-      MicroPrintf("Type %s currently not supported.",
-                  TfLiteTypeGetName(input->type));
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace split_v
-
-TfLiteRegistration Register_SPLIT_V() {
-  return tflite::micro::RegisterOp(nullptr, split_v::Prepare, split_v::Eval);
-}
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/squeeze.cc

@@ -1,117 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/memory_helpers.h"
-
-namespace tflite {
-namespace {
-
-struct SqueezeContext {
-  SqueezeContext(TfLiteContext* context, TfLiteNode* node) {
-    params = reinterpret_cast<TfLiteSqueezeParams*>(node->builtin_data);
-    micro_context = GetMicroContext(context);
-    input = micro_context->AllocateTempInputTensor(node, 0);
-    output = micro_context->AllocateTempOutputTensor(node, 0);
-  }
-  ~SqueezeContext() {
-    micro_context->DeallocateTempTfLiteTensor(input);
-    micro_context->DeallocateTempTfLiteTensor(output);
-  }
-  MicroContext* micro_context;
-  TfLiteSqueezeParams* params;
-  TfLiteTensor* input;
-  TfLiteTensor* output;
-};
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  SqueezeContext op_context(context, node);
-  const int input_num_dims = NumDimensions(op_context.input);
-  const int num_squeeze_dims = op_context.params->num_squeeze_dims;
-
-  // Determines number of dimensions of output tensor after squeeze.
-  const TfLiteIntArray* input_dims = op_context.input->dims;
-  const TfLiteIntArray* output_dims = op_context.output->dims;
-  const int* squeeze_dims = op_context.params->squeeze_dims;
-
-  constexpr int max_squeeze_dims = 8;
-  TF_LITE_ENSURE(context, input_num_dims <= max_squeeze_dims);
-  bool should_squeeze[max_squeeze_dims] = {};
-
-  if (num_squeeze_dims == 0) {
-    for (int idx = 0; idx < input_num_dims; ++idx) {
-      if (input_dims->data[idx] == 1) {
-        should_squeeze[idx] = true;
-      }
-    }
-  } else {
-    for (int idx = 0; idx < num_squeeze_dims; ++idx) {
-      int current = squeeze_dims[idx] < 0 ? squeeze_dims[idx] + input_num_dims
-                                          : squeeze_dims[idx];
-      TF_LITE_ENSURE(context, current >= 0 && current < input_num_dims &&
-                                  input_dims->data[current] == 1);
-      should_squeeze[current] = true;
-    }
-  }
-
-  // Ensure output dimensions are big enough.
-  for (int in_idx = 0, out_idx = 0; in_idx < input_num_dims; ++in_idx) {
-    if (!should_squeeze[in_idx]) {
-      TFLITE_CHECK_GE(output_dims->data[out_idx++], input_dims->data[in_idx]);
-    }
-  }
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
-
-  if (input->type == kTfLiteString) {
-    MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
-                input->type);
-    return kTfLiteError;
-  }
-
-  TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
-  size_t input_byte_size;
-  size_t output_byte_size;
-  TF_LITE_ENSURE_OK(context,
-                    TfLiteEvalTensorByteLength(input, &input_byte_size));
-  TF_LITE_ENSURE_OK(context,
-                    TfLiteEvalTensorByteLength(output, &output_byte_size));
-
-  TF_LITE_ENSURE_EQ(context, input_byte_size, output_byte_size);
-  memcpy(output->data.raw, input->data.raw, input_byte_size);
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_SQUEEZE() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/strided_slice.cc

@@ -1,209 +0,0 @@
-/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/kernels/internal/reference/strided_slice.h"
-
-#include <cmath>
-#include <cstring>
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace strided_slice {
-
-constexpr int kInputTensor = 0;
-constexpr int kBeginTensor = 1;
-constexpr int kEndTensor = 2;
-constexpr int kStridesTensor = 3;
-constexpr int kOutputTensor = 0;
-
-struct StridedSliceContext {
-  StridedSliceContext(TfLiteContext* context, TfLiteNode* node) {
-    params = reinterpret_cast<TfLiteStridedSliceParams*>(node->builtin_data);
-    micro_context = GetMicroContext(context);
-    input = micro_context->AllocateTempInputTensor(node, kInputTensor);
-    begin = micro_context->AllocateTempInputTensor(node, kBeginTensor);
-    end = micro_context->AllocateTempInputTensor(node, kEndTensor);
-    strides = micro_context->AllocateTempInputTensor(node, kStridesTensor);
-    output = micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-    dims = NumDimensions(input);
-  }
-  ~StridedSliceContext() {
-    micro_context->DeallocateTempTfLiteTensor(input);
-    micro_context->DeallocateTempTfLiteTensor(begin);
-    micro_context->DeallocateTempTfLiteTensor(end);
-    micro_context->DeallocateTempTfLiteTensor(strides);
-    micro_context->DeallocateTempTfLiteTensor(output);
-  }
-  const TfLiteStridedSliceParams* params;
-  MicroContext* micro_context;
-  TfLiteTensor* input;
-  TfLiteTensor* begin;
-  TfLiteTensor* end;
-  TfLiteTensor* strides;
-  TfLiteTensor* output;
-  int dims;
-};
-
-// This Op only supports 1-4D cases and since we use the reference 4D
-// implementation, the 1-3D tensors are mapped to 4D.
-const int kMaxDim = 4;
-
-tflite::StridedSliceParams BuildStridedSliceParams(
-    StridedSliceContext* op_context) {
-  tflite::StridedSliceParams op_params;
-  op_params.start_indices_count = op_context->dims;
-  op_params.stop_indices_count = op_context->dims;
-  op_params.strides_count = op_context->dims;
-
-  for (int i = 0; i < op_context->dims; ++i) {
-    op_params.start_indices[i] = GetTensorData<int32_t>(op_context->begin)[i];
-    op_params.stop_indices[i] = GetTensorData<int32_t>(op_context->end)[i];
-    op_params.strides[i] = GetTensorData<int32_t>(op_context->strides)[i];
-  }
-
-  op_params.begin_mask = op_context->params->begin_mask;
-  op_params.ellipsis_mask = 0;
-  op_params.end_mask = op_context->params->end_mask;
-  op_params.new_axis_mask = 0;
-  op_params.shrink_axis_mask = op_context->params->shrink_axis_mask;
-  return op_params;
-}
-
-// Processes the indexing tensors (begin, end and strides) to resize the
-// output tensor. This function is callable from both Prepare() and Eval() as
-// long as the caller ensures the indexing tensors are present.
-TfLiteStatus CheckOutputSize(TfLiteContext* context,
-                             StridedSliceContext* op_context) {
-  using ::tflite::strided_slice::StartForAxis;
-  using ::tflite::strided_slice::StopForAxis;
-  TfLiteIntArray* output_shape = op_context->output->dims;
-  int shape_size = 0;
-  auto op_params = BuildStridedSliceParams(op_context);
-  auto input_shape = GetTensorShape(op_context->input);
-  for (int idx = 0; idx < op_context->dims; ++idx) {
-    int32_t stride = GetTensorData<int32_t>(op_context->strides)[idx];
-    TF_LITE_ENSURE_MSG(context, stride != 0, "stride value has to be non-zero");
-    int32_t begin = StartForAxis(op_params, input_shape, idx);
-    int32_t end = StopForAxis(op_params, input_shape, idx, begin);
-
-    // When shrinking an axis, the end position does not matter (and can be
-    // incorrect when negative indexing is used, see Issue #19260). Always use
-    // begin + 1 to generate a length 1 slice, since begin has
-    // already been adjusted for negative indices by StartForAxis.
-    const bool shrink_axis = op_context->params->shrink_axis_mask & (1 << idx);
-    if (shrink_axis) {
-      end = begin + 1;
-    }
-
-    // This is valid for both positive and negative strides
-    int32_t dim_shape = std::ceil((end - begin) / static_cast<float>(stride));
-    dim_shape = dim_shape < 0 ? 0 : dim_shape;
-    if (!shrink_axis) {
-      TF_LITE_ENSURE_EQ(context, output_shape->data[shape_size], dim_shape);
-      shape_size++;
-    }
-  }
-  TF_LITE_ENSURE_EQ(context, output_shape->size, shape_size);
-  return kTfLiteOk;
-}
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(StridedSliceParams));
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  StridedSliceParams* op_params =
-      static_cast<StridedSliceParams*>(node->user_data);
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 4);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  StridedSliceContext op_context(context, node);
-  TF_LITE_ENSURE_MSG(context, op_context.dims <= kMaxDim,
-                     "input dim should not exceed 4");
-  auto params = BuildStridedSliceParams(&op_context);
-  memcpy(op_params, &params, sizeof(StridedSliceParams));
-  return CheckOutputSize(context, &op_context);
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const StridedSliceParams& op_params =
-      *(static_cast<const StridedSliceParams*>(node->user_data));
-
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  switch (output->type) {
-    case kTfLiteFloat32:
-      reference_ops::StridedSlice(op_params,
-                                  tflite::micro::GetTensorShape(input),
-                                  tflite::micro::GetTensorData<float>(input),
-                                  tflite::micro::GetTensorShape(output),
-                                  tflite::micro::GetTensorData<float>(output));
-      break;
-    case kTfLiteInt8:
-      reference_ops::StridedSlice(op_params,
-                                  tflite::micro::GetTensorShape(input),
-                                  tflite::micro::GetTensorData<int8_t>(input),
-                                  tflite::micro::GetTensorShape(output),
-                                  tflite::micro::GetTensorData<int8_t>(output));
-      break;
-    case kTfLiteInt16:
-      reference_ops::StridedSlice(
-          op_params, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int16_t>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int16_t>(output));
-      break;
-    case kTfLiteInt32:
-      reference_ops::StridedSlice(
-          op_params, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int32_t>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int32_t>(output));
-      break;
-    case kTfLiteBool:
-      reference_ops::StridedSlice(op_params,
-                                  tflite::micro::GetTensorShape(input),
-                                  tflite::micro::GetTensorData<bool>(input),
-                                  tflite::micro::GetTensorShape(output),
-                                  tflite::micro::GetTensorData<bool>(output));
-      break;
-    default:
-      MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
-                  input->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-}  // namespace strided_slice
-
-TfLiteRegistration Register_STRIDED_SLICE() {
-  return tflite::micro::RegisterOp(strided_slice::Init, strided_slice::Prepare,
-                                   strided_slice::Eval);
-}
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/svdf.h

@@ -1,99 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_LITE_MICRO_KERNELS_SVDF_H_
-#define TENSORFLOW_LITE_MICRO_KERNELS_SVDF_H_
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-
-namespace tflite {
-
-struct OpDataSvdf {
-  int32_t effective_scale_1_a;
-  int32_t effective_scale_2_a;
-  // b versions of each scale are kept at int since the numbers are just the
-  // shift value - typically between [-32, 32].
-  int effective_scale_1_b;
-  int effective_scale_2_b;
-  int scratch_tensor_index;
-  int scratch_output_tensor_index;
-
-  // Cached tensor zero point values for quantized operations.
-  int input_zero_point;
-  int output_zero_point;
-  int activation_state_zero_point;
-};
-
-// Input tensors.
-extern const int kSvdfInputTensor;
-extern const int kSvdfWeightsFeatureTensor;
-extern const int kSvdfWeightsTimeTensor;
-extern const int kSvdfBiasTensor;
-// This is a variable tensor, and will be modified by this op.
-extern const int kSvdfInputActivationStateTensor;
-
-// Output tensor.
-extern const int kSvdfOutputTensor;
-
-void EvalInt8SvdfReference(TfLiteContext* context, TfLiteNode* node,
-                           const TfLiteEvalTensor* input_tensor,
-                           const TfLiteEvalTensor* weights_feature_tensor,
-                           const TfLiteEvalTensor* weights_time_tensor,
-                           const TfLiteEvalTensor* bias_tensor,
-                           const TfLiteSVDFParams* params,
-                           TfLiteEvalTensor* activation_state_tensor,
-                           TfLiteEvalTensor* output_tensor,
-                           const OpDataSvdf& data);
-
-// TODO(#523): remove 16-bit code when no longer needed.
-void EvalInt16SvdfReference(TfLiteContext* context, TfLiteNode* node,
-                            const TfLiteEvalTensor* input_tensor,
-                            const TfLiteEvalTensor* weights_feature_tensor,
-                            const TfLiteEvalTensor* weights_time_tensor,
-                            const TfLiteEvalTensor* bias_tensor,
-                            const TfLiteSVDFParams* params,
-                            TfLiteEvalTensor* activation_state_tensor,
-                            TfLiteEvalTensor* output_tensor,
-                            const OpDataSvdf& data);
-
-void EvalFloatSvdfReference(
-    TfLiteContext* context, TfLiteNode* node, const TfLiteEvalTensor* input,
-    const TfLiteEvalTensor* weights_feature,
-    const TfLiteEvalTensor* weights_time, const TfLiteEvalTensor* bias,
-    const TfLiteSVDFParams* params, int scratch_tensor_index,
-    TfLiteEvalTensor* activation_state, TfLiteEvalTensor* output);
-
-TfLiteStatus PrepareSvdf(TfLiteContext* context, TfLiteNode* node);
-
-// This is the most generic TfLiteRegistration. The actual supported types may
-// still be target dependent. The only requirement is that every implementation
-// (reference or optimized) must define this function.
-TfLiteRegistration Register_SVDF();
-
-#if defined(HEXAGON) || defined(CMSIS_NN)
-TfLiteRegistration Register_SVDF_INT8();
-
-#else
-// Note that while this block gets used for both reference and optimized kernels
-// that do not have any specialized implementations, the only goal here is to
-// define fallback implementation that allow reference kernels to still be used
-// from applications that call a more specific kernel variant.
-
-inline TfLiteRegistration Register_SVDF_INT8() { return Register_SVDF(); }
-
-#endif
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_MICRO_KERNELS_SVDF_H_

code/components/tflite-lib/tensorflow/lite/micro/kernels/tanh.cc

@@ -1,203 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/integer_ops/tanh.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/reference/tanh.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/micro_utils.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace activations {
-namespace {
-constexpr int kInputTensor = 0;
-constexpr int kOutputTensor = 0;
-
-struct OpData {
-  int32_t input_zero_point;
-  int32_t input_range_radius;
-  int32_t input_multiplier;
-  int input_left_shift;
-};
-
-void* TanhInit(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(OpData));
-}
-
-TfLiteStatus CalculateArithmeticOpData(TfLiteContext* context, TfLiteNode* node,
-                                       OpData* data) {
-  MicroContext* micro_context = GetMicroContext(context);
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-
-  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
-
-  if (input->type == kTfLiteInt8) {
-    static constexpr int kInputIntegerBits = 4;
-    const double input_real_multiplier =
-        static_cast<double>(input->params.scale) *
-        static_cast<double>(1 << (31 - kInputIntegerBits));
-
-    const double q = std::frexp(input_real_multiplier, &data->input_left_shift);
-    data->input_multiplier = static_cast<int32_t>(TfLiteRound(q * (1ll << 31)));
-
-    data->input_range_radius =
-        CalculateInputRadius(kInputIntegerBits, data->input_left_shift, 31);
-  }
-
-  if (input->type == kTfLiteInt16) {
-    static constexpr int kInputIntegerBits = 3;
-    static constexpr int kOutputFractionalBits = 15;
-
-    // These operators are implemented in fixed-point arithmetic,
-    // which intrinsically wants symmetric ranges (zero_point==0)
-    // and power-of-two scales (power-of-two is abbreviated below as POT).
-    // While more general support would be possible by means of rescaling,
-    // that would add some overhead and some loss of accuracy and wouldn't
-    // be used at the moment as current quantized LSTM applications are
-    // happy with symmetric, power-of-two-scales quantization. So we just
-    // implement that narrow case only for now.
-
-    TF_LITE_ENSURE_EQ(context, input->params.zero_point, 0);
-    TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
-
-    int input_scale_log2_rounded;
-    bool param_scale_pot =
-        CheckedLog2(input->params.scale, &input_scale_log2_rounded);
-
-    data->input_left_shift =
-        (15 - kInputIntegerBits) + input_scale_log2_rounded;
-    param_scale_pot &=
-        (data->input_left_shift == 0 || data->input_left_shift == 1);
-
-    if (param_scale_pot) {
-      data->input_multiplier = 0;
-    } else {
-      // Calculate multiplier to change input scale to 1/(3*4096)
-      // as required by the table lookup.
-      // The number 3.0 in the multiplier comes from here,
-      // because the interval is [-10.7, 10.7] instead of [-8, 8].
-      // So, in this scaling +/-2^17 represents +/-10.7.
-
-      double multiplier =
-          static_cast<double>(input->params.scale) * 4096.0 * 3.0;
-      data->input_left_shift = 0;
-
-      while (multiplier <= 32767.0 / 2.0 && data->input_left_shift <= 30) {
-        data->input_left_shift++;
-        multiplier = multiplier * 2.0;
-      }
-
-      data->input_multiplier = static_cast<int32_t>(multiplier);
-    }
-
-    int output_scale_log2_rounded;
-    TF_LITE_ENSURE(
-        context, CheckedLog2(output->params.scale, &output_scale_log2_rounded));
-    TF_LITE_ENSURE_EQ(context, output_scale_log2_rounded,
-                      -kOutputFractionalBits);
-  }
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-TfLiteStatus TanhPrepare(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-
-  OpData* data = static_cast<OpData*>(node->user_data);
-
-  MicroContext* micro_context = GetMicroContext(context);
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  data->input_zero_point = input->params.zero_point;
-  TF_LITE_ENSURE_OK(context, CalculateArithmeticOpData(context, node, data));
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteStatus TanhEval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const OpData& data = *(static_cast<const OpData*>(node->user_data));
-
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      reference_ops::Tanh(tflite::micro::GetTensorShape(input),
-                          tflite::micro::GetTensorData<float>(input),
-                          tflite::micro::GetTensorShape(output),
-                          tflite::micro::GetTensorData<float>(output));
-      return kTfLiteOk;
-    } break;
-    case kTfLiteInt16: {
-      reference_integer_ops::Tanh(
-          data.input_multiplier, data.input_left_shift,
-          tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int16_t>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int16_t>(output));
-      return kTfLiteOk;
-    } break;
-    case kTfLiteInt8: {
-      reference_integer_ops::Tanh(
-          data.input_zero_point, data.input_range_radius, data.input_multiplier,
-          data.input_left_shift, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int8_t>(output));
-      return kTfLiteOk;
-    } break;
-    default:
-      MicroPrintf("Input %s, output %s not supported.",
-                  TfLiteTypeGetName(input->type),
-                  TfLiteTypeGetName(output->type), context);
-      return kTfLiteError;
-  }
-}
-
-}  // namespace activations
-
-TfLiteRegistration Register_TANH() {
-  return tflite::micro::RegisterOp(
-      activations::TanhInit, activations::TanhPrepare, activations::TanhEval);
-}
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/transpose.cc

@@ -1,121 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/kernels/internal/reference/transpose.h"
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/internal/types.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-constexpr int kInputTensor = 0;
-constexpr int kPermTensor = 1;
-constexpr int kOutputTensor = 0;
-
-struct TransposeContext {
-  TransposeContext(TfLiteContext* context, TfLiteNode* node) {
-    micro_context = GetMicroContext(context);
-    input = micro_context->AllocateTempInputTensor(node, kInputTensor);
-    perm = micro_context->AllocateTempInputTensor(node, kPermTensor);
-    output = micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  }
-  ~TransposeContext() {
-    micro_context->DeallocateTempTfLiteTensor(input);
-    micro_context->DeallocateTempTfLiteTensor(perm);
-    micro_context->DeallocateTempTfLiteTensor(output);
-  }
-  MicroContext* micro_context;
-  TfLiteTensor* input;
-  TfLiteTensor* perm;
-  TfLiteTensor* output;
-};
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-
-  TransposeContext op_context(context, node);
-
-  // Ensure validity of input tensor.
-  TF_LITE_ENSURE_MSG(context, NumDimensions(op_context.input) <= 5,
-                     "Transpose op only supports 1D-5D input arrays.");
-  TF_LITE_ENSURE_TYPES_EQ(context, op_context.input->type,
-                          op_context.output->type);
-
-  int dims = NumDimensions(op_context.input);
-  const int32_t* perm_data = GetTensorData<int32_t>(op_context.perm);
-
-  // Ensure validity of the permutations tensor as a 1D tensor.
-  TF_LITE_ENSURE_EQ(context, NumDimensions(op_context.perm), 1);
-  TF_LITE_ENSURE_EQ(context, op_context.perm->dims->data[0], dims);
-  for (int idx = 0; idx < dims; ++idx) {
-    TF_LITE_ENSURE_MSG(context, (perm_data[idx] >= 0 && perm_data[idx] < dims),
-                       "Transpose op permutations array is out of bounds.");
-  }
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* perm_tensor =
-      tflite::micro::GetEvalInput(context, node, kPermTensor);
-  const int32_t* perm_data = perm_tensor->data.i32;
-  const int size = perm_tensor->dims->data[0];
-  TransposeParams params;
-  params.perm_count = size;
-  for (int i = 0; i < size; ++i) {
-    params.perm[i] = perm_data[i];
-  }
-
-  // Transpose kernel only does rearranging values not numeric evaluations
-  // on each cell. It's safe to implement per size of scalar type and this
-  // trick keeps the total code size in a reasonable range.
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  switch (input->type) {
-    case kTfLiteFloat32:
-      reference_ops::Transpose(params, tflite::micro::GetTensorShape(input),
-                               tflite::micro::GetTensorData<float>(input),
-                               tflite::micro::GetTensorShape(output),
-                               tflite::micro::GetTensorData<float>(output));
-      break;
-    case kTfLiteInt8:
-      reference_ops::Transpose(params, tflite::micro::GetTensorShape(input),
-                               tflite::micro::GetTensorData<int8_t>(input),
-                               tflite::micro::GetTensorShape(output),
-                               tflite::micro::GetTensorData<int8_t>(output));
-      break;
-    default:
-      MicroPrintf(
-          "Type %s is currently not supported by Transpose. "
-          "Only float32 and int8 is supported",
-          TfLiteTypeGetName(input->type));
-      return kTfLiteError;
-  }
-
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_TRANSPOSE() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/transpose_conv.cc

@@ -1,343 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/kernels/internal/reference/transpose_conv.h"
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/common.h"
-#include "tensorflow/lite/kernels/internal/quantization_util.h"
-#include "tensorflow/lite/kernels/internal/reference/integer_ops/transpose_conv.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/kernels/padding.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-// For the TfLite transpose_conv implementation, input tensor 0 corresponds to
-// the OutputShapeTensor. However, since TFLM does not support dynamic tensors,
-// the TFLM implementation ignores input tensor 0 and the only inputs we care
-// about are kFilterTensor, kInputTensor and kBiasTensor.
-constexpr int kFilterTensor = 1;
-constexpr int kInputTensor = 2;
-constexpr int kBiasTensor = 3;
-constexpr int kOutputTensor = 0;
-
-// Conv is quantized along dimension 0:
-// https://www.tensorflow.org/lite/performance/quantization_spec
-constexpr int kConvQuantizedDimension = 0;
-
-struct OpData {
-  ConvParams params;
-
-  // A scratch buffer is required for quantized implementations.
-  int scratch_buffer_index;
-
-  // TODO(b/192090531): Remove this once all 8x16 transpose conv models use
-  // 64-bit biases.
-  int bias_converted_buffer_index;
-
-  // Multiplier and shift arrays are required for the int8 implementation.
-  int32_t* per_channel_output_multiplier;
-  int32_t* per_channel_output_shift;
-};
-
-inline PaddingType RuntimePaddingType(TfLitePadding padding) {
-  switch (padding) {
-    case TfLitePadding::kTfLitePaddingSame:
-      return PaddingType::kSame;
-    case TfLitePadding::kTfLitePaddingValid:
-      return PaddingType::kValid;
-    case TfLitePadding::kTfLitePaddingUnknown:
-    default:
-      return PaddingType::kNone;
-  }
-}
-
-TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node,
-                             const TfLiteTransposeConvParams* params, int width,
-                             int height, int filter_width, int filter_height,
-                             const TfLiteType data_type, OpData* data) {
-  bool has_bias = node->inputs->size == 4;
-  // Check number of inputs/outputs
-  TF_LITE_ENSURE(context, has_bias || node->inputs->size == 3);
-  TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);
-
-  // Matching GetWindowedOutputSize in TensorFlow.
-  auto padding = params->padding;
-  int unused_output_width;
-  int unused_output_height;
-  TfLitePaddingValues padding_values = ComputePaddingHeightWidth(
-      params->stride_height, params->stride_width, 1,
-      1,  // Dilation height and width are always 1 for transpose_conv.
-      height, width, filter_height, filter_width, padding,
-      &unused_output_height, &unused_output_width);
-
-  data->params.padding_type = RuntimePaddingType(padding);
-  data->params.padding_values.width = padding_values.width;
-  data->params.padding_values.height = padding_values.height;
-
-  // Note that quantized inference requires that all tensors have their
-  // parameters set. This is usually done during quantized training.
-  if (data_type != kTfLiteFloat32) {
-    MicroContext* micro_context = GetMicroContext(context);
-
-    TfLiteTensor* input =
-        micro_context->AllocateTempInputTensor(node, kInputTensor);
-    TF_LITE_ENSURE(context, input != nullptr);
-    TfLiteTensor* filter =
-        micro_context->AllocateTempInputTensor(node, kFilterTensor);
-    TF_LITE_ENSURE(context, filter != nullptr);
-    TfLiteTensor* bias =
-        micro_context->AllocateTempInputTensor(node, kBiasTensor);
-    TfLiteTensor* output =
-        micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-    TF_LITE_ENSURE(context, output != nullptr);
-    int output_channels = filter->dims->data[kConvQuantizedDimension];
-
-    TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams(
-        context, input, filter, bias, output, kTfLiteActNone,
-        &data->params.output_multiplier, &data->params.output_shift,
-        &data->params.quantized_activation_min,
-        &data->params.quantized_activation_max,
-        data->per_channel_output_multiplier, data->per_channel_output_shift,
-        output_channels));
-
-    // TODO(b/192090531): Remove this once all 8x16 transpose conv models use
-    // 64-bit biases.
-    if (input->type == kTfLiteInt16) {
-      TFLITE_DCHECK(filter->type == kTfLiteInt8);
-      TFLITE_DCHECK(output->type == kTfLiteInt16);
-      if (bias->type == kTfLiteInt16) {
-        TFLITE_DCHECK(
-            context->RequestScratchBufferInArena(
-                context, GetTensorShape(bias).FlatSize() * sizeof(std::int64_t),
-                &(data->bias_converted_buffer_index)) == kTfLiteOk);
-      }
-    }
-
-    micro_context->DeallocateTempTfLiteTensor(input);
-    micro_context->DeallocateTempTfLiteTensor(filter);
-    micro_context->DeallocateTempTfLiteTensor(output);
-    if (bias != nullptr) {
-      micro_context->DeallocateTempTfLiteTensor(bias);
-    }
-  }
-  return kTfLiteOk;
-}
-
-void* Init(TfLiteContext* context, const char* buffer, size_t length) {
-  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
-  return context->AllocatePersistentBuffer(context, sizeof(OpData));
-}
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  TFLITE_DCHECK(node->user_data != nullptr);
-  TFLITE_DCHECK(node->builtin_data != nullptr);
-
-  OpData* data = static_cast<OpData*>(node->user_data);
-  const auto params =
-      static_cast<const TfLiteTransposeConvParams*>(node->builtin_data);
-
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* filter =
-      micro_context->AllocateTempInputTensor(node, kFilterTensor);
-  TF_LITE_ENSURE(context, filter != nullptr);
-
-  // Get height and width of the output.
-  const int width = SizeOfDimension(output, 2);
-  const int height = SizeOfDimension(output, 1);
-  const int filter_width = SizeOfDimension(filter, 2);
-  const int filter_height = SizeOfDimension(filter, 1);
-
-  // Dynamically allocate per-channel quantization parameters.
-  const int num_channels = filter->dims->data[kConvQuantizedDimension];
-  data->per_channel_output_multiplier =
-      static_cast<int32_t*>(context->AllocatePersistentBuffer(
-          context, num_channels * sizeof(int32_t)));
-  data->per_channel_output_shift =
-      static_cast<int32_t*>(context->AllocatePersistentBuffer(
-          context, num_channels * sizeof(int32_t)));
-
-  // Quantized kernels use an int32 scratch buffer.
-  if (input->type == kTfLiteInt8) {
-    TFLITE_DCHECK(context->RequestScratchBufferInArena != nullptr);
-    TFLITE_DCHECK(context->RequestScratchBufferInArena(
-                      context,
-                      GetTensorShape(output).FlatSize() * sizeof(int32_t),
-                      &(data->scratch_buffer_index)) == kTfLiteOk);
-  }
-
-  // Quantized 16x8 kernels use an int64 scratch buffer.
-  if (input->type == kTfLiteInt16) {
-    TFLITE_DCHECK(context->RequestScratchBufferInArena != nullptr);
-    TFLITE_DCHECK(context->RequestScratchBufferInArena(
-                      context,
-                      GetTensorShape(output).FlatSize() * sizeof(std::int64_t),
-                      &(data->scratch_buffer_index)) == kTfLiteOk);
-  }
-
-  // All per-channel quantized tensors need valid zero point and scale arrays.
-  if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) {
-    TF_LITE_ENSURE_EQ(context, filter->quantization.type,
-                      kTfLiteAffineQuantization);
-
-    const auto* affine_quantization =
-        static_cast<TfLiteAffineQuantization*>(filter->quantization.params);
-    TF_LITE_ENSURE(context, affine_quantization);
-    TF_LITE_ENSURE(context, affine_quantization->scale);
-    TF_LITE_ENSURE(context, affine_quantization->zero_point);
-
-    TF_LITE_ENSURE(context,
-                   affine_quantization->scale->size == 1 ||
-                       affine_quantization->scale->size ==
-                           filter->dims->data[kConvQuantizedDimension]);
-    TF_LITE_ENSURE_EQ(context, affine_quantization->scale->size,
-                      affine_quantization->zero_point->size);
-  }
-
-  TF_LITE_ENSURE_STATUS(CalculateOpData(context, node, params, width, height,
-                                        filter_width, filter_height,
-                                        input->type, data));
-
-  // Offsets (zero points)
-  data->params.input_offset = -input->params.zero_point;
-  data->params.weights_offset = -filter->params.zero_point;
-  data->params.output_offset = output->params.zero_point;
-
-  // Stride
-  data->params.stride_width = params->stride_width;
-  data->params.stride_height = params->stride_height;
-
-  micro_context->DeallocateTempTfLiteTensor(output);
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(filter);
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  const TfLiteEvalTensor* filter =
-      tflite::micro::GetEvalInput(context, node, kFilterTensor);
-  const TfLiteEvalTensor* bias =
-      (NumInputs(node) == 4)
-          ? tflite::micro::GetEvalInput(context, node, kBiasTensor)
-          : nullptr;
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-
-  TFLITE_DCHECK(node->user_data != nullptr);
-  const OpData& data = *(static_cast<const OpData*>(node->user_data));
-
-  TF_LITE_ENSURE_EQ(context, input->type, output->type);
-  TF_LITE_ENSURE_MSG(
-      context,
-      input->type == filter->type ||
-          (input->type == kTfLiteInt16 && filter->type == kTfLiteInt8),
-      "Hybrid models are not supported on TFLite Micro.");
-
-  switch (input->type) {  // Already know in/out types are same.
-    case kTfLiteFloat32: {
-      reference_ops::TransposeConv(
-          data.params, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<float>(input),
-          tflite::micro::GetTensorShape(filter),
-          tflite::micro::GetTensorData<float>(filter),
-          tflite::micro::GetTensorShape(bias),
-          tflite::micro::GetOptionalTensorData<float>(bias),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<float>(output),
-          tflite::micro::GetTensorShape(nullptr), nullptr);
-      break;
-    }
-    case kTfLiteInt8: {
-      int32_t* scratch_buffer = static_cast<int32_t*>(
-          context->GetScratchBuffer(context, data.scratch_buffer_index));
-      reference_integer_ops::TransposeConv(
-          data.params, data.per_channel_output_multiplier,
-          data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
-          tflite::micro::GetTensorData<int8_t>(input),
-          tflite::micro::GetTensorShape(filter),
-          tflite::micro::GetTensorData<int8_t>(filter),
-          tflite::micro::GetTensorShape(bias),
-          tflite::micro::GetOptionalTensorData<int32_t>(bias),
-          tflite::micro::GetTensorShape(output),
-          tflite::micro::GetTensorData<int8_t>(output),
-          tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer);
-      break;
-    }
-    case kTfLiteInt16: {
-      std::int64_t* scratch_buffer = static_cast<int64_t*>(
-          context->GetScratchBuffer(context, data.scratch_buffer_index));
-      // TODO(b/192090531): Remove this once all 8x16 transpose conv models use
-      // 64-bit biases.
-      if (bias != nullptr && bias->type == kTfLiteInt16) {
-        std::int64_t* bias_converted_buffer =
-            static_cast<int64_t*>(context->GetScratchBuffer(
-                context, data.bias_converted_buffer_index));
-        for (int i = 0; i < tflite::micro::GetTensorShape(bias).FlatSize();
-             i++) {
-          bias_converted_buffer[i] = bias->data.i16[i];
-        }
-        reference_integer_ops::TransposeConv(
-            data.params, data.per_channel_output_multiplier,
-            data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
-            tflite::micro::GetTensorData<int16_t>(input),
-            tflite::micro::GetTensorShape(filter),
-            tflite::micro::GetTensorData<int8_t>(filter),
-            tflite::micro::GetTensorShape(bias), bias_converted_buffer,
-            tflite::micro::GetTensorShape(output),
-            tflite::micro::GetTensorData<int16_t>(output),
-            tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer);
-      } else {
-        reference_integer_ops::TransposeConv(
-            data.params, data.per_channel_output_multiplier,
-            data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
-            tflite::micro::GetTensorData<int16_t>(input),
-            tflite::micro::GetTensorShape(filter),
-            tflite::micro::GetTensorData<int8_t>(filter),
-            tflite::micro::GetTensorShape(bias),
-            tflite::micro::GetOptionalTensorData<std::int64_t>(bias),
-            tflite::micro::GetTensorShape(output),
-            tflite::micro::GetTensorData<int16_t>(output),
-            tflite::micro::GetTensorShape(nullptr), nullptr, scratch_buffer);
-      }
-      break;
-    }
-    default:
-      MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
-                  input->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace
-
-TfLiteRegistration Register_TRANSPOSE_CONV() {
-  return tflite::micro::RegisterOp(Init, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/unidirectional_sequence_lstm_test_config.h

@@ -1,244 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_LITE_MICRO_KERNELS_UNIDIRECTIONAL_SEQUENCE_LSTM_TEST_CONFIG_H_
-#define TENSORFLOW_LITE_MICRO_KERNELS_UNIDIRECTIONAL_SEQUENCE_LSTM_TEST_CONFIG_H_
-
-#include "tensorflow/lite/c/common.h"
-
-namespace tflite {
-namespace testing {
-
-// TODO(b/230666079) enable below tests for xtensa when the xtensa
-// kernel is reconciled with reference kernel
-#if !defined(XTENSA)
-
-struct LstmIntegerTestConfig {
-  const int n_batch;
-  const int n_input;
-  const int n_cell;
-  const int n_output;
-  const int sequence_length;
-  const bool time_major;
-  const bool use_cifg;
-  const bool use_peephole;
-  const bool use_projection_weights;
-  const bool use_projection_bias;
-  const bool use_layer_norm;
-  const bool use_8x8_8_implementation;
-  float intermediate_scale[5][2];
-  int intermediate_zp[5][2];
-  TfLiteAffineQuantization* intermediate_qparam;
-
-  const float* input;
-  int8_t* input_quant;
-
-  const float* input_to_input_weights;
-  int8_t* lstm_i2i_quant;
-  const float* input_to_forget_weights;
-  int8_t* lstm_i2f_quant;
-  const float* input_to_cell_weights;
-  int8_t* lstm_i2c_quant;
-  const float* input_to_output_weights;
-  int8_t* lstm_i2o_quant;
-
-  const float* recurrent_to_input_weights;
-  int8_t* lstm_r2i_quant;
-  const float* recurrent_to_forget_weights;
-  int8_t* lstm_r2f_quant;
-  const float* recurrent_to_cell_weights;
-  int8_t* lstm_r2c_quant;
-  const float* recurrent_to_output_weights;
-  int8_t* lstm_r2o_quant;
-
-  const float* cell_to_input_weights;
-  int16_t* lstm_c2i_quant;
-  const float* cell_to_forget_weights;
-  int16_t* lstm_c2f_quant;
-  const float* cell_to_output_weights;
-  int16_t* lstm_c2o_quant;
-
-  const float* input_gate_bias;
-  int32_t* lstm_igate_bias_quant;
-  const float* forget_gate_bias;
-  int32_t* lstm_fgate_bias_quant;
-  const float* cell_gate_bias;
-  int32_t* lstm_cgate_bias_quant;
-  const float* output_gate_bias;
-  int32_t* lstm_ogate_bias_quant;
-
-  const float* projection_weights;
-  int8_t* lstm_proj_w_quant;
-  const float* projection_bias;
-  int32_t* projection_bias_quant;
-
-  int16_t* output_state;
-  int16_t* cell_state;
-
-  const float* input_layer_norm_coefficients;
-  int16_t* lstm_input_layer_norm_coeff_quant;
-  const float* forget_layer_norm_coefficients;
-  int16_t* lstm_forget_layer_norm_coeff_quant;
-  const float* cell_layer_norm_coefficients;
-  int16_t* lstm_cell_layer_norm_coeff_quant;
-  const float* output_layer_norm_coefficients;
-  int16_t* lstm_output_layer_norm_coeff_quant;
-
-  int8_t* output;
-  const int8_t* expected_output;
-
-  bool asymmetric_quantize_inputs;
-  const float ranges[25][2];
-};
-
-struct LstmFloatTestConfig {
-  const int n_batch;
-  const int n_input;
-  const int n_cell;
-  const int n_output;
-  const int sequence_length;
-  const bool time_major;
-  const bool use_cifg;
-  const bool use_peephole;
-  const bool use_projection_weights;
-  const bool use_projection_bias;
-  const bool use_layer_norm;
-  const float cell_clip;
-  const float proj_clip;
-
-  const float* input_original;
-  float* input;
-
-  const float* input_to_input_weights;
-  const float* input_to_forget_weights;
-  const float* input_to_cell_weights;
-  const float* input_to_output_weights;
-
-  const float* recurrent_to_input_weights;
-  const float* recurrent_to_forget_weights;
-  const float* recurrent_to_cell_weights;
-  const float* recurrent_to_output_weights;
-
-  const float* cell_to_input_weights;
-  const float* cell_to_forget_weights;
-  const float* cell_to_output_weights;
-
-  const float* input_gate_bias;
-  const float* forget_gate_bias;
-  const float* cell_gate_bias;
-  const float* output_gate_bias;
-
-  const float* projection_weights;
-  const float* projection_bias;
-
-  float* output_state;
-  float* cell_state;
-
-  const float* input_layer_norm_coefficients;
-  const float* forget_layer_norm_coefficients;
-  const float* cell_layer_norm_coefficients;
-  const float* output_layer_norm_coefficients;
-
-  float* output;
-  const float* expected_output_original;
-  float* expected_output;
-};
-
-struct LstmWeightQuantizationBuffers {
-  int8_t* lstm_i2i_quant;
-  float* lstm_i2i_scale;
-  int* lstm_i2i_zp;
-  TfLiteAffineQuantization* lstm_i2i_qparam;
-
-  int8_t* lstm_i2f_quant;
-  float* lstm_i2f_scale;
-  int* lstm_i2f_zp;
-  TfLiteAffineQuantization* lstm_i2f_qparam;
-
-  int8_t* lstm_i2c_quant;
-  float* lstm_i2c_scale;
-  int* lstm_i2c_zp;
-  TfLiteAffineQuantization* lstm_i2c_qparam;
-
-  int8_t* lstm_i2o_quant;
-  float* lstm_i2o_scale;
-  int* lstm_i2o_zp;
-  TfLiteAffineQuantization* lstm_i2o_qparam;
-
-  int8_t* lstm_r2i_quant;
-  float* lstm_r2i_scale;
-  int* lstm_r2i_zp;
-  TfLiteAffineQuantization* lstm_r2i_qparam;
-
-  int8_t* lstm_r2f_quant;
-  float* lstm_r2f_scale;
-  int* lstm_r2f_zp;
-  TfLiteAffineQuantization* lstm_r2f_qparam;
-
-  int8_t* lstm_r2c_quant;
-  float* lstm_r2c_scale;
-  int* lstm_r2c_zp;
-  TfLiteAffineQuantization* lstm_r2c_qparam;
-
-  int8_t* lstm_r2o_quant;
-  float* lstm_r2o_scale;
-  int* lstm_r2o_zp;
-  TfLiteAffineQuantization* lstm_r2o_qparam;
-
-  int8_t* lstm_c2i_quant;
-  float* lstm_c2i_scale;
-  int* lstm_c2i_zp;
-  TfLiteAffineQuantization* lstm_c2i_qparam;
-
-  int8_t* lstm_c2f_quant;
-  float* lstm_c2f_scale;
-  int* lstm_c2f_zp;
-  TfLiteAffineQuantization* lstm_c2f_qparam;
-
-  int8_t* lstm_c2o_quant;
-  float* lstm_c2o_scale;
-  int* lstm_c2o_zp;
-  TfLiteAffineQuantization* lstm_c2o_qparam;
-
-  int8_t* lstm_proj_w_quant;
-  float* lstm_proj_w_scale;
-  int* lstm_proj_w_zp;
-  TfLiteAffineQuantization* lstm_proj_w_qparam;
-};
-
-extern LstmIntegerTestConfig lstm_integer_no_peephole_config;
-
-extern LstmIntegerTestConfig lstm_integer_peephole_config;
-
-extern LstmFloatTestConfig lstm_no_cifg_no_peephole_no_proj_config;
-
-extern LstmFloatTestConfig lstm_cifg_peephole_no_proj_config;
-
-extern LstmFloatTestConfig lstm_no_cifg_peephole_proj_config;
-
-extern LstmFloatTestConfig lstm_no_cifg_peephole_proj_bias_config;
-
-extern LstmWeightQuantizationBuffers lstm_no_cifg_no_peephole_no_proj_buffers;
-
-extern LstmWeightQuantizationBuffers lstm_cifg_peephole_no_proj_buffers;
-
-extern LstmWeightQuantizationBuffers lstm_no_cifg_peephole_proj_buffers;
-
-extern LstmFloatTestConfig cifg_peephole_no_proj_config_layer_norm;
-
-#endif  // !defined(XTENSA)
-}  // namespace testing
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_MICRO_KERNELS_UNIDIRECTIONAL_SEQUENCE_LSTM_TEST_CONFIG_H_

code/components/tflite-lib/tensorflow/lite/micro/kernels/unpack.cc

@@ -1,111 +0,0 @@
-/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/builtin_op_data.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace ops {
-namespace micro {
-namespace unpack {
-namespace {
-
-constexpr int kInputTensor = 0;
-
-template <typename T>
-TfLiteStatus UnpackImpl(TfLiteContext* context, TfLiteNode* node,
-                        const TfLiteEvalTensor* input, int output_count,
-                        int axis) {
-  const TfLiteEvalTensor* output0 =
-      tflite::micro::GetEvalOutput(context, node, 0);
-  const TfLiteIntArray* input_dims = input->dims;
-  const TfLiteIntArray* output_dims = output0->dims;
-  const int dimensions = input_dims->size;
-
-  if (axis < 0) {
-    axis += input->dims->size;
-  }
-
-  TFLITE_DCHECK_LT(axis, dimensions);
-
-  int outer_size = 1;
-  for (int i = 0; i < axis; ++i) {
-    outer_size *= input_dims->data[i];
-  }
-  int copy_size = 1;
-  for (int i = axis + 1; i < dimensions; ++i) {
-    copy_size *= input_dims->data[i];
-  }
-  int output_size = 1;
-  for (int i = 0; i < output_dims->size; ++i) {
-    output_size *= output_dims->data[i];
-  }
-  TFLITE_DCHECK_EQ(output_size, copy_size * outer_size);
-
-  const T* input_data = tflite::micro::GetTensorData<T>(input);
-
-  for (int i = 0; i < output_count; ++i) {
-    TfLiteEvalTensor* t = tflite::micro::GetEvalOutput(context, node, i);
-    T* output_data = tflite::micro::GetTensorData<T>(t);
-    for (int k = 0; k < outer_size; ++k) {
-      T* output_ptr = output_data + copy_size * k;
-      int loc = k * output_count * copy_size + i * copy_size;
-      const T* input_ptr = input_data + loc;
-      for (int j = 0; j < copy_size; ++j) output_ptr[j] = input_ptr[j];
-    }
-  }
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  TfLiteUnpackParams* data =
-      reinterpret_cast<TfLiteUnpackParams*>(node->builtin_data);
-
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-
-  switch (input->type) {
-    case kTfLiteFloat32: {
-      return UnpackImpl<float>(context, node, input, data->num, data->axis);
-    }
-    case kTfLiteInt32: {
-      return UnpackImpl<int32_t>(context, node, input, data->num, data->axis);
-    }
-    case kTfLiteInt8: {
-      return UnpackImpl<int8_t>(context, node, input, data->num, data->axis);
-    }
-    default: {
-      MicroPrintf("Type '%s' is not supported by unpack.",
-                  TfLiteTypeGetName(input->type));
-      return kTfLiteError;
-    }
-  }
-
-  return kTfLiteOk;
-}
-}  // namespace
-}  // namespace unpack
-
-TfLiteRegistration Register_UNPACK() {
-  return tflite::micro::RegisterOp(nullptr, nullptr, unpack::Eval);
-}
-
-}  // namespace micro
-}  // namespace ops
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/kernels/zeros_like.cc

@@ -1,87 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/kernels/kernel_util.h"
-
-namespace tflite {
-namespace {
-
-constexpr int kInputTensor = 0;
-constexpr int kOutputTensor = 0;
-
-TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
-  MicroContext* micro_context = GetMicroContext(context);
-
-  TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
-  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
-  TfLiteTensor* input =
-      micro_context->AllocateTempInputTensor(node, kInputTensor);
-  TF_LITE_ENSURE(context, input != nullptr);
-  TfLiteTensor* output =
-      micro_context->AllocateTempOutputTensor(node, kOutputTensor);
-  TF_LITE_ENSURE(context, output != nullptr);
-  output->type = input->type;
-
-  micro_context->DeallocateTempTfLiteTensor(input);
-  micro_context->DeallocateTempTfLiteTensor(output);
-  return kTfLiteOk;
-}
-
-template <typename T>
-void resetZeros(T* out, const int num_elements) {
-  for (int i = 0; i < num_elements; ++i) {
-    out[i] = static_cast<T>(0);
-  }
-}
-
-TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
-  const TfLiteEvalTensor* input =
-      tflite::micro::GetEvalInput(context, node, kInputTensor);
-  TfLiteEvalTensor* output =
-      tflite::micro::GetEvalOutput(context, node, kOutputTensor);
-  int flat_size = MatchingFlatSize(tflite::micro::GetTensorShape(input),
-                                   tflite::micro::GetTensorShape(output));
-  switch (input->type) {
-    case kTfLiteInt64:
-      resetZeros(tflite::micro::GetTensorData<int64_t>(output), flat_size);
-      break;
-    case kTfLiteInt32:
-      resetZeros(tflite::micro::GetTensorData<int32_t>(output), flat_size);
-      break;
-    case kTfLiteInt8:
-      resetZeros(tflite::micro::GetTensorData<int8_t>(output), flat_size);
-      break;
-    case kTfLiteFloat32:
-      resetZeros(tflite::micro::GetTensorData<float>(output), flat_size);
-      break;
-    default:
-      MicroPrintf(
-          "ZerosLike only currently supports int64, int32, "
-          "and float32, got %d.",
-          input->type);
-      return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-}  // namespace
-
-TfLiteRegistration Register_ZEROS_LIKE() {
-  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/micro_allocation_info.cc

@@ -1,355 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/micro/micro_allocation_info.h"
-
-#include "tensorflow/lite/c/c_api_types.h"
-#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/kernels/kernel_util.h"
-#include "tensorflow/lite/micro/memory_helpers.h"
-#include "tensorflow/lite/micro/memory_planner/greedy_memory_planner.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-
-namespace tflite {
-
-namespace {
-constexpr char kOfflineMemAllocMetadata[] = "OfflineMemoryAllocation";
-constexpr int kUninitializedLifetime = -1;
-}  // namespace
-
-// Mark the given Allocation info as first created at the specified allocation
-// scope count. Only the first creation must be recorded since the allocation
-// scope count monotonically increases throughout the lifetime marking process.
-void AllocationInfoBuilder::UpdateFirstCreated(AllocationInfo* current,
-                                               int allocation_scope_count) {
-  TFLITE_DCHECK(current->first_created <= allocation_scope_count);
-  if (current->first_created == kUninitializedLifetime) {
-    current->first_created = allocation_scope_count;
-  }
-}
-
-// Mark the given AllocationInfo as last used at the specified allocation scope
-// count. Update the last used marker every time, since the allocation scope
-// count monotonically increases through the lifetime marking process.
-void AllocationInfoBuilder::UpdateLastUsed(AllocationInfo* current,
-                                           int allocation_scope_count) {
-  TFLITE_DCHECK(current->last_used <= allocation_scope_count);
-  current->last_used = allocation_scope_count;
-}
-
-TfLiteStatus AllocationInfoBuilder::MarkSubgraphLifetimesIfNecessary(
-    const Operator* op, internal::ScratchBufferRequest* scratch_buffer_requests,
-    ScratchBufferHandle* scratch_buffer_handles,
-    SubgraphAllocations* allocations) {
-  int first_subgraph_index = -1;
-  int second_subgraph_index = -1;
-  const OperatorCode* opcode =
-      model_->operator_codes()->Get(op->opcode_index());
-  switch (opcode->builtin_code()) {
-    case BuiltinOperator_IF: {
-      first_subgraph_index =
-          op->builtin_options_as_IfOptions()->then_subgraph_index();
-      second_subgraph_index =
-          op->builtin_options_as_IfOptions()->else_subgraph_index();
-      break;
-    }
-    case BuiltinOperator_CALL_ONCE: {
-      first_subgraph_index =
-          op->builtin_options_as_CallOnceOptions()->init_subgraph_index();
-      break;
-    }
-    case BuiltinOperator_WHILE: {
-      first_subgraph_index =
-          op->builtin_options_as_WhileOptions()->cond_subgraph_index();
-      second_subgraph_index =
-          op->builtin_options_as_WhileOptions()->body_subgraph_index();
-      break;
-    }
-    default: {
-      break;
-    }
-  }
-  if (first_subgraph_index != -1) {
-    // Enter a new allocation scope for each subgraph.
-    allocation_scope_count_++;
-    TF_LITE_ENSURE_STATUS(
-        MarkAllocationLifetimes(first_subgraph_index, scratch_buffer_requests,
-                                scratch_buffer_handles, allocations));
-  }
-  if (second_subgraph_index != -1) {
-    // Enter a new allocation scope for each subgraph.
-    allocation_scope_count_++;
-    TF_LITE_ENSURE_STATUS(
-        MarkAllocationLifetimes(second_subgraph_index, scratch_buffer_requests,
-                                scratch_buffer_handles, allocations));
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus AllocationInfoBuilder::CreateAllocationInfo(
-    int scratch_buffer_request_count) {
-  size_t subgraph_offsets_length = model_->subgraphs()->size() * sizeof(size_t);
-  info_.subgraph_offsets =
-      reinterpret_cast<size_t*>(non_persistent_allocator_->AllocateTemp(
-          subgraph_offsets_length, alignof(size_t)));
-  if (info_.subgraph_offsets == nullptr) {
-    TF_LITE_REPORT_ERROR(
-        reporter_,
-        "Failed to allocate memory for memory planning, %d bytes required",
-        subgraph_offsets_length);
-    return kTfLiteError;
-  }
-  size_t tensor_count = 0;
-  for (size_t subgraph_idx = 0; subgraph_idx < model_->subgraphs()->size();
-       subgraph_idx++) {
-    // Add all tensors in each subgraph to the AllocationInfo array. Even weight
-    // tensors are added but marked with needs_allocating = false. Including all
-    // tensors in the graph here simplifies logic.
-    info_.subgraph_offsets[subgraph_idx] = tensor_count;
-    tensor_count += model_->subgraphs()->Get(subgraph_idx)->tensors()->size();
-  }
-  info_.tensor_count = tensor_count;
-
-  // Scratch buffer allocations follow tensor allocations, so the scratch offset
-  // is equal to the number of tensor allocations.
-  info_.scratch_offset = tensor_count;
-  info_.allocation_info_count = tensor_count + scratch_buffer_request_count;
-  info_.scratch_buffer_count = scratch_buffer_request_count;
-  size_t bytes = sizeof(AllocationInfo) * info_.allocation_info_count;
-
-  // Allocate an array of AllocationInfo structs from the temp section. This
-  // struct will be used by AllocationInfoBuilder to find buffer usage.
-  info_.allocation_info = reinterpret_cast<AllocationInfo*>(
-      non_persistent_allocator_->AllocateTemp(bytes, alignof(AllocationInfo)));
-  if (info_.allocation_info == nullptr) {
-    TF_LITE_REPORT_ERROR(
-        reporter_,
-        "Failed to allocate memory for memory planning, %d bytes required",
-        bytes);
-    return kTfLiteError;
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus AllocationInfoBuilder::FreeAllocationInfo() {
-  non_persistent_allocator_->DeallocateTemp(
-      reinterpret_cast<uint8_t*>(info_.allocation_info));
-  non_persistent_allocator_->DeallocateTemp(
-      reinterpret_cast<uint8_t*>(info_.subgraph_offsets));
-  return kTfLiteOk;
-}
-
-TfLiteStatus AllocationInfoBuilder::ValidateSubgraph(
-    const SubGraph* subgraph, TfLiteEvalTensor* eval_tensors) {
-  uint32_t operators_size = NumSubgraphOperators(subgraph);
-
-  for (uint32_t i = 0; i < operators_size; i++) {
-    const auto op = subgraph->operators()->Get(i);
-    for (size_t n = 0;
-         op->intermediates() != nullptr && n < op->intermediates()->size();
-         n++) {
-      const int tensor_index = op->intermediates()->Get(n);
-      size_t tensor_size = -1;
-      TF_LITE_ENSURE_STATUS(TfLiteEvalTensorByteLength(
-          &eval_tensors[tensor_index], &tensor_size));
-      if (tensor_size != 0) {
-        MicroPrintf(
-            "Does not support intermediate tensor with non-zero size: %d",
-            tensor_size);
-        return kTfLiteError;
-      }
-    }
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus AllocationInfoBuilder::InitializeAllocationInfo(
-    const int32_t* offline_offsets, SubgraphAllocations* allocations) {
-  AllocationInfo* allocation_info = info_.allocation_info;
-  // Initialize allocation info for every tensor in every subgraph.
-  for (size_t subgraph_idx = 0; subgraph_idx < model_->subgraphs()->size();
-       subgraph_idx++) {
-    const SubGraph* subgraph = model_->subgraphs()->Get(subgraph_idx);
-    TfLiteEvalTensor* eval_tensors = allocations[subgraph_idx].tensors;
-    AllocationInfo* subgraph_allocation_info =
-        &allocation_info[info_.subgraph_offsets[subgraph_idx]];
-
-    // Ensure constraints are met.
-    TF_LITE_ENSURE_STATUS(ValidateSubgraph(subgraph, eval_tensors));
-
-    for (size_t i = 0; i < subgraph->tensors()->size(); ++i) {
-      AllocationInfo* current = &subgraph_allocation_info[i];
-      current->output_ptr = &(eval_tensors[i].data.data);
-
-      TF_LITE_ENSURE_STATUS(
-          TfLiteEvalTensorByteLength(&eval_tensors[i], &current->bytes));
-
-      current->first_created = kUninitializedLifetime;
-      current->last_used = kUninitializedLifetime;
-      current->needs_allocating =
-          (eval_tensors[i].data.data == nullptr) &&
-          (!subgraph->tensors()->Get(i)->is_variable()) &&
-          (current->bytes != 0);
-      if (offline_offsets) {
-        current->offline_offset = offline_offsets[i];
-      } else {
-        current->offline_offset = kOnlinePlannedBuffer;
-      }
-    }
-  }
-  // Initialize allocation info for every scratch buffer.
-  AllocationInfo* scratch_allocation_info =
-      &allocation_info[info_.scratch_offset];
-  for (size_t i = 0; i < info_.scratch_buffer_count; i++) {
-    AllocationInfo* current = &scratch_allocation_info[i];
-    current->first_created = kUninitializedLifetime;
-    current->last_used = kUninitializedLifetime;
-    current->needs_allocating = true;
-    current->offline_offset = kOnlinePlannedBuffer;
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus AllocationInfoBuilder::MarkAllocationLifetimes(
-    int subgraph_idx, internal::ScratchBufferRequest* scratch_buffer_requests,
-    ScratchBufferHandle* scratch_buffer_handles,
-    SubgraphAllocations* allocations) {
-  const SubGraph* subgraph = model_->subgraphs()->Get(subgraph_idx);
-
-  AllocationInfo* allocation_info = info_.allocation_info;
-  // Each subgraph's tensor allocations are in a contiguous block starting at
-  // subgraph_offsets_[subgraph index] with one entry per tensor.
-  AllocationInfo* subgraph_allocation_info =
-      &allocation_info[info_.subgraph_offsets[subgraph_idx]];
-
-  uint32_t operators_size = NumSubgraphOperators(subgraph);
-  // Mark all inputs as created at the start of the subgraph invocation.
-  for (size_t i = 0;
-       subgraph->inputs() != nullptr && i < subgraph->inputs()->size(); ++i) {
-    const int tensor_index = subgraph->inputs()->Get(i);
-    AllocationInfo* current = &subgraph_allocation_info[tensor_index];
-    UpdateFirstCreated(current, allocation_scope_count_);
-  }
-
-  for (uint32_t i = 0; i < operators_size; i++) {
-    // Each operator has a new allocation scope.
-    allocation_scope_count_++;
-    const auto* op = subgraph->operators()->Get(i);
-    // Figure out when the first creation and use of each tensor is.
-    for (size_t n = 0; op->outputs() != nullptr && n < op->outputs()->size();
-         ++n) {
-      const int tensor_index = op->outputs()->Get(n);
-      AllocationInfo* current = &subgraph_allocation_info[tensor_index];
-      UpdateFirstCreated(current, allocation_scope_count_);
-    }
-
-    // Keep track of scope count before any subgraphs, so that scratch buffers'
-    // lifetime within a control flow op properly overlaps with all subgraphs.
-    int start_allocation_scope_count = allocation_scope_count_;
-
-    // Control flow operators can invoke subgraphs. Plan these subgraphs
-    // before continuing on to the rest of the graph.
-    MarkSubgraphLifetimesIfNecessary(op, scratch_buffer_requests,
-                                     scratch_buffer_handles, allocations);
-
-    // Figure out when the last use of each tensor is.
-    for (size_t n = 0; op->inputs() != nullptr && n < op->inputs()->size();
-         ++n) {
-      const int tensor_index = op->inputs()->Get(n);
-      // Optional bias tensors can have an index of -1 when they are omitted.
-      if (tensor_index >= 0) {
-        AllocationInfo* current = &subgraph_allocation_info[tensor_index];
-        // No need to update creation since it is either marked by the subgraph
-        // or producer op, or it is not part of the memory plan (weight, bias
-        // tensor).
-        UpdateLastUsed(current, allocation_scope_count_);
-      }
-    }
-    for (size_t n = 0; op->outputs() != nullptr && n < op->outputs()->size();
-         ++n) {
-      const int tensor_index = op->outputs()->Get(n);
-      AllocationInfo* current = &subgraph_allocation_info[tensor_index];
-      UpdateLastUsed(current, allocation_scope_count_);
-    }
-
-    // Mark thse lifetime of scratch buffers belonging to the current node. This
-    // operation is O(N * M) where N is the total number of visited nodes and M
-    // is the total number of scratch buffers.
-    // TODO(b/217794030): Optimize this memory planning code.
-    AllocationInfo* scratch_allocation_info =
-        &allocation_info[info_.scratch_offset];
-    for (size_t scratch_idx = 0; scratch_idx < info_.scratch_buffer_count;
-         scratch_idx++) {
-      internal::ScratchBufferRequest request =
-          scratch_buffer_requests[scratch_idx];
-      AllocationInfo* current = &scratch_allocation_info[scratch_idx];
-      if (request.node_idx == static_cast<int>(i) &&
-          request.subgraph_idx == static_cast<int>(subgraph_idx)) {
-        ScratchBufferHandle* current_handle =
-            &(scratch_buffer_handles[scratch_idx]);
-        current->output_ptr = reinterpret_cast<void**>(&current_handle->data);
-        current->bytes = request.bytes;
-        UpdateFirstCreated(current, start_allocation_scope_count);
-        UpdateLastUsed(current, allocation_scope_count_);
-      }
-    }
-  }
-
-  // Mark all outputs as persistent to the end of the subgraph invocation.
-  for (size_t i = 0;
-       subgraph->outputs() != nullptr && i < subgraph->outputs()->size(); ++i) {
-    const int tensor_index = subgraph->outputs()->Get(i);
-    AllocationInfo* current = &subgraph_allocation_info[tensor_index];
-    UpdateLastUsed(current, allocation_scope_count_);
-  }
-  return kTfLiteOk;
-}
-
-// Get offline tensors allocation plan. See
-// micro/docs/memory_management.md for more info.
-TfLiteStatus AllocationInfoBuilder::GetOfflinePlannedOffsets(
-    const int32_t** offline_planner_offsets) {
-  if (model_->metadata()) {
-    for (size_t i = 0; i < model_->metadata()->size(); ++i) {
-      auto metadata = model_->metadata()->Get(i);
-      const size_t metadata_name_size = (size_t)metadata->name()->size();
-
-      if ((strncmp(metadata->name()->c_str(), kOfflineMemAllocMetadata,
-                   std::min(metadata_name_size,
-                            strlen(kOfflineMemAllocMetadata))) == 0) &&
-          metadata_name_size == strlen(kOfflineMemAllocMetadata)) {
-        const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers =
-            model_->buffers();
-        auto* buffer = (*buffers)[metadata->buffer()];
-        auto* array = buffer->data();
-        const uint32_t* metadata_buffer =
-            reinterpret_cast<const uint32_t*>(array->data());
-        const size_t nbr_tensors = static_cast<size_t>(metadata_buffer[2]);
-        *offline_planner_offsets =
-            reinterpret_cast<const int32_t*>(&metadata_buffer[3]);
-
-        if (info_.tensor_count != nbr_tensors) {
-          TF_LITE_REPORT_ERROR(reporter_,
-                               "Nbr of offline buffer offsets (%d) in metadata "
-                               "not equal nbr tensors (%d)\n",
-                               nbr_tensors, info_.tensor_count);
-          return kTfLiteError;
-        }
-      }
-    }
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/micro_allocator.cc

@@ -1,965 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/micro_allocator.h"
-
-#include <cstddef>
-#include <cstdint>
-
-#include "flatbuffers/flatbuffers.h"  // from @flatbuffers
-#include "tensorflow/lite/c/c_api_types.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/core/api/error_reporter.h"
-#include "tensorflow/lite/core/api/flatbuffer_conversions.h"
-#include "tensorflow/lite/core/api/op_resolver.h"
-#include "tensorflow/lite/core/api/tensor_utils.h"
-#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/micro/arena_allocator/non_persistent_arena_buffer_allocator.h"
-#include "tensorflow/lite/micro/arena_allocator/persistent_arena_buffer_allocator.h"
-#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h"
-#include "tensorflow/lite/micro/compatibility.h"
-#include "tensorflow/lite/micro/flatbuffer_utils.h"
-#include "tensorflow/lite/micro/memory_helpers.h"
-#include "tensorflow/lite/micro/memory_planner/greedy_memory_planner.h"
-#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h"
-#include "tensorflow/lite/micro/micro_allocation_info.h"
-#include "tensorflow/lite/micro/micro_arena_constants.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-#include "tensorflow/lite/schema/schema_utils.h"
-
-namespace tflite {
-
-namespace {
-
-// Maximum number of scratch buffer requests per operator. Operator kernels that
-// request more than this value will receive an exception.
-constexpr size_t kMaxScratchBuffersPerOp = 12;
-
-// Sentinel value used as a placeholder to mark a ScratchBufferRequest request
-// needs a node id assignment.
-constexpr int kUnassignedScratchBufferRequestIndex = -1;
-
-const TfLiteIntArray kZeroLengthIntArray = {};
-
-class MicroBuiltinDataAllocator : public BuiltinDataAllocator {
- public:
-  explicit MicroBuiltinDataAllocator(
-      IPersistentBufferAllocator* persistent_allocator)
-      : persistent_allocator_(persistent_allocator) {}
-
-  void* Allocate(size_t size, size_t alignment_hint) override {
-    return persistent_allocator_->AllocatePersistentBuffer(size,
-                                                           alignment_hint);
-  }
-  void Deallocate(void* data) override {
-    // Do not deallocate, builtin data needs to be available for the life time
-    // of the model.
-  }
-
-  TF_LITE_REMOVE_VIRTUAL_DELETE
-
- private:
-  IPersistentBufferAllocator* persistent_allocator_;
-};
-
-TfLiteStatus CreatePlan(ErrorReporter* error_reporter,
-                        MicroMemoryPlanner* planner,
-                        const AllocationInfo* allocation_info,
-                        size_t allocation_info_size) {
-  // Add the tensors to our allocation plan.
-  for (size_t i = 0; i < allocation_info_size; ++i) {
-    const AllocationInfo* current = &allocation_info[i];
-    if (current->needs_allocating) {
-      size_t aligned_bytes_required =
-          AlignSizeUp(current->bytes, MicroArenaBufferAlignment());
-      if (current->offline_offset == kOnlinePlannedBuffer) {
-        TF_LITE_ENSURE_STATUS(
-            planner->AddBuffer(error_reporter, aligned_bytes_required,
-                               current->first_created, current->last_used));
-      } else {
-        TF_LITE_ENSURE_STATUS(planner->AddBuffer(
-            error_reporter, aligned_bytes_required, current->first_created,
-            current->last_used, current->offline_offset));
-      }
-    }
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus CommitPlan(ErrorReporter* error_reporter,
-                        MicroMemoryPlanner* planner, uint8_t* starting_point,
-                        const AllocationInfo* allocation_info,
-                        size_t allocation_info_size) {
-  // Figure out the actual memory addresses for each buffer, based on the plan.
-  int planner_index = 0;
-  for (size_t i = 0; i < allocation_info_size; ++i) {
-    const AllocationInfo* current = &allocation_info[i];
-    if (current->needs_allocating) {
-      int offset = -1;
-      TF_LITE_ENSURE_STATUS(
-          planner->GetOffsetForBuffer(error_reporter, planner_index, &offset));
-      *current->output_ptr = reinterpret_cast<void*>(starting_point + offset);
-      ++planner_index;
-    }
-  }
-  return kTfLiteOk;
-}
-
-IPersistentBufferAllocator* CreatePersistentArenaAllocator(uint8_t* buffer_head,
-                                                           size_t buffer_size) {
-  // Align the actually used area by the tail because persistent buffer grows
-  // from the bottom to top.
-  uint8_t* aligned_buffer_tail =
-      AlignPointerDown(buffer_head + buffer_size, MicroArenaBufferAlignment());
-  size_t aligned_buffer_size = aligned_buffer_tail - buffer_head;
-  PersistentArenaBufferAllocator tmp =
-      PersistentArenaBufferAllocator(buffer_head, aligned_buffer_size);
-
-  // Allocate enough bytes from the buffer to create a
-  // SingleArenaBufferAllocator. The new instance will use the current adjusted
-  // tail buffer from the tmp allocator instance.
-  uint8_t* allocator_buffer =
-      tmp.AllocatePersistentBuffer(sizeof(PersistentArenaBufferAllocator),
-                                   alignof(PersistentArenaBufferAllocator));
-  // Use the default copy constructor to populate internal states.
-  return new (allocator_buffer) PersistentArenaBufferAllocator(tmp);
-}
-
-// NonPersistentBufferAllocator instance is created in the persistent buffer
-// because it has to be persistent to keep track of the non-persistent buffer
-// information.
-INonPersistentBufferAllocator* CreateNonPersistentArenaAllocator(
-    uint8_t* buffer_head, size_t buffer_size,
-    IPersistentBufferAllocator* persistent_buffer_allocator) {
-  uint8_t* allocator_buffer =
-      persistent_buffer_allocator->AllocatePersistentBuffer(
-          sizeof(NonPersistentArenaBufferAllocator),
-          alignof(NonPersistentArenaBufferAllocator));
-  // Align the actually used area by the head because persistent buffer grows
-  // from the head to bottom.
-  uint8_t* aligned_buffer_head =
-      AlignPointerUp(buffer_head, MicroArenaBufferAlignment());
-  size_t aligned_buffer_size = buffer_head + buffer_size - aligned_buffer_head;
-
-  INonPersistentBufferAllocator* non_persistent_buffer_allocator =
-      new (allocator_buffer) NonPersistentArenaBufferAllocator(
-          aligned_buffer_head, aligned_buffer_size);
-  return non_persistent_buffer_allocator;
-}
-
-}  // namespace
-
-namespace internal {
-
-// Returns a pointer to any buffer associated with the flatbuffer tensor. Can
-// return nullptr if no buffer is found.
-void* GetFlatbufferTensorBuffer(
-    const tflite::Tensor& flatbuffer_tensor,
-    const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers) {
-  // We need to figure out where the actual contents of this tensor are stored
-  // in memory. We'll check to see if there's a serialized buffer (pretty much
-  // the same as a constant op in TensorFlow) associated with this tensor first,
-  // and if there is update the runtime structure to point to its location in
-  // memory.
-  // First see if there's any buffer information in the serialized tensor.
-  // TODO(b/170379532): Add better unit tests to validate flatbuffer values.
-  void* out_buffer = nullptr;
-  if (auto* buffer = (*buffers)[flatbuffer_tensor.buffer()]) {
-    // If we've found a buffer, does it have any data?
-    if (auto* array = buffer->data()) {
-      // If it has any data, is the data size larger than zero?
-      if (array->size()) {
-        // We've found a buffer with valid data, so update the runtime tensor
-        // data structure to point to it.
-        out_buffer = const_cast<void*>(static_cast<const void*>(array->data()));
-      }
-    }
-    // TODO(petewarden): It's not clear in what circumstances we could have a
-    // buffer in the serialized tensor, but it doesn't have any data in it. Is
-    // that a validly-generated file, and if so what does it mean, or is it an
-    // error condition? It would be good to tighten up the specification to make
-    // it less ambiguous.
-  }
-  return out_buffer;
-}
-
-TfLiteStatus InitializeTfLiteTensorFromFlatbuffer(
-    IPersistentBufferAllocator* persistent_buffer_allocator,
-    INonPersistentBufferAllocator* non_persistent_buffer_allocator,
-    bool allocate_temp, const tflite::Tensor& flatbuffer_tensor,
-    const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers,
-    ErrorReporter* error_reporter, TfLiteTensor* result) {
-  TFLITE_DCHECK(result != nullptr);
-
-  *result = {};
-  // Make sure the serialized type is one we know how to deal with, and convert
-  // it from a flatbuffer enum into a constant used by the kernel C API.
-  TF_LITE_ENSURE_STATUS(ConvertTensorType(flatbuffer_tensor.type(),
-                                          &result->type, error_reporter));
-  // Make sure we remember if the serialized tensor is designated as a variable.
-  result->is_variable = flatbuffer_tensor.is_variable();
-
-  result->data.data = GetFlatbufferTensorBuffer(flatbuffer_tensor, buffers);
-
-  // TODO(petewarden): Some of these paths aren't getting enough testing
-  // coverage, so we should figure out some tests that exercise them.
-  if (result->data.data == nullptr) {
-    // The tensor contents haven't been set from a serialized buffer, so
-    // make a note that they will be allocated from memory. The actual
-    // allocation won't happen until later.
-    result->allocation_type = kTfLiteArenaRw;
-  } else {
-    // We set the data from a serialized buffer, so record tha.
-    result->allocation_type = kTfLiteMmapRo;
-  }
-
-  // Figure out what the size in bytes of the buffer is and store it.
-  size_t type_size;
-  TF_LITE_ENSURE_STATUS(BytesRequiredForTensor(
-      flatbuffer_tensor, &result->bytes, &type_size, error_reporter));
-
-  if (flatbuffer_tensor.shape() == nullptr) {
-    // flatbuffer_tensor.shape() can return a nullptr in the case of a scalar
-    // tensor.
-    // TODO(b/188459715): figure out why const_cast is required here.
-    result->dims = const_cast<TfLiteIntArray*>(&kZeroLengthIntArray);
-  } else {
-    // TFLM doesn't allow reshaping the tensor which requires dynamic memory
-    // allocation so it is safe to drop the const qualifier. In the future, if
-    // we really want to update the tensor shape, we can always pass in a new
-    // TfLiteIntArray - especially we have to do so if the dimension is
-    result->dims = FlatBufferVectorToTfLiteTypeArray(flatbuffer_tensor.shape());
-  }
-
-  // Copy the quantization information from the serialized data.
-  const auto* src_quantization = flatbuffer_tensor.quantization();
-  if (src_quantization && src_quantization->scale() &&
-      (src_quantization->scale()->size() > 0) &&
-      src_quantization->zero_point() &&
-      (src_quantization->zero_point()->size() > 0)) {
-    // Always populate the TfLiteTensor.params field, even if there are
-    // per-channel quantization parameters.
-    result->params.scale = src_quantization->scale()->Get(0);
-    // Note that the zero_point field in the FlatBuffers schema is a 64-bit
-    // integer, but the zero_point field in the TfLiteQuantizationParams struct
-    // is a 32-bit integer.
-    result->params.zero_point =
-        static_cast<int32_t>(src_quantization->zero_point()->Get(0));
-
-    // Populate per-channel quantization params.
-    int channels = src_quantization->scale()->size();
-    TfLiteAffineQuantization* quantization =
-        allocate_temp
-            ? reinterpret_cast<TfLiteAffineQuantization*>(
-                  non_persistent_buffer_allocator->AllocateTemp(
-                      sizeof(TfLiteAffineQuantization),
-                      alignof(TfLiteAffineQuantization)))
-            : reinterpret_cast<TfLiteAffineQuantization*>(
-                  persistent_buffer_allocator->AllocatePersistentBuffer(
-                      sizeof(TfLiteAffineQuantization),
-                      alignof(TfLiteAffineQuantization)));
-    if (quantization == nullptr) {
-      TF_LITE_REPORT_ERROR(error_reporter,
-                           "Unable to allocate TfLiteAffineQuantization.\n");
-      return kTfLiteError;
-    }
-
-    // TODO(b/153688719): Reduce tail allocation by using a global zero-point
-    // buffer. This value can not be reused from the flatbuffer since the
-    // zero_point is stored as a int64_t.
-    quantization->zero_point =
-        allocate_temp
-            ? reinterpret_cast<TfLiteIntArray*>(
-                  non_persistent_buffer_allocator->AllocateTemp(
-                      TfLiteIntArrayGetSizeInBytes(channels),
-                      alignof(TfLiteIntArray)))
-            : reinterpret_cast<TfLiteIntArray*>(
-                  persistent_buffer_allocator->AllocatePersistentBuffer(
-                      TfLiteIntArrayGetSizeInBytes(channels),
-                      alignof(TfLiteIntArray)));
-    if (quantization->zero_point == nullptr) {
-      TF_LITE_REPORT_ERROR(error_reporter,
-                           "Unable to allocate quantization->zero_point.\n");
-      return kTfLiteError;
-    }
-
-    quantization->scale =
-        FlatBufferVectorToTfLiteTypeArray(src_quantization->scale());
-
-    quantization->zero_point->size = channels;
-    int* zero_point_data = quantization->zero_point->data;
-    for (int i = 0; i < channels; i++) {
-      // As a space-saving optimization, zero point arrays for weights can be
-      // reduced to a single value, since all zero points for weights are 0.
-      zero_point_data[i] = src_quantization->zero_point()->size() ==
-                                   src_quantization->scale()->size()
-                               ? src_quantization->zero_point()->Get(i)
-                               : src_quantization->zero_point()->Get(0);
-    }
-    // TODO(rocky): Need to add a micro_allocator test case that fails when
-    // this is not copied:
-    quantization->quantized_dimension = src_quantization->quantized_dimension();
-
-    result->quantization = {kTfLiteAffineQuantization, quantization};
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus InitializeTfLiteEvalTensorFromFlatbuffer(
-    const tflite::Tensor& flatbuffer_tensor,
-    const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers,
-    ErrorReporter* error_reporter, TfLiteEvalTensor* result) {
-  *result = {};
-  // Make sure the serialized type is one we know how to deal with, and convert
-  // it from a flatbuffer enum into a constant used by the kernel C API.
-  TF_LITE_ENSURE_STATUS(ConvertTensorType(flatbuffer_tensor.type(),
-                                          &result->type, error_reporter));
-
-  result->data.data = GetFlatbufferTensorBuffer(flatbuffer_tensor, buffers);
-
-  if (flatbuffer_tensor.shape() == nullptr) {
-    // flatbuffer_tensor.shape() can return a nullptr in the case of a scalar
-    // tensor.
-    result->dims = const_cast<TfLiteIntArray*>(&kZeroLengthIntArray);
-  } else {
-    result->dims = FlatBufferVectorToTfLiteTypeArray(flatbuffer_tensor.shape());
-  }
-  return kTfLiteOk;
-}
-
-}  // namespace internal
-
-size_t MicroAllocator::GetDefaultTailUsage(bool is_memory_planner_given) {
-  // TODO(b/208703041): a template version of AlignSizeUp to make expression
-  // shorter.
-  size_t total_size =
-      AlignSizeUp(sizeof(SingleArenaBufferAllocator),
-                  alignof(SingleArenaBufferAllocator)) +
-      AlignSizeUp(sizeof(MicroAllocator), alignof(MicroAllocator)) +
-      AlignSizeUp(sizeof(MicroBuiltinDataAllocator),
-                  alignof(MicroBuiltinDataAllocator)) +
-      AlignSizeUp(sizeof(SubgraphAllocations), alignof(SubgraphAllocations));
-  if (!is_memory_planner_given) {
-    total_size +=
-        AlignSizeUp(sizeof(GreedyMemoryPlanner), alignof(GreedyMemoryPlanner));
-  }
-  return total_size;
-}
-
-MicroAllocator::MicroAllocator(SingleArenaBufferAllocator* memory_allocator,
-                               MicroMemoryPlanner* memory_planner,
-                               ErrorReporter* error_reporter)
-    : non_persistent_buffer_allocator_(memory_allocator),
-      persistent_buffer_allocator_(memory_allocator),
-      memory_planner_(memory_planner),
-      error_reporter_(error_reporter),
-      model_is_allocating_(false) {}
-
-MicroAllocator::MicroAllocator(
-    IPersistentBufferAllocator* persistent_buffer_allocator,
-    INonPersistentBufferAllocator* non_persistent_buffer_allocator,
-    MicroMemoryPlanner* memory_planner, ErrorReporter* error_reporter)
-    : non_persistent_buffer_allocator_(non_persistent_buffer_allocator),
-      persistent_buffer_allocator_(persistent_buffer_allocator),
-      memory_planner_(memory_planner),
-      error_reporter_(error_reporter),
-      model_is_allocating_(false) {}
-
-MicroAllocator::~MicroAllocator() {}
-
-MicroAllocator* MicroAllocator::Create(uint8_t* tensor_arena, size_t arena_size,
-                                       MicroMemoryPlanner* memory_planner,
-                                       ErrorReporter* error_reporter) {
-  uint8_t* aligned_arena =
-      AlignPointerUp(tensor_arena, MicroArenaBufferAlignment());
-  size_t aligned_arena_size = tensor_arena + arena_size - aligned_arena;
-  SingleArenaBufferAllocator* memory_allocator =
-      SingleArenaBufferAllocator::Create(error_reporter, aligned_arena,
-                                         aligned_arena_size);
-
-  return Create(memory_allocator, memory_planner, error_reporter);
-}
-
-MicroAllocator* MicroAllocator::Create(uint8_t* tensor_arena, size_t arena_size,
-                                       ErrorReporter* error_reporter) {
-  uint8_t* aligned_arena =
-      AlignPointerUp(tensor_arena, MicroArenaBufferAlignment());
-  size_t aligned_arena_size = tensor_arena + arena_size - aligned_arena;
-  SingleArenaBufferAllocator* memory_allocator =
-      SingleArenaBufferAllocator::Create(error_reporter, aligned_arena,
-                                         aligned_arena_size);
-
-  // By default create GreedyMemoryPlanner.
-  // If a different MemoryPlanner is needed, use the other api.
-  uint8_t* memory_planner_buffer = memory_allocator->AllocatePersistentBuffer(
-      sizeof(GreedyMemoryPlanner), alignof(GreedyMemoryPlanner));
-  GreedyMemoryPlanner* memory_planner =
-      new (memory_planner_buffer) GreedyMemoryPlanner();
-
-  return Create(memory_allocator, memory_planner, error_reporter);
-}
-
-MicroAllocator* MicroAllocator::Create(
-    SingleArenaBufferAllocator* memory_allocator,
-    MicroMemoryPlanner* memory_planner, ErrorReporter* error_reporter) {
-  TFLITE_DCHECK(memory_allocator != nullptr);
-  TFLITE_DCHECK(error_reporter != nullptr);
-  TFLITE_DCHECK(memory_planner != nullptr);
-
-  uint8_t* allocator_buffer = memory_allocator->AllocatePersistentBuffer(
-      sizeof(MicroAllocator), alignof(MicroAllocator));
-  MicroAllocator* allocator = new (allocator_buffer) MicroAllocator(
-      memory_allocator, memory_allocator, memory_planner, error_reporter);
-  return allocator;
-}
-
-MicroAllocator* MicroAllocator::Create(uint8_t* persistent_tensor_arena,
-                                       size_t persistent_arena_size,
-                                       uint8_t* non_persistent_tensor_arena,
-                                       size_t non_persistent_arena_size,
-                                       ErrorReporter* error_reporter) {
-  TFLITE_DCHECK(persistent_tensor_arena != nullptr);
-  TFLITE_DCHECK(non_persistent_tensor_arena != nullptr);
-  TFLITE_DCHECK(persistent_tensor_arena != non_persistent_tensor_arena);
-  TFLITE_DCHECK(error_reporter != nullptr);
-
-  IPersistentBufferAllocator* persistent_buffer_allocator =
-      CreatePersistentArenaAllocator(persistent_tensor_arena,
-                                     persistent_arena_size);
-  INonPersistentBufferAllocator* non_persistent_buffer_allocator =
-      CreateNonPersistentArenaAllocator(non_persistent_tensor_arena,
-                                        non_persistent_arena_size,
-                                        persistent_buffer_allocator);
-
-  uint8_t* memory_planner_buffer =
-      persistent_buffer_allocator->AllocatePersistentBuffer(
-          sizeof(GreedyMemoryPlanner), alignof(GreedyMemoryPlanner));
-  GreedyMemoryPlanner* memory_planner =
-      new (memory_planner_buffer) GreedyMemoryPlanner();
-
-  uint8_t* micro_allocator_buffer =
-      persistent_buffer_allocator->AllocatePersistentBuffer(
-          sizeof(MicroAllocator), alignof(MicroAllocator));
-  MicroAllocator* allocator = new (micro_allocator_buffer) MicroAllocator(
-      persistent_buffer_allocator, non_persistent_buffer_allocator,
-      memory_planner, error_reporter);
-  return allocator;
-}
-
-SubgraphAllocations* MicroAllocator::StartModelAllocation(const Model* model) {
-  TFLITE_DCHECK(model != nullptr);
-
-  if (model_is_allocating_) {
-    TF_LITE_REPORT_ERROR(error_reporter_,
-                         "MicroAllocator: Model allocation started before "
-                         "finishing previously allocated model");
-    return nullptr;
-  }
-
-  model_is_allocating_ = true;
-
-  uint8_t* data_allocator_buffer =
-      persistent_buffer_allocator_->AllocatePersistentBuffer(
-          sizeof(MicroBuiltinDataAllocator),
-          alignof(MicroBuiltinDataAllocator));
-  builtin_data_allocator_ = new (data_allocator_buffer)
-      MicroBuiltinDataAllocator(persistent_buffer_allocator_);
-
-  if (InitScratchBufferData() != kTfLiteOk) {
-    return nullptr;
-  }
-
-  // Allocate struct to store eval tensors, nodes and registrations.
-  SubgraphAllocations* output = reinterpret_cast<SubgraphAllocations*>(
-      persistent_buffer_allocator_->AllocatePersistentBuffer(
-          sizeof(SubgraphAllocations) * model->subgraphs()->size(),
-          alignof(SubgraphAllocations)));
-  if (output == nullptr) {
-    MicroPrintf("Failed to allocate memory for model metadata.");
-    return nullptr;
-  }
-
-  if (AllocateTfLiteEvalTensors(model, output) != kTfLiteOk ||
-      AllocateNodeAndRegistrations(model, output) != kTfLiteOk) {
-    return nullptr;
-  }
-  return output;
-}
-
-TfLiteStatus MicroAllocator::FinishModelAllocation(
-    const Model* model, SubgraphAllocations* subgraph_allocations,
-    ScratchBufferHandle** scratch_buffer_handles) {
-  if (!model_is_allocating_) {
-    TF_LITE_REPORT_ERROR(error_reporter_,
-                         "MicroAllocator: Model allocation finished before "
-                         "starting allocating model");
-    return kTfLiteError;
-  }
-
-  // Allocate scratch buffer metadata.
-  TF_LITE_ENSURE_STATUS(AllocateScratchBufferHandles(
-      scratch_buffer_handles, scratch_buffer_request_count_));
-
-  // Allocate buffers for variable tensors.
-  for (size_t subgraph_idx = 0; subgraph_idx < model->subgraphs()->size();
-       subgraph_idx++) {
-    const SubGraph* subgraph = model->subgraphs()->Get(subgraph_idx);
-    TFLITE_DCHECK(subgraph != nullptr);
-    TF_LITE_ENSURE_STATUS(AllocateVariables(
-        subgraph, subgraph_allocations[subgraph_idx].tensors));
-  }
-
-  // Plan all subgraphs and scratch buffers together.
-  TF_LITE_ENSURE_STATUS(CommitStaticMemoryPlan(model, subgraph_allocations,
-                                               *scratch_buffer_handles));
-  model_is_allocating_ = false;
-  return kTfLiteOk;
-}
-
-void* MicroAllocator::AllocatePersistentBuffer(size_t bytes) {
-  return persistent_buffer_allocator_->AllocatePersistentBuffer(
-      bytes, MicroArenaBufferAlignment());
-}
-
-TfLiteStatus MicroAllocator::RequestScratchBufferInArena(size_t bytes,
-                                                         int subgraph_idx,
-                                                         int* buffer_idx) {
-  // All scratch buffer requests are stored in the head section of the arena
-  // when a model is in the prepare phase. First align a scratch buffer request
-  // pointer to the start of the head:
-  internal::ScratchBufferRequest* requests = GetScratchBufferRequests();
-
-  // Count the number of requested scratch buffers for the current node:
-  size_t current_node_request_count = 0;
-  for (size_t i = 0; i < scratch_buffer_request_count_; ++i) {
-    if (requests[i].node_idx == kUnassignedScratchBufferRequestIndex) {
-      ++current_node_request_count;
-    }
-  }
-
-  // First, ensure that the per-kernel request has not exceeded the limit:
-  if (current_node_request_count >= kMaxScratchBuffersPerOp) {
-    TF_LITE_REPORT_ERROR(
-        error_reporter_,
-        "Scratch buffer request exeeds limit per operator (%d)",
-        kMaxScratchBuffersPerOp);
-    return kTfLiteError;
-  }
-
-  // Initialize and assign values for the request at the current index:
-  internal::ScratchBufferRequest* current_request =
-      &requests[scratch_buffer_request_count_];
-  *current_request = {};
-  // Assign -1 as a sentinel value that will be updated when the node finishes
-  // allocating:
-  current_request->bytes = bytes;
-  current_request->node_idx = kUnassignedScratchBufferRequestIndex;
-  current_request->subgraph_idx = subgraph_idx;
-
-  // Assign the current request index to the out-param:
-  *buffer_idx = scratch_buffer_request_count_;
-
-  // Bump the request count to prepare for the next request:
-  ++scratch_buffer_request_count_;
-  return kTfLiteOk;
-}
-
-TfLiteStatus MicroAllocator::FinishPrepareNodeAllocations(int node_id) {
-  // When a node has finished preparing, all temp allocations performed by the
-  // kernel should be cleaned up:
-  TF_LITE_ENSURE_STATUS(ResetTempAllocations());
-
-  // Find and update any new scratch buffer requests for the current node:
-  internal::ScratchBufferRequest* requests = GetScratchBufferRequests();
-
-  for (size_t i = 0; i < scratch_buffer_request_count_; ++i) {
-    // A request with a node_idx of -1 is a sentinel value used to indicate this
-    // was a new request for the current node. The allocator finally knows the
-    // node index at this point. Assign the value and update the list of new
-    // requests so the head section can be adjusted to allow for the next kernel
-    // to allocate at most kMaxScratchBuffersPerOp requests:
-    if (requests[i].node_idx == kUnassignedScratchBufferRequestIndex) {
-      requests[i].node_idx = node_id;
-    }
-  }
-
-  // Ensure that the head is re-adjusted to allow for another at-most
-  // kMaxScratchBuffersPerOp scratch buffer requests in the next operator:
-  TF_LITE_ENSURE_STATUS(non_persistent_buffer_allocator_->ResizeBuffer(
-      scratch_buffer_head_,
-      sizeof(internal::ScratchBufferRequest) *
-          (scratch_buffer_request_count_ + kMaxScratchBuffersPerOp),
-      alignof(internal::ScratchBufferRequest)));
-
-  return kTfLiteOk;
-}
-
-size_t MicroAllocator::used_bytes() const {
-  return non_persistent_buffer_allocator_->GetNonPersistentUsedBytes() +
-         persistent_buffer_allocator_->GetPersistentUsedBytes();
-}
-
-TfLiteStatus MicroAllocator::AllocateNodeAndRegistrations(
-    const Model* model, SubgraphAllocations* subgraph_allocations) {
-  TFLITE_DCHECK(subgraph_allocations != nullptr);
-
-  for (size_t subgraph_idx = 0; subgraph_idx < model->subgraphs()->size();
-       subgraph_idx++) {
-    const SubGraph* subgraph = model->subgraphs()->Get(subgraph_idx);
-    TFLITE_DCHECK(subgraph != nullptr);
-
-    uint32_t operators_size = NumSubgraphOperators(subgraph);
-
-    // Initialize NodeAndRegistrations for the subgraph.
-    NodeAndRegistration* output = reinterpret_cast<NodeAndRegistration*>(
-        persistent_buffer_allocator_->AllocatePersistentBuffer(
-            sizeof(NodeAndRegistration) * operators_size,
-            alignof(NodeAndRegistration)));
-    if (output == nullptr) {
-      TF_LITE_REPORT_ERROR(
-          error_reporter_,
-          "Failed to allocate memory for node_and_registrations.");
-      return kTfLiteError;
-    }
-    subgraph_allocations[subgraph_idx].node_and_registrations = output;
-  }
-  return kTfLiteOk;
-}
-
-TfLiteTensor* MicroAllocator::AllocatePersistentTfLiteTensor(
-    const Model* model, const SubgraphAllocations* subgraph_allocations,
-    int tensor_index, int subgraph_index) {
-  const SubGraph* subgraph = model->subgraphs()->Get(subgraph_index);
-  TFLITE_DCHECK(subgraph != nullptr);
-
-  // This value is allocated from persistent arena space. It is guaranteed to be
-  // around for the lifetime of the application.
-  TfLiteTensor* tensor = AllocatePersistentTfLiteTensorInternal();
-
-  // Populate any fields from the flatbuffer, since this TfLiteTensor struct is
-  // allocated in the persistent section of the arena, ensure that additional
-  // allocations also take place in that section of the arena.
-  if (PopulateTfLiteTensorFromFlatbuffer(
-          model, tensor, tensor_index, subgraph_index,
-          /*allocate_temp=*/false) != kTfLiteOk) {
-    TF_LITE_REPORT_ERROR(error_reporter_,
-                         "Failed to populate a persistent TfLiteTensor struct "
-                         "from flatbuffer data!");
-    return nullptr;
-  }
-
-  if (subgraph_allocations != nullptr) {
-    // Tensor buffers that are allocated at runtime (e.g. non-weight buffers)
-    // and not located in the flatbuffer are stored on the pre-allocated list of
-    // TfLiteEvalTensors structs. These structs are the source of truth, simply
-    // point the corresponding buffer to the new TfLiteTensor data value.
-    tensor->data.data =
-        subgraph_allocations[subgraph_index].tensors[tensor_index].data.data;
-    // TfLiteEvalTensor structs must also be the source of truth for the
-    // TfLiteTensor dims.
-    tensor->dims =
-        subgraph_allocations[subgraph_index].tensors[tensor_index].dims;
-  }
-  return tensor;
-}
-
-void MicroAllocator::DeallocateTempTfLiteTensor(TfLiteTensor* tensor) {
-  TFLITE_DCHECK(tensor != nullptr);
-
-  if (tensor->quantization.type == kTfLiteAffineQuantization) {
-    TFLITE_DCHECK(tensor->quantization.params != nullptr);
-    TfLiteAffineQuantization* quantization =
-        reinterpret_cast<TfLiteAffineQuantization*>(
-            tensor->quantization.params);
-
-    non_persistent_buffer_allocator_->DeallocateTemp(
-        reinterpret_cast<uint8_t*>(quantization->zero_point));
-    non_persistent_buffer_allocator_->DeallocateTemp(
-        reinterpret_cast<uint8_t*>(quantization));
-  }
-
-  // Clear the data in case someone still access tensor arena by mistake
-  tensor->quantization.type = kTfLiteNoQuantization;
-  tensor->quantization.params = nullptr;
-  tensor->data.data = nullptr;
-  tensor->dims = nullptr;
-  non_persistent_buffer_allocator_->DeallocateTemp(
-      reinterpret_cast<uint8_t*>(tensor));
-}
-
-TfLiteTensor* MicroAllocator::AllocateTempTfLiteTensor(
-    const Model* model, const SubgraphAllocations* subgraph_allocations,
-    int tensor_index, int subgraph_index) {
-  const SubGraph* subgraph = model->subgraphs()->Get(subgraph_index);
-  TFLITE_DCHECK(subgraph != nullptr);
-
-  // This value is allocated from temporary arena space. It is guaranteed to be
-  // around for at least the scope of the calling function. Since this struct
-  // allocation takes place in temp space, no need to own or cleanup.
-  TfLiteTensor* tensor = reinterpret_cast<TfLiteTensor*>(
-      non_persistent_buffer_allocator_->AllocateTemp(sizeof(TfLiteTensor),
-                                                     alignof(TfLiteTensor)));
-
-  // Populate any fields from the flatbuffer, since this TfLiteTensor struct is
-  // allocated in the temp section of the arena, ensure that additional
-  // allocations also take place in that section of the arena.
-  if (PopulateTfLiteTensorFromFlatbuffer(model, tensor, tensor_index,
-                                         subgraph_index,
-                                         /*allocate_temp=*/true) != kTfLiteOk) {
-    TF_LITE_REPORT_ERROR(
-        error_reporter_,
-        "Failed to populate a temp TfLiteTensor struct from flatbuffer data!");
-    return nullptr;
-  }
-
-  if (subgraph_allocations != nullptr) {
-    // Tensor buffers that are allocated at runtime (e.g. non-weight buffers)
-    // and not located in the flatbuffer are stored on the pre-allocated list of
-    // TfLiteEvalTensors structs. These structs are the source of truth, simply
-    // point the corresponding buffer to the new TfLiteTensor data value.
-    tensor->data.data =
-        subgraph_allocations[subgraph_index].tensors[tensor_index].data.data;
-    // TfLiteEvalTensor structs must also be the source of truth for the
-    // TfLiteTensor dims.
-    tensor->dims =
-        subgraph_allocations[subgraph_index].tensors[tensor_index].dims;
-  }
-  return tensor;
-}
-
-TfLiteStatus MicroAllocator::ResetTempAllocations() {
-  return non_persistent_buffer_allocator_->ResetTempAllocations();
-}
-
-bool MicroAllocator::IsAllTempDeallocated() {
-  return non_persistent_buffer_allocator_->IsAllTempDeallocated();
-}
-
-TfLiteStatus MicroAllocator::AllocateTfLiteEvalTensors(
-    const Model* model, SubgraphAllocations* subgraph_allocations) {
-  TFLITE_DCHECK(subgraph_allocations != nullptr);
-
-  for (size_t subgraph_idx = 0; subgraph_idx < model->subgraphs()->size();
-       subgraph_idx++) {
-    const SubGraph* subgraph = model->subgraphs()->Get(subgraph_idx);
-    TFLITE_DCHECK(subgraph != nullptr);
-
-    size_t alloc_count = subgraph->tensors()->size();
-    TfLiteEvalTensor* tensors = reinterpret_cast<TfLiteEvalTensor*>(
-        persistent_buffer_allocator_->AllocatePersistentBuffer(
-            sizeof(TfLiteEvalTensor) * alloc_count, alignof(TfLiteEvalTensor)));
-    if (tensors == nullptr) {
-      TF_LITE_REPORT_ERROR(
-          error_reporter_,
-          "Failed to allocate memory for context->eval_tensors, "
-          "%d bytes required",
-          sizeof(TfLiteEvalTensor) * alloc_count);
-      return kTfLiteError;
-    }
-
-    for (size_t i = 0; i < alloc_count; ++i) {
-      TfLiteStatus status = internal::InitializeTfLiteEvalTensorFromFlatbuffer(
-          *subgraph->tensors()->Get(i), model->buffers(), error_reporter_,
-          &tensors[i]);
-      if (status != kTfLiteOk) {
-        TF_LITE_REPORT_ERROR(error_reporter_, "Failed to initialize tensor %d",
-                             i);
-        return kTfLiteError;
-      }
-    }
-    subgraph_allocations[subgraph_idx].tensors = tensors;
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus MicroAllocator::AllocateVariables(const SubGraph* subgraph,
-                                               TfLiteEvalTensor* eval_tensors) {
-  for (size_t i = 0; i < subgraph->tensors()->size(); ++i) {
-    auto* tensor = subgraph->tensors()->Get(i);
-    if (tensor->is_variable()) {
-      size_t buffer_size;
-      TF_LITE_ENSURE_STATUS(
-          TfLiteEvalTensorByteLength(&eval_tensors[i], &buffer_size));
-
-      eval_tensors[i].data.data =
-          persistent_buffer_allocator_->AllocatePersistentBuffer(
-              buffer_size, MicroArenaBufferAlignment());
-
-      if (eval_tensors[i].data.data == nullptr) {
-        TF_LITE_REPORT_ERROR(error_reporter_,
-                             "Failed to allocate variable tensor of size %d",
-                             buffer_size);
-        return kTfLiteError;
-      }
-    }
-  }
-  return kTfLiteOk;
-}
-
-TfLiteTensor* MicroAllocator::AllocatePersistentTfLiteTensorInternal() {
-  return reinterpret_cast<TfLiteTensor*>(
-      persistent_buffer_allocator_->AllocatePersistentBuffer(
-          sizeof(TfLiteTensor), alignof(TfLiteTensor)));
-}
-
-TfLiteStatus MicroAllocator::PopulateTfLiteTensorFromFlatbuffer(
-    const Model* model, TfLiteTensor* tensor, int tensor_index,
-    int subgraph_idx, bool allocate_temp) {
-  // TODO(b/162311891): This method serves as a stub to ensure quantized
-  // allocations in the tail can be recorded. Once the interpreter has APIs for
-  // accessing buffers on TfLiteEvalTensor this method can be dropped.
-  return internal::InitializeTfLiteTensorFromFlatbuffer(
-      persistent_buffer_allocator_, non_persistent_buffer_allocator_,
-      allocate_temp,
-      *model->subgraphs()->Get(subgraph_idx)->tensors()->Get(tensor_index),
-      model->buffers(), error_reporter_, tensor);
-}
-
-ErrorReporter* MicroAllocator::error_reporter() const {
-  return error_reporter_;
-}
-
-TfLiteStatus MicroAllocator::CommitStaticMemoryPlan(
-    const Model* model, SubgraphAllocations* allocations,
-    ScratchBufferHandle* scratch_buffer_handles) {
-  size_t head_usage = 0;
-  // Create static memory plan
-  // 1. Calculate AllocationInfo to know the lifetime of each tensor/buffer.
-  // 2. Add them into the planner (such as the GreedyMemoryPlanner).
-  // 3. Static memory planning using the planner.
-  // 4. Set tensor/buffer pointers based on the offsets from the previous step.
-  //
-  // Note that AllocationInfo is only needed for creating the plan. It will be
-  // allocated from the temp section and cleaned up at the bottom of this
-  // function.
-
-  // Use the AllocationInfoBuilder class to help determine where buffers are
-  // used in the subgraph.
-  AllocationInfoBuilder builder(model, non_persistent_buffer_allocator_,
-                                error_reporter_);
-  TF_LITE_ENSURE_STATUS(
-      builder.CreateAllocationInfo(scratch_buffer_request_count_));
-
-  const int32_t* offline_planner_offsets = nullptr;
-  TF_LITE_ENSURE_STATUS(
-      builder.GetOfflinePlannedOffsets(&offline_planner_offsets));
-  TF_LITE_ENSURE_STATUS(
-      builder.InitializeAllocationInfo(offline_planner_offsets, allocations));
-
-  internal::ScratchBufferRequest* scratch_buffer_requests =
-      GetScratchBufferRequests();
-  TF_LITE_ENSURE_STATUS(builder.MarkAllocationLifetimes(
-      0, scratch_buffer_requests, scratch_buffer_handles, allocations));
-  int allocation_info_count = builder.AllocationCount();
-  AllocationInfo* allocation_info = builder.Finish();
-
-  // Remaining arena size that memory planner can use for calculating offsets.
-  size_t remaining_arena_size =
-      non_persistent_buffer_allocator_->GetAvailableMemory(
-          MicroArenaBufferAlignment());
-  uint8_t* planner_arena = non_persistent_buffer_allocator_->AllocateTemp(
-      remaining_arena_size, MicroArenaBufferAlignment());
-  TF_LITE_ENSURE(error_reporter_, planner_arena != nullptr);
-  memory_planner_->Init(planner_arena, remaining_arena_size);
-  TF_LITE_ENSURE_STATUS(CreatePlan(error_reporter_, memory_planner_,
-                                   allocation_info, allocation_info_count));
-
-  // Commit the plan.
-  TF_LITE_ENSURE_STATUS(
-      CommitPlan(error_reporter_, memory_planner_,
-                 non_persistent_buffer_allocator_->GetOverlayMemoryAddress(),
-                 allocation_info, allocation_info_count));
-
-  // Reset all temp allocations used above:
-  builder.FreeAllocationInfo();
-  non_persistent_buffer_allocator_->DeallocateTemp(planner_arena);
-  TF_LITE_ENSURE_STATUS(
-      non_persistent_buffer_allocator_->ResetTempAllocations());
-  TF_LITE_ENSURE_STATUS(
-      non_persistent_buffer_allocator_->DeallocateResizableBuffer(
-          scratch_buffer_head_));
-
-#ifdef TF_LITE_SHOW_MEMORY_USE
-  memory_planner_->PrintMemoryPlan();
-#endif
-  head_usage = memory_planner_->GetMaximumMemorySize();
-
-  // The head is used to store memory plans for one model at a time during the
-  // model preparation stage, and is re-purposed to store scratch buffer handles
-  // during model invocation. The head must be as large as the greater of the
-  // largest model memory plan's size and the total space required for all
-  // scratch buffer handles.
-  if (max_head_buffer_usage_ < head_usage) {
-    max_head_buffer_usage_ = head_usage;
-  }
-
-  // The head is used for storing scratch buffer allocations before finalizing a
-  // memory plan in this function. Ensure that the head is set to the largest
-  // memory plan sent through the allocator:
-  TF_LITE_ENSURE_STATUS(
-      non_persistent_buffer_allocator_->ReserveNonPersistentOverlayMemory(
-          max_head_buffer_usage_, MicroArenaBufferAlignment()));
-  return kTfLiteOk;
-}
-
-TfLiteStatus MicroAllocator::AllocateScratchBufferHandles(
-    ScratchBufferHandle** scratch_buffer_handles, size_t handle_count) {
-  TFLITE_DCHECK(scratch_buffer_handles != nullptr);
-
-  if (scratch_buffer_request_count_ == 0) {
-    // No scratch buffer requests were requested during model allocation.
-    return kTfLiteOk;
-  }
-
-  // Allocate a consecutive block of memory store the scratch buffer handles.
-  // This alignment ensures quick lookup during inference time for the model:
-  *scratch_buffer_handles = reinterpret_cast<ScratchBufferHandle*>(
-      persistent_buffer_allocator_->AllocatePersistentBuffer(
-          sizeof(ScratchBufferHandle) * handle_count,
-          alignof(ScratchBufferHandle)));
-
-  return kTfLiteOk;
-}
-
-TfLiteStatus MicroAllocator::InitScratchBufferData() {
-  // A model is preparing to allocate resources, ensure that scratch buffer
-  // request counter is cleared:
-  scratch_buffer_request_count_ = 0;
-
-  // All requests will be stored in the head section. Each kernel is allowed at
-  // most kMaxScratchBuffersPerOp requests. Adjust the head to reserve at most
-  // that many requests to begin:
-  scratch_buffer_head_ =
-      non_persistent_buffer_allocator_->AllocateResizableBuffer(
-          sizeof(internal::ScratchBufferRequest) * kMaxScratchBuffersPerOp,
-          alignof(internal::ScratchBufferRequest));
-  if (scratch_buffer_head_ == nullptr) {
-    return kTfLiteError;
-  }
-
-  return kTfLiteOk;
-}
-
-internal::ScratchBufferRequest* MicroAllocator::GetScratchBufferRequests() {
-  return reinterpret_cast<internal::ScratchBufferRequest*>(AlignPointerUp(
-      scratch_buffer_head_, alignof(internal::ScratchBufferRequest)));
-}
-
-BuiltinDataAllocator* MicroAllocator::GetBuiltinDataAllocator() {
-  return builtin_data_allocator_;
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/micro_allocator.h

@@ -1,331 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_
-#define TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_
-
-#include <cstddef>
-#include <cstdint>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/core/api/error_reporter.h"
-#include "tensorflow/lite/core/api/flatbuffer_conversions.h"
-#include "tensorflow/lite/micro/arena_allocator/single_arena_buffer_allocator.h"
-#include "tensorflow/lite/micro/compatibility.h"
-#include "tensorflow/lite/micro/flatbuffer_utils.h"
-#include "tensorflow/lite/micro/memory_planner/micro_memory_planner.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-
-namespace tflite {
-
-// TODO(b/199402574): rename to tflite_internal or just remove internal
-// namespace.
-namespace internal {
-
-// Sets up all of the data structure members for a TfLiteTensor based on the
-// contents of a serialized tensor in the flatbuffer.
-// TODO(b/162311891): Drop this method when the interpreter has an API for
-// returning buffers on TfLiteEvalTensor.
-TfLiteStatus InitializeTfLiteTensorFromFlatbuffer(
-    IPersistentBufferAllocator* persistent_buffer_allocator,
-    INonPersistentBufferAllocator* non_persistent_buffer_allocator,
-    bool allocate_temp, const tflite::Tensor& flatbuffer_tensor,
-    const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers,
-    ErrorReporter* error_reporter, TfLiteTensor* result);
-
-// Holds placeholder information for a scratch buffer request from a kernel.
-// This struct is only used during the model prepare stage. Each request from a
-// kernel is stored in the head section. During the prepare stage, the head
-// section will at least hold kMaxScratchBuffersPerOp number of requests plus
-// any requests from previous kernel requests.
-//
-// When the memory plan is finalized, these structs are no longer used in favor
-// of a sequential, array of ScratchBufferHandle allocations in the tail
-// section. These allocations are indexed by the request API defined in the
-// TfLiteContext struct.
-struct ScratchBufferRequest {
-  // Number of bytes required by the buffer. The actual allocated size might be
-  // greater than `bytes` due to buffer alignment.
-  size_t bytes;
-  // Node where the buffer is allocated for. This provides useful information to
-  // determine the lifetime of the buffer. In AllocationInfo, this buffer will
-  // have `before` = node_idx and `after` = node_idx.
-  int node_idx;
-  int subgraph_idx;
-};
-
-}  // namespace internal
-
-struct NodeAndRegistration {
-  TfLiteNode node;
-  const TfLiteRegistration* registration;
-};
-
-// Holds a pointer to a buffer for a scratch buffer requested by a kernel during
-// the model prepare stage. This struct is allocated in-place and allows for
-// quick pointer-indexed lookup for speed during model inference.
-struct ScratchBufferHandle {
-  // Pointer to location of the scratch buffer:
-  uint8_t* data;
-};
-
-// Stores all per-subgraph allocations. This includes the node and registration
-// array, and tensor list for each subgraph.
-struct SubgraphAllocations {
-  NodeAndRegistration* node_and_registrations;
-  TfLiteEvalTensor* tensors;
-};
-
-// Allocator responsible for allocating memory for all intermediate tensors
-// necessary to invoke a model.
-//
-// The lifetime of the model, tensor arena and error reporter must be at
-// least as long as that of the allocator object, since the allocator needs
-// them to be accessible during its entire lifetime.
-//
-// The MicroAllocator simply plans out additional allocations that are required
-// to standup a model for inference in TF Micro. This class currently relies on
-// an additional allocator - SingleArenaBufferAllocator - for all allocations
-// from an arena. These allocations are divided into head (non-persistent) and
-// tail (persistent) regions:
-//
-// Memory layout to help understand how it works
-// This information could change in the future version.
-// ************** .memory_allocator->GetBuffer()
-// Tensors/Scratch buffers (head)
-// ************** .head_watermark
-// unused memory
-// ************** .memory_allocator->GetBuffer() + ->GetMaxBufferSize()
-//                                               - ->GetDataSize()
-// persistent area (tail)
-// ************** .memory_allocator->GetBuffer() + ->GetMaxBufferSize()
-class MicroAllocator {
- public:
-  // Creates a MicroAllocator instance from a given tensor arena. This arena
-  // will be managed by the created instance. The GreedyMemoryPlanner will
-  // by default be used and created on the arena.
-  // Note: Please use alignas(16) to make sure tensor_arena is 16
-  // bytes aligned, otherwise some head room will be wasted.
-  // TODO(b/157615197): Cleanup constructor + factory usage.
-  static MicroAllocator* Create(uint8_t* tensor_arena, size_t arena_size,
-                                ErrorReporter* error_reporter);
-
-  // Creates a MicroAllocator instance from a given tensor arena and a given
-  // MemoryPlanner. This arena will be managed by the created instance. Note:
-  // Please use alignas(16) to make sure tensor_arena is 16 bytes
-  // aligned, otherwise some head room will be wasted.
-  static MicroAllocator* Create(uint8_t* tensor_arena, size_t arena_size,
-                                MicroMemoryPlanner* memory_planner,
-                                ErrorReporter* error_reporter);
-
-  // Creates a MicroAllocator instance using the provided
-  // SingleArenaBufferAllocator instance and the MemoryPlanner. This allocator
-  // instance will use the SingleArenaBufferAllocator instance to manage
-  // allocations internally.
-  static MicroAllocator* Create(SingleArenaBufferAllocator* memory_allocator,
-                                MicroMemoryPlanner* memory_planner,
-                                ErrorReporter* error_reporter);
-
-  // Creates a MicroAllocator instance using the provided
-  // SingleArenaBufferAllocator instance and the MemoryPlanner. This allocator
-  // instance will use the SingleArenaBufferAllocator instance to manage
-  // allocations internally.
-  static MicroAllocator* Create(uint8_t* persistent_tensor_arena,
-                                size_t persistent_arena_size,
-                                uint8_t* non_persistent_tensor_arena,
-                                size_t non_persistent_arena_size,
-                                ErrorReporter* error_reporter);
-
-  // Returns the fixed amount of memory overhead of MicroAllocator.
-  static size_t GetDefaultTailUsage(bool is_memory_planner_given);
-
-  // Allocates internal resources required for model inference for each subgraph
-  // from the arena.
-  //
-  // This method will run through the flatbuffer data supplied in the model to
-  // properly allocate tensor, node, and op registration data. This method is
-  // expected to be followed with a call to FinishModelAllocation()  Returns a
-  // pointer to an array of SubgraphAllocations (also stored in the tail of the
-  // arena) where each index corresponds to a different subgraph in the model.
-  // Return value is nullptr if the allocations failed.
-  SubgraphAllocations* StartModelAllocation(const Model* model);
-
-  // Finish allocating internal resources required for model inference.
-  //
-  // -Plan the memory for activation tensors and scratch buffers.
-  // -Update eval tensors for each subgraph based on planned offsets.
-  // -Allocate scratch buffer handles array and update based on planned offsets.
-  //
-  // This method should be called after assigning model resources
-  // in StartModelAllocation(). The subgraph_allocations pointer should be the
-  // value passed into this class during StartModelAllocation(). Scratch buffer
-  // handles are stored in the out-param `scratch_buffer_handles` array which is
-  // allocated in this method. This value will be used in `GetScratchBuffer`
-  // call to retrieve scratch buffers.
-  TfLiteStatus FinishModelAllocation(
-      const Model* model, SubgraphAllocations* subgraph_allocations,
-      ScratchBufferHandle** scratch_buffer_handles);
-
-  // Allocates a TfLiteTensor struct and populates the returned value with
-  // properties from the model flatbuffer. This struct is allocated from
-  // persistent arena memory is only guaranteed for the lifetime of the
-  // application. The eval_tensors pointer should be the value passed into this
-  // class during StartModelAllocation() and contains the source-of-truth for
-  // buffers.
-  virtual TfLiteTensor* AllocatePersistentTfLiteTensor(
-      const Model* model, const SubgraphAllocations* subgraph_allocations,
-      int tensor_index, int subgraph_index);
-
-  // Allocates a TfLiteTensor struct and populates the returned value with
-  // properties from the model flatbuffer. This struct is allocated from
-  // temporary arena memory is only guaranteed until a call is made to
-  // ResetTempAllocations(). Subgraph_allocaitons contains the array of
-  // TfLiteEvalTensors. If the newly allocated temp at the specified subgraph
-  // and tensor index is already present int the TfLiteEvalTensor array, its
-  // data buffer will be re-used.
-  virtual TfLiteTensor* AllocateTempTfLiteTensor(
-      const Model* model, const SubgraphAllocations* subgraph_allocations,
-      int tensor_index, int subgraph_index);
-
-  virtual void DeallocateTempTfLiteTensor(TfLiteTensor*);
-
-  // Resets all temporary allocations. This method should be called after a
-  // chain of temp allocations (e.g. chain of TfLiteTensor objects via
-  // AllocateTfLiteTensor()).
-  virtual TfLiteStatus ResetTempAllocations();
-
-  // Returns true if all temporary buffers including temp TfLiteTensor are
-  // already deallocated.
-  virtual bool IsAllTempDeallocated();
-
-  // Allocates persistent buffer which has the same life time as the allocator.
-  // The memory is immediately available and is allocated from the tail of the
-  // arena.
-  virtual void* AllocatePersistentBuffer(size_t bytes);
-
-  // Register a scratch buffer of size `bytes` for Node with `node_id`.
-  // This method only requests a buffer with a given size to be used after a
-  // model has finished allocation via FinishModelAllocation(). All requested
-  // buffers will be accessible by the out-param in that method.
-  TfLiteStatus RequestScratchBufferInArena(size_t bytes, int subgraph_idx,
-                                           int* buffer_idx);
-
-  // Finish allocating a specific NodeAndRegistration prepare block (kernel
-  // entry for a model) with a given node ID. This call ensures that any scratch
-  // buffer requests and temporary allocations are handled and ready for the
-  // next node prepare block.
-  TfLiteStatus FinishPrepareNodeAllocations(int node_id);
-
-  // Returns the arena usage in bytes, only available after
-  // `FinishModelAllocation`. Otherwise, it will return 0.
-  size_t used_bytes() const;
-
-  BuiltinDataAllocator* GetBuiltinDataAllocator();
-
- protected:
-  MicroAllocator(SingleArenaBufferAllocator* memory_allocator,
-                 MicroMemoryPlanner* memory_planner,
-                 ErrorReporter* error_reporter);
-  MicroAllocator(IPersistentBufferAllocator* persistent_buffer_allocator,
-                 INonPersistentBufferAllocator* non_persistent_buffer_allocator,
-                 MicroMemoryPlanner* memory_planner,
-                 ErrorReporter* error_reporter);
-  virtual ~MicroAllocator();
-
-  // Allocates an array in the arena to hold pointers to the node and
-  // registration pointers required to represent the inference graph of the
-  // model.
-  virtual TfLiteStatus AllocateNodeAndRegistrations(
-      const Model* model, SubgraphAllocations* subgraph_allocations);
-
-  // Allocates the list of persistent TfLiteEvalTensors that are used for the
-  // "eval" phase of model inference. These structs will be the source of truth
-  // for all tensor buffers.
-  virtual TfLiteStatus AllocateTfLiteEvalTensors(
-      const Model* model, SubgraphAllocations* subgraph_allocations);
-  // Allocates persistent tensor buffers for variable tensors in the subgraph.
-  virtual TfLiteStatus AllocateVariables(const SubGraph* subgraph,
-                                         TfLiteEvalTensor* eval_tensors);
-
-  // Allocate and return a persistent TfLiteTensor.
-  // TODO(b/162311891): Drop this method when the interpreter has an API for
-  // accessing TfLiteEvalTensor structs.
-  virtual TfLiteTensor* AllocatePersistentTfLiteTensorInternal();
-
-  // Populates a TfLiteTensor struct with data from the model flatbuffer. Any
-  // quantization data is allocated from either the tail (persistent) or temp
-  // sections of the arena based on the allocation flag.
-  virtual TfLiteStatus PopulateTfLiteTensorFromFlatbuffer(const Model* model,
-                                                          TfLiteTensor* tensor,
-                                                          int tensor_index,
-                                                          int subgraph_idx,
-                                                          bool allocate_temp);
-
-  ErrorReporter* error_reporter() const;
-
- private:
-  // Commits a memory plan for all non-persistent buffer allocations in the
-  // 'head' section of the memory arena. The eval_tensors pointer is the list of
-  // pre-allocated TfLiteEvalTensor structs that will point to the buffers that
-  // will be allocated into the head section in this function call. The
-  // scratch_buffer_handles pointer is the array of pre-allocated
-  // ScratchBufferHandle structs that will point to allocated buffers also in
-  // the head section.
-  virtual TfLiteStatus CommitStaticMemoryPlan(
-      const Model* model, SubgraphAllocations* allocations,
-      ScratchBufferHandle* scratch_buffer_handles);
-
-  // Allocates an array of ScratchBufferHandle structs in the tail section for a
-  // given number of handles.
-  virtual TfLiteStatus AllocateScratchBufferHandles(
-      ScratchBufferHandle** scratch_buffer_handles, size_t handle_count);
-
-  // Clears all internal scratch buffer request counts and resets the head to
-  // prepare for kernels to request scratch buffer data when a model is
-  // preparing.
-  TfLiteStatus InitScratchBufferData();
-
-  // Returns the pointer for the array of ScratchBufferRequest allocations in
-  // the head section.
-  internal::ScratchBufferRequest* GetScratchBufferRequests();
-
-  // A simple memory allocator that always allocate from the arena tail or head.
-  INonPersistentBufferAllocator* non_persistent_buffer_allocator_;
-  IPersistentBufferAllocator* persistent_buffer_allocator_;
-
-  // Allocator used to allocate persistent builtin data.
-  BuiltinDataAllocator* builtin_data_allocator_;
-
-  // Activation buffer memory planner.
-  MicroMemoryPlanner* memory_planner_;
-
-  ErrorReporter* error_reporter_;
-  bool model_is_allocating_;
-
-  // Holds the number of ScratchBufferRequest instances stored in the head
-  // section when a model is allocating.
-  size_t scratch_buffer_request_count_ = 0;
-
-  // Holds ScratchBufferRequest when a model is allocating
-  uint8_t* scratch_buffer_head_ = nullptr;
-
-  // Holds the byte length of the memory plan with the largest head usage. Used
-  // to ensure that multi-tenant allocations can share the head for buffers.
-  size_t max_head_buffer_usage_ = 0;
-
-  TF_LITE_REMOVE_VIRTUAL_DELETE
-};
-
-}  // namespace tflite
-#endif  // TENSORFLOW_LITE_MICRO_MICRO_ALLOCATOR_H_

code/components/tflite-lib/tensorflow/lite/micro/micro_interpreter.cc

@@ -1,340 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/micro/micro_interpreter.h"
-
-#include <cstdarg>
-#include <cstddef>
-#include <cstdint>
-
-#include "flatbuffers/flatbuffers.h"  // from @flatbuffers
-#include "tensorflow/lite/c/c_api_types.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/core/api/error_reporter.h"
-#include "tensorflow/lite/core/api/tensor_utils.h"
-#include "tensorflow/lite/micro/flatbuffer_utils.h"
-#include "tensorflow/lite/micro/memory_helpers.h"
-#include "tensorflow/lite/micro/micro_allocator.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/micro/micro_op_resolver.h"
-#include "tensorflow/lite/micro/micro_profiler.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-#include "tensorflow/lite/schema/schema_utils.h"
-
-namespace tflite {
-
-MicroInterpreter::MicroInterpreter(const Model* model,
-                                   const MicroOpResolver& op_resolver,
-                                   uint8_t* tensor_arena,
-                                   size_t tensor_arena_size,
-                                   ErrorReporter* error_reporter,
-                                   MicroResourceVariables* resource_variables,
-                                   MicroProfiler* profiler)
-    : model_(model),
-      op_resolver_(op_resolver),
-      error_reporter_(error_reporter),
-      allocator_(*MicroAllocator::Create(tensor_arena, tensor_arena_size,
-                                         error_reporter)),
-
-      graph_(&context_, model, &allocator_, resource_variables),
-      tensors_allocated_(false),
-      initialization_status_(kTfLiteError),
-      input_tensors_(nullptr),
-      output_tensors_(nullptr),
-      micro_context_(&allocator_, model_, &graph_) {
-  Init(profiler);
-}
-
-MicroInterpreter::MicroInterpreter(const Model* model,
-                                   const MicroOpResolver& op_resolver,
-                                   MicroAllocator* allocator,
-                                   ErrorReporter* error_reporter,
-                                   MicroResourceVariables* resource_variables,
-                                   MicroProfiler* profiler)
-    : model_(model),
-      op_resolver_(op_resolver),
-      error_reporter_(error_reporter),
-      allocator_(*allocator),
-      graph_(&context_, model, allocator, resource_variables),
-      tensors_allocated_(false),
-      initialization_status_(kTfLiteError),
-      input_tensors_(nullptr),
-      output_tensors_(nullptr),
-      micro_context_(&allocator_, model_, &graph_) {
-  Init(profiler);
-}
-
-MicroInterpreter::~MicroInterpreter() {
-  if (graph_.GetAllocations() != nullptr) {
-    graph_.FreeSubgraphs();
-  }
-}
-
-void MicroInterpreter::Init(MicroProfiler* profiler) {
-  context_.impl_ = static_cast<void*>(&micro_context_);
-  context_.ReportError = MicroContextReportOpError;
-  context_.GetTensor = MicroContextGetTensor;
-  context_.GetEvalTensor = MicroContextGetEvalTensor;
-  context_.profiler = profiler;
-
-  initialization_status_ = kTfLiteOk;
-}
-
-TfLiteStatus MicroInterpreter::PrepareNodeAndRegistrationDataFromFlatbuffer() {
-  for (int subgraph_idx = 0; subgraph_idx < graph_.NumSubgraphs();
-       subgraph_idx++) {
-    const SubGraph* subgraph = model_->subgraphs()->Get(subgraph_idx);
-    TFLITE_DCHECK(subgraph != nullptr);
-
-    auto* opcodes = model_->operator_codes();
-    BuiltinDataAllocator* builtin_data_allocator =
-        allocator_.GetBuiltinDataAllocator();
-    uint32_t operators_size = NumSubgraphOperators(subgraph);
-    for (size_t i = 0; i < operators_size; ++i) {
-      const auto* op = subgraph->operators()->Get(i);
-      const size_t index = op->opcode_index();
-      if (index >= opcodes->size()) {
-        MicroPrintf("Missing registration for opcode_index %d\n", index);
-        return kTfLiteError;
-      }
-      const auto* opcode = opcodes->Get(index);
-      TfLiteStatus status =
-          GetRegistrationFromOpCode(opcode, op_resolver_, error_reporter_,
-                                    &(graph_.GetAllocations()[subgraph_idx]
-                                          .node_and_registrations[i]
-                                          .registration));
-      if (status != kTfLiteOk) {
-        MicroPrintf("Failed to get registration from op code %s\n ",
-                    EnumNameBuiltinOperator(GetBuiltinCode(opcode)));
-        return status;
-      }
-      const auto* registration = graph_.GetAllocations()[subgraph_idx]
-                                     .node_and_registrations[i]
-                                     .registration;
-      if (registration == nullptr) {
-        MicroPrintf("Skipping op for opcode_index %d\n", index);
-        return kTfLiteError;
-      }
-      BuiltinOperator op_type =
-          static_cast<BuiltinOperator>(registration->builtin_code);
-
-      const char* custom_data = nullptr;
-      size_t custom_data_size = 0;
-      unsigned char* builtin_data = nullptr;
-
-      if (op_type == BuiltinOperator_CUSTOM) {
-        // Custom Ops may or may not have a non-null custom_options field.
-        if (op->custom_options() != nullptr) {
-          custom_data =
-              reinterpret_cast<const char*>(op->custom_options()->data());
-          custom_data_size = op->custom_options()->size();
-        }
-      } else {
-        if (op->custom_options() != nullptr) {
-          MicroPrintf(
-              "Unsupported behavior: found builtin operator %s with custom "
-              "options.\n",
-              EnumNameBuiltinOperator(op_type));
-          return kTfLiteError;
-        }
-
-        MicroOpResolver::BuiltinParseFunction parser =
-            op_resolver_.GetOpDataParser(op_type);
-        if (parser == nullptr) {
-          MicroPrintf("Did not find a parser for %s",
-                      EnumNameBuiltinOperator(op_type));
-
-          return kTfLiteError;
-        }
-        TF_LITE_ENSURE_STATUS(parser(op, error_reporter_,
-                                     builtin_data_allocator,
-                                     (void**)(&builtin_data)));
-      }
-
-      TfLiteIntArray* inputs_array =
-          FlatBufferVectorToTfLiteTypeArray(op->inputs());
-      TfLiteIntArray* outputs_array =
-          FlatBufferVectorToTfLiteTypeArray(op->outputs());
-
-      TfLiteNode* node = &(
-          graph_.GetAllocations()[subgraph_idx].node_and_registrations[i].node);
-      *node = {};
-      node->inputs = inputs_array;
-      node->outputs = outputs_array;
-      node->builtin_data = reinterpret_cast<void*>(builtin_data);
-      node->custom_initial_data = custom_data;
-      node->custom_initial_data_size = custom_data_size;
-
-      if (op->intermediates() && (op->intermediates()->size() > 0)) {
-        node->intermediates =
-            FlatBufferVectorToTfLiteTypeArray(op->intermediates());
-      }
-    }
-  }
-  return kTfLiteOk;
-}
-
-TfLiteStatus MicroInterpreter::AllocateTensors() {
-  SubgraphAllocations* allocations = allocator_.StartModelAllocation(model_);
-
-  if (allocations == nullptr) {
-    TF_LITE_REPORT_ERROR(error_reporter_,
-                         "Failed starting model allocation.\n");
-    initialization_status_ = kTfLiteError;
-    return kTfLiteError;
-  }
-
-  graph_.SetSubgraphAllocations(allocations);
-
-  TF_LITE_ENSURE_STATUS(PrepareNodeAndRegistrationDataFromFlatbuffer());
-
-  // Only allow AllocatePersistentBuffer in Init stage.
-  context_.AllocatePersistentBuffer = MicroContextAllocatePersistentBuffer;
-  context_.RequestScratchBufferInArena = nullptr;
-  context_.GetScratchBuffer = nullptr;
-  context_.GetExternalContext = nullptr;
-  TF_LITE_ENSURE_STATUS(graph_.InitSubgraphs());
-
-  // Both AllocatePersistentBuffer and RequestScratchBufferInArena is
-  // available in Prepare stage.
-  context_.RequestScratchBufferInArena =
-      MicroContextRequestScratchBufferInArena;
-  // external_context become available in Prepare stage.
-  context_.GetExternalContext = MicroContextGetExternalContext;
-
-  TF_LITE_ENSURE_STATUS(graph_.PrepareSubgraphs());
-
-  // Prepare is done, we're ready for Invoke. Memory allocation is no longer
-  // allowed. Kernels can only fetch scratch buffers via GetScratchBuffer.
-  context_.AllocatePersistentBuffer = nullptr;
-  context_.RequestScratchBufferInArena = nullptr;
-  context_.GetScratchBuffer = MicroContextGetScratchBuffer;
-
-  TF_LITE_ENSURE_OK(&context_, allocator_.FinishModelAllocation(
-                                   model_, graph_.GetAllocations(),
-                                   &scratch_buffer_handles_));
-
-  micro_context_.SetScratchBufferHandles(scratch_buffer_handles_);
-
-  // TODO(b/162311891): Drop these allocations when the interpreter supports
-  // handling buffers from TfLiteEvalTensor.
-  input_tensors_ =
-      reinterpret_cast<TfLiteTensor**>(allocator_.AllocatePersistentBuffer(
-          sizeof(TfLiteTensor*) * inputs_size()));
-  if (input_tensors_ == nullptr) {
-    TF_LITE_REPORT_ERROR(
-        error_reporter_,
-        "Failed to allocate memory for context->input_tensors_, "
-        "%d bytes required",
-        sizeof(TfLiteTensor*) * inputs_size());
-    return kTfLiteError;
-  }
-
-  for (size_t i = 0; i < inputs_size(); ++i) {
-    input_tensors_[i] = allocator_.AllocatePersistentTfLiteTensor(
-        model_, graph_.GetAllocations(), inputs().Get(i), 0);
-    if (input_tensors_[i] == nullptr) {
-      TF_LITE_REPORT_ERROR(error_reporter_,
-                           "Failed to initialize input tensor %d", i);
-      return kTfLiteError;
-    }
-  }
-
-  // TODO(b/162311891): Drop these allocations when the interpreter supports
-  // handling buffers from TfLiteEvalTensor.
-  output_tensors_ =
-      reinterpret_cast<TfLiteTensor**>(allocator_.AllocatePersistentBuffer(
-          sizeof(TfLiteTensor*) * outputs_size()));
-  if (output_tensors_ == nullptr) {
-    TF_LITE_REPORT_ERROR(
-        error_reporter_,
-        "Failed to allocate memory for context->output_tensors_, "
-        "%d bytes required",
-        sizeof(TfLiteTensor*) * outputs_size());
-    return kTfLiteError;
-  }
-
-  for (size_t i = 0; i < outputs_size(); ++i) {
-    output_tensors_[i] = allocator_.AllocatePersistentTfLiteTensor(
-        model_, graph_.GetAllocations(), outputs().Get(i), 0);
-    if (output_tensors_[i] == nullptr) {
-      TF_LITE_REPORT_ERROR(error_reporter_,
-                           "Failed to initialize output tensor %d", i);
-      return kTfLiteError;
-    }
-  }
-
-  TF_LITE_ENSURE_STATUS(ResetVariableTensors());
-
-  tensors_allocated_ = true;
-  return kTfLiteOk;
-}
-
-TfLiteStatus MicroInterpreter::Invoke() {
-  if (initialization_status_ != kTfLiteOk) {
-    TF_LITE_REPORT_ERROR(error_reporter_,
-                         "Invoke() called after initialization failed\n");
-    return kTfLiteError;
-  }
-
-  // Ensure tensors are allocated before the interpreter is invoked to avoid
-  // difficult to debug segfaults.
-  if (!tensors_allocated_) {
-    TF_LITE_ENSURE_OK(&context_, AllocateTensors());
-  }
-  return graph_.InvokeSubgraph(0);
-}
-
-TfLiteTensor* MicroInterpreter::input(size_t index) {
-  const size_t length = inputs_size();
-  if (index >= length) {
-    TF_LITE_REPORT_ERROR(error_reporter_,
-                         "Input index %d out of range (length is %d)", index,
-                         length);
-    return nullptr;
-  }
-  return input_tensors_[index];
-}
-
-TfLiteTensor* MicroInterpreter::output(size_t index) {
-  const size_t length = outputs_size();
-  if (index >= length) {
-    TF_LITE_REPORT_ERROR(error_reporter_,
-                         "Output index %d out of range (length is %d)", index,
-                         length);
-    return nullptr;
-  }
-  return output_tensors_[index];
-}
-// Repurposing free subgraphs to reset state for some ops for now
-// will reset api is made. See b/220940833#comment25 for more context.
-TfLiteStatus MicroInterpreter::Reset() {
-  TfLiteStatus status = graph_.FreeSubgraphs();
-  if (status != kTfLiteOk) {
-    return status;
-  }
-  return graph_.ResetVariableTensors();
-}
-
-// TODO: remove this API completely in favor of MicroInterpreter::Reset
-TfLiteStatus MicroInterpreter::ResetVariableTensors() {
-  return graph_.ResetVariableTensors();
-}
-
-TfLiteStatus MicroInterpreter::SetMicroExternalContext(
-    void* external_context_payload) {
-  return micro_context_.set_external_context(external_context_payload);
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/micro_interpreter.h

@@ -1,177 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_
-#define TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_
-
-#include <cstddef>
-#include <cstdint>
-
-#include "flatbuffers/flatbuffers.h"  // from @flatbuffers
-#include "tensorflow/lite/c/c_api_types.h"
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/core/api/error_reporter.h"
-#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
-#include "tensorflow/lite/micro/micro_allocator.h"
-#include "tensorflow/lite/micro/micro_context.h"
-#include "tensorflow/lite/micro/micro_graph.h"
-#include "tensorflow/lite/micro/micro_op_resolver.h"
-#include "tensorflow/lite/micro/micro_profiler.h"
-#include "tensorflow/lite/portable_type_to_tflitetype.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-
-/// Copied from tensorflow/lite/version.h to avoid a dependency chain into
-// tensorflow/core.
-#define TFLITE_SCHEMA_VERSION (3)
-
-namespace tflite {
-
-class MicroInterpreter {
- public:
-  // The lifetime of the model, op resolver, tensor arena, error reporter,
-  // resource variables, and profiler must be at least as long as that of the
-  // interpreter object, since the interpreter may need to access them at any
-  // time. This means that you should usually create them with the same scope as
-  // each other, for example having them all allocated on the stack as local
-  // variables through a top-level function. The interpreter doesn't do any
-  // deallocation of any of the pointed-to objects, ownership remains with the
-  // caller.
-  MicroInterpreter(const Model* model, const MicroOpResolver& op_resolver,
-                   uint8_t* tensor_arena, size_t tensor_arena_size,
-                   ErrorReporter* error_reporter,
-                   MicroResourceVariables* resource_variables = nullptr,
-                   MicroProfiler* profiler = nullptr);
-
-  // Create an interpreter instance using an existing MicroAllocator instance.
-  // This constructor should be used when creating an allocator that needs to
-  // have allocation handled in more than one interpreter or for recording
-  // allocations inside the interpreter. The lifetime of the allocator must be
-  // as long as that of the interpreter object.
-  MicroInterpreter(const Model* model, const MicroOpResolver& op_resolver,
-                   MicroAllocator* allocator, ErrorReporter* error_reporter,
-                   MicroResourceVariables* resource_variables = nullptr,
-                   MicroProfiler* profiler = nullptr);
-
-  ~MicroInterpreter();
-
-  // Runs through the model and allocates all necessary input, output and
-  // intermediate tensors.
-  TfLiteStatus AllocateTensors();
-
-  // In order to support partial graph runs for strided models, this can return
-  // values other than kTfLiteOk and kTfLiteError.
-  // TODO(b/149795762): Add this to the TfLiteStatus enum.
-  TfLiteStatus Invoke();
-
-  // This is the recommended API for an application to pass an external payload
-  // pointer as an external context to kernels. The life time of the payload
-  // pointer should be at least as long as this interpreter. TFLM supports only
-  // one external context.
-  TfLiteStatus SetMicroExternalContext(void* external_context_payload);
-
-  TfLiteTensor* input(size_t index);
-  size_t inputs_size() const {
-    return model_->subgraphs()->Get(0)->inputs()->size();
-  }
-  const flatbuffers::Vector<int32_t>& inputs() const {
-    return *model_->subgraphs()->Get(0)->inputs();
-  }
-  TfLiteTensor* input_tensor(size_t index) { return input(index); }
-  template <class T>
-  T* typed_input_tensor(int tensor_index) {
-    if (TfLiteTensor* tensor_ptr = input_tensor(tensor_index)) {
-      if (tensor_ptr->type == typeToTfLiteType<T>()) {
-        return GetTensorData<T>(tensor_ptr);
-      }
-    }
-    return nullptr;
-  }
-
-  TfLiteTensor* output(size_t index);
-  size_t outputs_size() const {
-    return model_->subgraphs()->Get(0)->outputs()->size();
-  }
-  const flatbuffers::Vector<int32_t>& outputs() const {
-    return *model_->subgraphs()->Get(0)->outputs();
-  }
-  TfLiteTensor* output_tensor(size_t index) { return output(index); }
-  template <class T>
-  T* typed_output_tensor(int tensor_index) {
-    if (TfLiteTensor* tensor_ptr = output_tensor(tensor_index)) {
-      if (tensor_ptr->type == typeToTfLiteType<T>()) {
-        return GetTensorData<T>(tensor_ptr);
-      }
-    }
-    return nullptr;
-  }
-
-  // Reset the state to be what you would expect when the interpreter is first
-  // created. i.e. after Init and Prepare is called for the very first time.
-  TfLiteStatus Reset();
-
-  // TODO(b/244457206): remove this in favor of Reset()
-  // Reset all variable tensors to the default value.
-  TfLiteStatus ResetVariableTensors();
-
-  TfLiteStatus initialization_status() const { return initialization_status_; }
-
-  // Populates node and registration pointers representing the inference graph
-  // of the model from values inside the flatbuffer (loaded from the TfLiteModel
-  // instance). Persistent data (e.g. operator data) is allocated from the
-  // arena.
-  TfLiteStatus PrepareNodeAndRegistrationDataFromFlatbuffer();
-
-  // For debugging only.
-  // Returns the actual used arena in bytes. This method gives the optimal arena
-  // size. It's only available after `AllocateTensors` has been called.
-  // Note that normally `tensor_arena` requires 16 bytes alignment to fully
-  // utilize the space. If it's not the case, the optimial arena size would be
-  // arena_used_bytes() + 16.
-  size_t arena_used_bytes() const { return allocator_.used_bytes(); }
-
- protected:
-  const MicroAllocator& allocator() const { return allocator_; }
-  const TfLiteContext& context() const { return context_; }
-
- private:
-  // TODO(b/158263161): Consider switching to Create() function to enable better
-  // error reporting during initialization.
-  void Init(MicroProfiler* profiler);
-
-  // Gets the current subgraph index used from within context methods.
-  int get_subgraph_index() { return graph_.GetCurrentSubgraphIndex(); }
-
-  const Model* model_;
-  const MicroOpResolver& op_resolver_;
-  ErrorReporter* error_reporter_;
-  TfLiteContext context_ = {};
-  MicroAllocator& allocator_;
-  MicroGraph graph_;
-  bool tensors_allocated_;
-
-  TfLiteStatus initialization_status_;
-
-  ScratchBufferHandle* scratch_buffer_handles_ = nullptr;
-
-  // TODO(b/162311891): Clean these pointers up when this class supports buffers
-  // from TfLiteEvalTensor.
-  TfLiteTensor** input_tensors_;
-  TfLiteTensor** output_tensors_;
-
-  MicroContext micro_context_;
-};
-
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_MICRO_MICRO_INTERPRETER_H_

code/components/tflite-lib/tensorflow/lite/micro/micro_mutable_op_resolver.h

@@ -1,645 +0,0 @@
-/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#ifndef TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_
-#define TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_
-
-#include <cstdio>
-#include <cstring>
-
-#include "tensorflow/lite/c/common.h"
-#include "tensorflow/lite/core/api/error_reporter.h"
-#include "tensorflow/lite/core/api/flatbuffer_conversions.h"
-#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/kernels/op_macros.h"
-#include "tensorflow/lite/micro/compatibility.h"
-#include "tensorflow/lite/micro/kernels/add.h"
-#include "tensorflow/lite/micro/kernels/conv.h"
-#include "tensorflow/lite/micro/kernels/depthwise_conv.h"
-#include "tensorflow/lite/micro/kernels/ethosu.h"
-#include "tensorflow/lite/micro/kernels/fully_connected.h"
-#include "tensorflow/lite/micro/kernels/micro_ops.h"
-#include "tensorflow/lite/micro/kernels/pooling.h"
-#include "tensorflow/lite/micro/kernels/reduce.h"
-#include "tensorflow/lite/micro/kernels/softmax.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/micro/micro_op_resolver.h"
-#include "tensorflow/lite/schema/schema_generated.h"
-
-namespace tflite {
-TfLiteRegistration* Register_DETECTION_POSTPROCESS();
-
-template <unsigned int tOpCount>
-class MicroMutableOpResolver : public MicroOpResolver {
- public:
-  TF_LITE_REMOVE_VIRTUAL_DELETE
-
-  explicit MicroMutableOpResolver(ErrorReporter* error_reporter = nullptr)
-      : error_reporter_(error_reporter) {}
-
-  const TfLiteRegistration* FindOp(tflite::BuiltinOperator op) const override {
-    if (op == BuiltinOperator_CUSTOM) return nullptr;
-
-    for (unsigned int i = 0; i < registrations_len_; ++i) {
-      const TfLiteRegistration& registration = registrations_[i];
-      if (registration.builtin_code == op) {
-        return &registration;
-      }
-    }
-    return nullptr;
-  }
-
-  const TfLiteRegistration* FindOp(const char* op) const override {
-    for (unsigned int i = 0; i < registrations_len_; ++i) {
-      const TfLiteRegistration& registration = registrations_[i];
-      if ((registration.builtin_code == BuiltinOperator_CUSTOM) &&
-          (strcmp(registration.custom_name, op) == 0)) {
-        return &registration;
-      }
-    }
-    return nullptr;
-  }
-
-  MicroOpResolver::BuiltinParseFunction GetOpDataParser(
-      BuiltinOperator op) const override {
-    TFLITE_DCHECK(num_buitin_ops_ <= tOpCount);
-    for (unsigned int i = 0; i < num_buitin_ops_; ++i) {
-      if (builtin_codes_[i] == op) return builtin_parsers_[i];
-    }
-    return nullptr;
-  }
-
-  // Registers a Custom Operator with the MicroOpResolver.
-  //
-  // Only the first call for a given name will be successful. i.e. if this
-  // function is called again for a previously added Custom Operator, the
-  // MicroOpResolver will be unchanged and this function will return
-  // kTfLiteError.
-  TfLiteStatus AddCustom(const char* name, TfLiteRegistration* registration) {
-    if (registrations_len_ >= tOpCount) {
-      MicroPrintf(
-          "Couldn't register custom op '%s', resolver size is too"
-          "small (%d)",
-          name, tOpCount);
-      return kTfLiteError;
-    }
-
-    if (FindOp(name) != nullptr) {
-      MicroPrintf("Calling AddCustom for the same op more than once ");
-      MicroPrintf("is not supported (Op: %s).", name);
-      return kTfLiteError;
-    }
-
-    TfLiteRegistration* new_registration = &registrations_[registrations_len_];
-    registrations_len_ += 1;
-
-    *new_registration = *registration;
-    new_registration->builtin_code = BuiltinOperator_CUSTOM;
-    new_registration->custom_name = name;
-    return kTfLiteOk;
-  }
-
-  // The Add* functions below add the various Builtin operators to the
-  // MicroMutableOpResolver object.
-
-  TfLiteStatus AddAbs() {
-    return AddBuiltin(BuiltinOperator_ABS, tflite::ops::micro::Register_ABS(),
-                      ParseAbs);
-  }
-
-  TfLiteStatus AddAdd(const TfLiteRegistration& registration = Register_ADD()) {
-    return AddBuiltin(BuiltinOperator_ADD, registration, ParseAdd);
-  }
-
-  TfLiteStatus AddAddN() {
-    return AddBuiltin(BuiltinOperator_ADD_N, tflite::Register_ADD_N(),
-                      ParseAddN);
-  }
-
-  TfLiteStatus AddArgMax() {
-    return AddBuiltin(BuiltinOperator_ARG_MAX,
-                      tflite::ops::micro::Register_ARG_MAX(), ParseArgMax);
-  }
-
-  TfLiteStatus AddArgMin() {
-    return AddBuiltin(BuiltinOperator_ARG_MIN,
-                      tflite::ops::micro::Register_ARG_MIN(), ParseArgMin);
-  }
-
-  TfLiteStatus AddAssignVariable() {
-    return AddBuiltin(BuiltinOperator_ASSIGN_VARIABLE,
-                      tflite::Register_ASSIGN_VARIABLE(), ParseAssignVariable);
-  }
-
-  TfLiteStatus AddAveragePool2D(
-      const TfLiteRegistration& registration = Register_AVERAGE_POOL_2D()) {
-    return AddBuiltin(BuiltinOperator_AVERAGE_POOL_2D, registration, ParsePool);
-  }
-
-  TfLiteStatus AddBatchToSpaceNd() {
-    return AddBuiltin(BuiltinOperator_BATCH_TO_SPACE_ND,
-                      Register_BATCH_TO_SPACE_ND(), ParseBatchToSpaceNd);
-  }
-
-  TfLiteStatus AddBroadcastArgs() {
-    return AddBuiltin(BuiltinOperator_BROADCAST_ARGS, Register_BROADCAST_ARGS(),
-                      ParseBroadcastArgs);
-  }
-
-  TfLiteStatus AddBroadcastTo() {
-    return AddBuiltin(BuiltinOperator_BROADCAST_TO, Register_BROADCAST_TO(),
-                      ParseBroadcastTo);
-  }
-
-  TfLiteStatus AddCallOnce() {
-    return AddBuiltin(BuiltinOperator_CALL_ONCE, Register_CALL_ONCE(),
-                      ParseCallOnce);
-  }
-
-  TfLiteStatus AddCast() {
-    return AddBuiltin(BuiltinOperator_CAST, Register_CAST(), ParseCast);
-  }
-
-  TfLiteStatus AddCeil() {
-    return AddBuiltin(BuiltinOperator_CEIL, tflite::ops::micro::Register_CEIL(),
-                      ParseCeil);
-  }
-
-  TfLiteStatus AddCircularBuffer() {
-    return AddCustom("CIRCULAR_BUFFER", tflite::Register_CIRCULAR_BUFFER());
-  }
-
-  TfLiteStatus AddConcatenation() {
-    return AddBuiltin(BuiltinOperator_CONCATENATION,
-                      tflite::ops::micro::Register_CONCATENATION(),
-                      ParseConcatenation);
-  }
-
-  TfLiteStatus AddConv2D(
-      const TfLiteRegistration& registration = Register_CONV_2D()) {
-    return AddBuiltin(BuiltinOperator_CONV_2D, registration, ParseConv2D);
-  }
-
-  TfLiteStatus AddCos() {
-    return AddBuiltin(BuiltinOperator_COS, tflite::ops::micro::Register_COS(),
-                      ParseCos);
-  }
-
-  TfLiteStatus AddCumSum() {
-    return AddBuiltin(BuiltinOperator_CUMSUM, tflite::Register_CUMSUM(),
-                      ParseCumsum);
-  }
-
-  TfLiteStatus AddDepthToSpace() {
-    return AddBuiltin(BuiltinOperator_DEPTH_TO_SPACE,
-                      tflite::Register_DEPTH_TO_SPACE(), ParseDepthToSpace);
-  }
-
-  TfLiteStatus AddDepthwiseConv2D(
-      const TfLiteRegistration& registration = Register_DEPTHWISE_CONV_2D()) {
-    return AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D, registration,
-                      ParseDepthwiseConv2D);
-  }
-
-  TfLiteStatus AddDequantize() {
-    return AddBuiltin(BuiltinOperator_DEQUANTIZE, tflite::Register_DEQUANTIZE(),
-                      ParseDequantize);
-  }
-
-  TfLiteStatus AddDetectionPostprocess() {
-    return AddCustom("TFLite_Detection_PostProcess",
-                     tflite::Register_DETECTION_POSTPROCESS());
-  }
-
-  TfLiteStatus AddDiv() {
-    return AddBuiltin(BuiltinOperator_DIV, tflite::Register_DIV(), ParseDiv);
-  }
-
-  TfLiteStatus AddElu() {
-    return AddBuiltin(BuiltinOperator_ELU, tflite::Register_ELU(), ParseElu);
-  }
-
-  TfLiteStatus AddEqual() {
-    return AddBuiltin(BuiltinOperator_EQUAL,
-                      tflite::ops::micro::Register_EQUAL(), ParseEqual);
-  }
-
-  TfLiteStatus AddEthosU() {
-    TfLiteRegistration* registration = tflite::Register_ETHOSU();
-    if (registration) {
-      return AddCustom(tflite::GetString_ETHOSU(), registration);
-    }
-    return kTfLiteOk;
-  }
-
-  TfLiteStatus AddExp() {
-    return AddBuiltin(BuiltinOperator_EXP, Register_EXP(), ParseExp);
-  }
-
-  TfLiteStatus AddExpandDims() {
-    return AddBuiltin(BuiltinOperator_EXPAND_DIMS, Register_EXPAND_DIMS(),
-                      ParseExpandDims);
-  }
-
-  TfLiteStatus AddFill() {
-    return AddBuiltin(BuiltinOperator_FILL, tflite::Register_FILL(), ParseFill);
-  }
-
-  TfLiteStatus AddFloor() {
-    return AddBuiltin(BuiltinOperator_FLOOR,
-                      tflite::ops::micro::Register_FLOOR(), ParseFloor);
-  }
-
-  TfLiteStatus AddFloorDiv() {
-    return AddBuiltin(BuiltinOperator_FLOOR_DIV, tflite::Register_FLOOR_DIV(),
-                      ParseFloorDiv);
-  }
-
-  TfLiteStatus AddFloorMod() {
-    return AddBuiltin(BuiltinOperator_FLOOR_MOD, tflite::Register_FLOOR_MOD(),
-                      ParseFloorMod);
-  }
-
-  TfLiteStatus AddFullyConnected(
-      const TfLiteRegistration& registration = Register_FULLY_CONNECTED()) {
-    return AddBuiltin(BuiltinOperator_FULLY_CONNECTED, registration,
-                      ParseFullyConnected);
-  }
-
-  TfLiteStatus AddGather() {
-    return AddBuiltin(BuiltinOperator_GATHER, tflite::Register_GATHER(),
-                      ParseGather);
-  }
-
-  TfLiteStatus AddGatherNd() {
-    return AddBuiltin(BuiltinOperator_GATHER_ND, tflite::Register_GATHER_ND(),
-                      ParseGatherNd);
-  }
-
-  TfLiteStatus AddGreater() {
-    return AddBuiltin(BuiltinOperator_GREATER,
-                      tflite::ops::micro::Register_GREATER(), ParseGreater);
-  }
-
-  TfLiteStatus AddGreaterEqual() {
-    return AddBuiltin(BuiltinOperator_GREATER_EQUAL,
-                      tflite::ops::micro::Register_GREATER_EQUAL(),
-                      ParseGreaterEqual);
-  }
-
-  TfLiteStatus AddHardSwish() {
-    return AddBuiltin(BuiltinOperator_HARD_SWISH, tflite::Register_HARD_SWISH(),
-                      ParseHardSwish);
-  }
-
-  TfLiteStatus AddIf() {
-    return AddBuiltin(BuiltinOperator_IF, tflite::Register_IF(), ParseIf);
-  }
-
-  TfLiteStatus AddL2Normalization() {
-    return AddBuiltin(BuiltinOperator_L2_NORMALIZATION,
-                      tflite::ops::micro::Register_L2_NORMALIZATION(),
-                      ParseL2Normalization);
-  }
-
-  TfLiteStatus AddL2Pool2D() {
-    return AddBuiltin(BuiltinOperator_L2_POOL_2D, tflite::Register_L2_POOL_2D(),
-                      ParsePool);
-  }
-
-  TfLiteStatus AddLeakyRelu() {
-    return AddBuiltin(BuiltinOperator_LEAKY_RELU, tflite::Register_LEAKY_RELU(),
-                      ParseLeakyRelu);
-  }
-
-  TfLiteStatus AddLess() {
-    return AddBuiltin(BuiltinOperator_LESS, tflite::ops::micro::Register_LESS(),
-                      ParseLess);
-  }
-
-  TfLiteStatus AddLessEqual() {
-    return AddBuiltin(BuiltinOperator_LESS_EQUAL,
-                      tflite::ops::micro::Register_LESS_EQUAL(),
-                      ParseLessEqual);
-  }
-
-  TfLiteStatus AddLog() {
-    return AddBuiltin(BuiltinOperator_LOG, tflite::ops::micro::Register_LOG(),
-                      ParseLog);
-  }
-
-  TfLiteStatus AddLogicalAnd() {
-    return AddBuiltin(BuiltinOperator_LOGICAL_AND,
-                      tflite::Register_LOGICAL_AND(), ParseLogicalAnd);
-  }
-
-  TfLiteStatus AddLogicalNot() {
-    return AddBuiltin(BuiltinOperator_LOGICAL_NOT,
-                      tflite::ops::micro::Register_LOGICAL_NOT(),
-                      ParseLogicalNot);
-  }
-
-  TfLiteStatus AddLogicalOr() {
-    return AddBuiltin(BuiltinOperator_LOGICAL_OR, tflite::Register_LOGICAL_OR(),
-                      ParseLogicalOr);
-  }
-
-  TfLiteStatus AddLogistic() {
-    return AddBuiltin(BuiltinOperator_LOGISTIC, tflite::Register_LOGISTIC(),
-                      ParseLogistic);
-  }
-
-  TfLiteStatus AddMaximum() {
-    return AddBuiltin(BuiltinOperator_MAXIMUM,
-                      tflite::ops::micro::Register_MAXIMUM(), ParseMaximum);
-  }
-
-  TfLiteStatus AddMaxPool2D(
-      const TfLiteRegistration& registration = Register_MAX_POOL_2D()) {
-    return AddBuiltin(BuiltinOperator_MAX_POOL_2D, registration, ParsePool);
-  }
-
-  TfLiteStatus AddMirrorPad() {
-    return AddBuiltin(BuiltinOperator_MIRROR_PAD, tflite::Register_MIRROR_PAD(),
-                      ParseMirrorPad);
-  }
-
-  TfLiteStatus AddMean() {
-    return AddBuiltin(BuiltinOperator_MEAN, Register_MEAN(), ParseReducer);
-  }
-
-  TfLiteStatus AddMinimum() {
-    return AddBuiltin(BuiltinOperator_MINIMUM,
-                      tflite::ops::micro::Register_MINIMUM(), ParseMinimum);
-  }
-
-  TfLiteStatus AddMul(const TfLiteRegistration& registration = Register_MUL()) {
-    return AddBuiltin(BuiltinOperator_MUL, registration, ParseMul);
-  }
-
-  TfLiteStatus AddNeg() {
-    return AddBuiltin(BuiltinOperator_NEG, tflite::ops::micro::Register_NEG(),
-                      ParseNeg);
-  }
-
-  TfLiteStatus AddNotEqual() {
-    return AddBuiltin(BuiltinOperator_NOT_EQUAL,
-                      tflite::ops::micro::Register_NOT_EQUAL(), ParseNotEqual);
-  }
-
-  TfLiteStatus AddPack() {
-    return AddBuiltin(BuiltinOperator_PACK, tflite::ops::micro::Register_PACK(),
-                      ParsePack);
-  }
-
-  TfLiteStatus AddPad() {
-    return AddBuiltin(BuiltinOperator_PAD, tflite::ops::micro::Register_PAD(),
-                      ParsePad);
-  }
-
-  TfLiteStatus AddPadV2() {
-    return AddBuiltin(BuiltinOperator_PADV2,
-                      tflite::ops::micro::Register_PADV2(), ParsePadV2);
-  }
-
-  TfLiteStatus AddPrelu() {
-    return AddBuiltin(BuiltinOperator_PRELU, tflite::Register_PRELU(),
-                      ParsePrelu);
-  }
-
-  TfLiteStatus AddQuantize() {
-    return AddBuiltin(BuiltinOperator_QUANTIZE, Register_QUANTIZE(),
-                      ParseQuantize);
-  }
-
-  TfLiteStatus AddReadVariable() {
-    return AddBuiltin(BuiltinOperator_READ_VARIABLE,
-                      tflite::Register_READ_VARIABLE(), ParseReadVariable);
-  }
-
-  TfLiteStatus AddReduceMax() {
-    return AddBuiltin(BuiltinOperator_REDUCE_MAX, Register_REDUCE_MAX(),
-                      ParseReducer);
-  }
-
-  TfLiteStatus AddRelu() {
-    return AddBuiltin(BuiltinOperator_RELU, tflite::Register_RELU(), ParseRelu);
-  }
-
-  TfLiteStatus AddRelu6() {
-    return AddBuiltin(BuiltinOperator_RELU6, tflite::Register_RELU6(),
-                      ParseRelu6);
-  }
-
-  TfLiteStatus AddReshape() {
-    return AddBuiltin(BuiltinOperator_RESHAPE,
-                      tflite::ops::micro::Register_RESHAPE(), ParseReshape);
-  }
-
-  TfLiteStatus AddResizeBilinear() {
-    return AddBuiltin(BuiltinOperator_RESIZE_BILINEAR,
-                      Register_RESIZE_BILINEAR(), ParseResizeBilinear);
-  }
-
-  TfLiteStatus AddResizeNearestNeighbor() {
-    return AddBuiltin(BuiltinOperator_RESIZE_NEAREST_NEIGHBOR,
-                      tflite::ops::micro::Register_RESIZE_NEAREST_NEIGHBOR(),
-                      ParseResizeNearestNeighbor);
-  }
-
-  TfLiteStatus AddRound() {
-    return AddBuiltin(BuiltinOperator_ROUND,
-                      tflite::ops::micro::Register_ROUND(), ParseRound);
-  }
-
-  TfLiteStatus AddRsqrt() {
-    return AddBuiltin(BuiltinOperator_RSQRT,
-                      tflite::ops::micro::Register_RSQRT(), ParseRsqrt);
-  }
-
-  TfLiteStatus AddSelectV2() {
-    return AddBuiltin(BuiltinOperator_SELECT_V2, Register_SELECT_V2(),
-                      ParseSelectV2);
-  }
-
-  TfLiteStatus AddShape() {
-    return AddBuiltin(BuiltinOperator_SHAPE, Register_SHAPE(), ParseShape);
-  }
-
-  TfLiteStatus AddSin() {
-    return AddBuiltin(BuiltinOperator_SIN, tflite::ops::micro::Register_SIN(),
-                      ParseSin);
-  }
-
-  TfLiteStatus AddSlice() {
-    return AddBuiltin(BuiltinOperator_SLICE, Register_SLICE(), ParseSlice);
-  }
-
-  TfLiteStatus AddSoftmax(
-      const TfLiteRegistration& registration = Register_SOFTMAX()) {
-    return AddBuiltin(BuiltinOperator_SOFTMAX, registration, ParseSoftmax);
-  }
-
-  TfLiteStatus AddSpaceToBatchNd() {
-    return AddBuiltin(BuiltinOperator_SPACE_TO_BATCH_ND,
-                      Register_SPACE_TO_BATCH_ND(), ParseSpaceToBatchNd);
-  }
-
-  TfLiteStatus AddSpaceToDepth() {
-    return AddBuiltin(BuiltinOperator_SPACE_TO_DEPTH, Register_SPACE_TO_DEPTH(),
-                      ParseSpaceToDepth);
-  }
-
-  TfLiteStatus AddSplit() {
-    return AddBuiltin(BuiltinOperator_SPLIT,
-                      tflite::ops::micro::Register_SPLIT(), ParseSplit);
-  }
-
-  TfLiteStatus AddSplitV() {
-    return AddBuiltin(BuiltinOperator_SPLIT_V,
-                      tflite::ops::micro::Register_SPLIT_V(), ParseSplitV);
-  }
-
-  TfLiteStatus AddSqueeze() {
-    return AddBuiltin(BuiltinOperator_SQUEEZE, Register_SQUEEZE(),
-                      ParseSqueeze);
-  }
-
-  TfLiteStatus AddSqrt() {
-    return AddBuiltin(BuiltinOperator_SQRT, tflite::ops::micro::Register_SQRT(),
-                      ParseSqrt);
-  }
-
-  TfLiteStatus AddSquare() {
-    return AddBuiltin(BuiltinOperator_SQUARE,
-                      tflite::ops::micro::Register_SQUARE(), ParseSquare);
-  }
-
-  TfLiteStatus AddSquaredDifference() {
-    return AddBuiltin(BuiltinOperator_SQUARED_DIFFERENCE,
-                      tflite::Register_SQUARED_DIFFERENCE(),
-                      ParseSquaredDifference);
-  }
-
-  TfLiteStatus AddStridedSlice() {
-    return AddBuiltin(BuiltinOperator_STRIDED_SLICE,
-                      tflite::ops::micro::Register_STRIDED_SLICE(),
-                      ParseStridedSlice);
-  }
-
-  TfLiteStatus AddSub() {
-    return AddBuiltin(BuiltinOperator_SUB, tflite::Register_SUB(), ParseSub);
-  }
-
-  TfLiteStatus AddSum() {
-    return AddBuiltin(BuiltinOperator_SUM, Register_SUM(), ParseReducer);
-  }
-
-  TfLiteStatus AddSvdf(
-      const TfLiteRegistration& registration = Register_SVDF()) {
-    return AddBuiltin(BuiltinOperator_SVDF, registration, ParseSvdf);
-  }
-
-  TfLiteStatus AddTanh() {
-    return AddBuiltin(BuiltinOperator_TANH, tflite::ops::micro::Register_TANH(),
-                      ParseTanh);
-  }
-
-  TfLiteStatus AddTransposeConv() {
-    return AddBuiltin(BuiltinOperator_TRANSPOSE_CONV,
-                      tflite::Register_TRANSPOSE_CONV(), ParseTransposeConv);
-  }
-
-  TfLiteStatus AddTranspose() {
-    return AddBuiltin(BuiltinOperator_TRANSPOSE, Register_TRANSPOSE(),
-                      ParseTranspose);
-  }
-
-  TfLiteStatus AddUnpack() {
-    return AddBuiltin(BuiltinOperator_UNPACK,
-                      tflite::ops::micro::Register_UNPACK(), ParseUnpack);
-  }
-
-  TfLiteStatus AddUnidirectionalSequenceLSTM() {
-    return AddBuiltin(BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM,
-                      Register_UNIDIRECTIONAL_SEQUENCE_LSTM(),
-                      ParseUnidirectionalSequenceLSTM);
-  }
-
-  TfLiteStatus AddVarHandle() {
-    return AddBuiltin(BuiltinOperator_VAR_HANDLE, Register_VAR_HANDLE(),
-                      ParseVarHandle);
-  }
-
-  TfLiteStatus AddWhile() {
-    return AddBuiltin(BuiltinOperator_WHILE, Register_WHILE(), ParseWhile);
-  }
-
-  TfLiteStatus AddZerosLike() {
-    return AddBuiltin(BuiltinOperator_ZEROS_LIKE, Register_ZEROS_LIKE(),
-                      ParseZerosLike);
-  }
-
-  unsigned int GetRegistrationLength() { return registrations_len_; }
-
- private:
-  TfLiteStatus AddBuiltin(tflite::BuiltinOperator op,
-                          const TfLiteRegistration& registration,
-                          MicroOpResolver::BuiltinParseFunction parser) {
-    if (op == BuiltinOperator_CUSTOM) {
-      MicroPrintf("Invalid parameter BuiltinOperator_CUSTOM to the ");
-      MicroPrintf("AddBuiltin function.");
-      return kTfLiteError;
-    }
-
-    if (FindOp(op) != nullptr) {
-      MicroPrintf("Calling AddBuiltin with the same op more than ");
-      MicroPrintf("once is not supported (Op: #%d).", op);
-      return kTfLiteError;
-    }
-
-    if (registrations_len_ >= tOpCount) {
-      MicroPrintf("Couldn't register builtin op #%d, resolver size ", op);
-      MicroPrintf("is too small (%d).", tOpCount);
-      return kTfLiteError;
-    }
-
-    registrations_[registrations_len_] = registration;
-    // Strictly speaking, the builtin_code is not necessary for TFLM but filling
-    // it in regardless.
-    registrations_[registrations_len_].builtin_code = op;
-    registrations_len_++;
-
-    builtin_codes_[num_buitin_ops_] = op;
-    builtin_parsers_[num_buitin_ops_] = parser;
-    num_buitin_ops_++;
-
-    return kTfLiteOk;
-  }
-
-  TfLiteRegistration registrations_[tOpCount];
-  unsigned int registrations_len_ = 0;
-
-  // Arrays (and counter) to store the builtin codes and their corresponding
-  // parse functions as these are registered with the Op Resolver.
-  BuiltinOperator builtin_codes_[tOpCount];
-  MicroOpResolver::BuiltinParseFunction builtin_parsers_[tOpCount];
-  unsigned int num_buitin_ops_ = 0;
-
-  ErrorReporter* error_reporter_;
-};
-
-};  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_

code/components/tflite-lib/tensorflow/lite/micro/micro_profiler.cc

@@ -1,115 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-#include "tensorflow/lite/micro/micro_profiler.h"
-
-#include <cinttypes>
-#include <cstdint>
-#include <cstring>
-
-#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-#include "tensorflow/lite/micro/micro_time.h"
-
-namespace tflite {
-
-uint32_t MicroProfiler::BeginEvent(const char* tag) {
-  if (num_events_ == kMaxEvents) {
-    num_events_ = 0;
-  }
-
-  tags_[num_events_] = tag;
-  start_ticks_[num_events_] = GetCurrentTimeTicks();
-  end_ticks_[num_events_] = start_ticks_[num_events_] - 1;
-  return num_events_++;
-}
-
-void MicroProfiler::EndEvent(uint32_t event_handle) {
-  TFLITE_DCHECK(event_handle < kMaxEvents);
-  end_ticks_[event_handle] = GetCurrentTimeTicks();
-}
-
-uint32_t MicroProfiler::GetTotalTicks() const {
-  int32_t ticks = 0;
-  for (int i = 0; i < num_events_; ++i) {
-    ticks += end_ticks_[i] - start_ticks_[i];
-  }
-  return ticks;
-}
-
-void MicroProfiler::Log() const {
-#if !defined(TF_LITE_STRIP_ERROR_STRINGS)
-  for (int i = 0; i < num_events_; ++i) {
-    uint32_t ticks = end_ticks_[i] - start_ticks_[i];
-    MicroPrintf("%s took %" PRIu32 " ticks (%d ms).", tags_[i], ticks,
-                TicksToMs(ticks));
-  }
-#endif
-}
-
-void MicroProfiler::LogCsv() const {
-#if !defined(TF_LITE_STRIP_ERROR_STRINGS)
-  MicroPrintf("\"Event\",\"Tag\",\"Ticks\"");
-  for (int i = 0; i < num_events_; ++i) {
-    uint32_t ticks = end_ticks_[i] - start_ticks_[i];
-    MicroPrintf("%d,%s,%" PRIu32, i, tags_[i], ticks);
-  }
-#endif
-}
-
-void MicroProfiler::LogTicksPerTagCsv() {
-#if !defined(TF_LITE_STRIP_ERROR_STRINGS)
-  MicroPrintf(
-      "\"Unique Tag\",\"Total ticks across all events with that tag.\"");
-  int total_ticks = 0;
-  for (int i = 0; i < num_events_; ++i) {
-    uint32_t ticks = end_ticks_[i] - start_ticks_[i];
-    TFLITE_DCHECK(tags_[i] != nullptr);
-    int position = FindExistingOrNextPosition(tags_[i]);
-    TFLITE_DCHECK(position >= 0);
-    total_ticks_per_tag[position].tag = tags_[i];
-    total_ticks_per_tag[position].ticks =
-        total_ticks_per_tag[position].ticks + ticks;
-    total_ticks += ticks;
-  }
-
-  for (int i = 0; i < num_events_; ++i) {
-    TicksPerTag each_tag_entry = total_ticks_per_tag[i];
-    if (each_tag_entry.tag == nullptr) {
-      break;
-    }
-    MicroPrintf("%s, %d", each_tag_entry.tag, each_tag_entry.ticks);
-  }
-  MicroPrintf("total number of ticks, %d", total_ticks);
-#endif
-}
-
-// This method finds a particular array element in the total_ticks_per_tag array
-// with the matching tag_name passed in the method. If it can find a
-// matching array element that has the same tag_name, then it will return the
-// position of the matching element. But if it unable to find a matching element
-// with the given tag_name, it will return the next available empty position
-// from the array.
-int MicroProfiler::FindExistingOrNextPosition(const char* tag_name) {
-  int pos = 0;
-  for (; pos < num_events_; pos++) {
-    TicksPerTag each_tag_entry = total_ticks_per_tag[pos];
-    if (each_tag_entry.tag == nullptr ||
-        strcmp(each_tag_entry.tag, tag_name) == 0) {
-      return pos;
-    }
-  }
-  return pos < num_events_ ? pos : -1;
-}
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/micro/micro_profiler.h

@@ -1,139 +0,0 @@
-/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#ifndef TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_
-#define TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_
-
-#include <cstdint>
-
-#include "tensorflow/lite/micro/compatibility.h"
-
-namespace tflite {
-
-// MicroProfiler creates a common way to gain fine-grained insight into runtime
-// performance. Bottleck operators can be identified along with slow code
-// sections. This can be used in conjunction with running the relevant micro
-// benchmark to evaluate end-to-end performance.
-class MicroProfiler {
- public:
-  MicroProfiler() = default;
-  virtual ~MicroProfiler() = default;
-
-  // Marks the start of a new event and returns an event handle that can be used
-  // to mark the end of the event via EndEvent. The lifetime of the tag
-  // parameter must exceed that of the MicroProfiler.
-  virtual uint32_t BeginEvent(const char* tag);
-
-  // Marks the end of an event associated with event_handle. It is the
-  // responsibility of the caller to ensure than EndEvent is called once and
-  // only once per event_handle.
-  //
-  // If EndEvent is called more than once for the same event_handle, the last
-  // call will be used as the end of event marker.If EndEvent is called 0 times
-  // for a particular event_handle, the duration of that event will be 0 ticks.
-  virtual void EndEvent(uint32_t event_handle);
-
-  // Clears all the events that have been currently profiled.
-  void ClearEvents() { num_events_ = 0; }
-
-  // Returns the sum of the ticks taken across all the events. This number
-  // is only meaningful if all of the events are disjoint (the end time of
-  // event[i] <= start time of event[i+1]).
-  uint32_t GetTotalTicks() const;
-
-  // Prints the profiling information of each of the events in human readable
-  // form.
-  void Log() const;
-
-  // Prints the profiling information of each of the events in CSV (Comma
-  // Separated Value) form.
-  void LogCsv() const;
-
-  // Prints  total ticks for each unique tag in CSV format.
-  // Output will have one row for each unique tag along with the
-  // total ticks summed across all events with that particular tag.
-  void LogTicksPerTagCsv();
-
- private:
-  // Maximum number of events that this class can keep track of. If we call
-  // AddEvent more than kMaxEvents number of times, then the oldest event's
-  // profiling information will be overwritten.
-  static constexpr int kMaxEvents = 1024;
-
-  const char* tags_[kMaxEvents];
-  uint32_t start_ticks_[kMaxEvents];
-  uint32_t end_ticks_[kMaxEvents];
-  int num_events_ = 0;
-
-  struct TicksPerTag {
-    const char* tag;
-    uint32_t ticks;
-  };
-  // In practice, the number of tags will be much lower than the number of
-  // events. But it is theoretically possible that each event to be unique and
-  // hence we allow total_ticks_per_tag to have kMaxEvents entries.
-  TicksPerTag total_ticks_per_tag[kMaxEvents] = {};
-
-  int FindExistingOrNextPosition(const char* tag_name);
-
-  TF_LITE_REMOVE_VIRTUAL_DELETE;
-};
-
-#if defined(TF_LITE_STRIP_ERROR_STRINGS)
-// For release builds, the ScopedMicroProfiler is a noop.
-//
-// This is done because the ScipedProfiler is used as part of the
-// MicroInterpreter and we want to ensure zero overhead for the release builds.
-class ScopedMicroProfiler {
- public:
-  explicit ScopedMicroProfiler(const char* tag, MicroProfiler* profiler) {}
-};
-
-#else
-
-// This class can be used to add events to a MicroProfiler object that span the
-// lifetime of the ScopedMicroProfiler object.
-// Usage example:
-//
-// MicroProfiler profiler();
-// ...
-// {
-//   ScopedMicroProfiler scoped_profiler("custom_tag", profiler);
-//   work_to_profile();
-// }
-class ScopedMicroProfiler {
- public:
-  explicit ScopedMicroProfiler(const char* tag, MicroProfiler* profiler)
-      : profiler_(profiler) {
-    if (profiler_ != nullptr) {
-      event_handle_ = profiler_->BeginEvent(tag);
-    }
-  }
-
-  ~ScopedMicroProfiler() {
-    if (profiler_ != nullptr) {
-      profiler_->EndEvent(event_handle_);
-    }
-  }
-
- private:
-  uint32_t event_handle_ = 0;
-  MicroProfiler* profiler_ = nullptr;
-};
-#endif  // !defined(TF_LITE_STRIP_ERROR_STRINGS)
-
-}  // namespace tflite
-
-#endif  // TENSORFLOW_LITE_MICRO_MICRO_PROFILER_H_

code/components/tflite-lib/tensorflow/lite/micro/recording_micro_allocator.cc

@@ -1,270 +0,0 @@
-/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-#include "tensorflow/lite/micro/recording_micro_allocator.h"
-
-#include "tensorflow/lite/core/api/error_reporter.h"
-#include "tensorflow/lite/kernels/internal/compatibility.h"
-#include "tensorflow/lite/micro/arena_allocator/recording_single_arena_buffer_allocator.h"
-#include "tensorflow/lite/micro/compatibility.h"
-#include "tensorflow/lite/micro/memory_helpers.h"
-#include "tensorflow/lite/micro/memory_planner/greedy_memory_planner.h"
-#include "tensorflow/lite/micro/micro_allocator.h"
-#include "tensorflow/lite/micro/micro_error_reporter.h"
-
-namespace tflite {
-
-size_t RecordingMicroAllocator::GetDefaultTailUsage() {
-  // RecordingMicroAllocator inherits from MicroAllocator and its tail usage is
-  // similar with MicroAllocator with SingleArenaBufferAllocator and
-  // MicroAllocator being replaced.
-  // TODO(b/208703041): a template version of AlignSizeUp to make expression
-  // shorter.
-  return MicroAllocator::GetDefaultTailUsage(
-             /*is_memory_planner_given=*/false) +
-         AlignSizeUp(sizeof(RecordingSingleArenaBufferAllocator),
-                     alignof(RecordingSingleArenaBufferAllocator)) -
-         AlignSizeUp(sizeof(SingleArenaBufferAllocator),
-                     alignof(SingleArenaBufferAllocator)) +
-         AlignSizeUp(sizeof(RecordingMicroAllocator),
-                     alignof(RecordingMicroAllocator)) -
-         AlignSizeUp(sizeof(MicroAllocator), alignof(MicroAllocator));
-}
-
-RecordingMicroAllocator::RecordingMicroAllocator(
-    RecordingSingleArenaBufferAllocator* recording_memory_allocator,
-    MicroMemoryPlanner* memory_planner, ErrorReporter* error_reporter)
-    : MicroAllocator(recording_memory_allocator, memory_planner,
-                     error_reporter),
-      recording_memory_allocator_(recording_memory_allocator) {}
-
-RecordingMicroAllocator* RecordingMicroAllocator::Create(
-    uint8_t* tensor_arena, size_t arena_size, ErrorReporter* error_reporter) {
-  TFLITE_DCHECK(error_reporter != nullptr);
-
-  RecordingSingleArenaBufferAllocator* simple_memory_allocator =
-      RecordingSingleArenaBufferAllocator::Create(error_reporter, tensor_arena,
-                                                  arena_size);
-  TFLITE_DCHECK(simple_memory_allocator != nullptr);
-
-  uint8_t* memory_planner_buffer =
-      simple_memory_allocator->AllocatePersistentBuffer(
-          sizeof(GreedyMemoryPlanner), alignof(GreedyMemoryPlanner));
-  GreedyMemoryPlanner* memory_planner =
-      new (memory_planner_buffer) GreedyMemoryPlanner();
-
-  uint8_t* allocator_buffer = simple_memory_allocator->AllocatePersistentBuffer(
-      sizeof(RecordingMicroAllocator), alignof(RecordingMicroAllocator));
-  RecordingMicroAllocator* allocator =
-      new (allocator_buffer) RecordingMicroAllocator(
-          simple_memory_allocator, memory_planner, error_reporter);
-  return allocator;
-}
-
-RecordedAllocation RecordingMicroAllocator::GetRecordedAllocation(
-    RecordedAllocationType allocation_type) const {
-  switch (allocation_type) {
-    case RecordedAllocationType::kTfLiteEvalTensorData:
-      return recorded_tflite_eval_tensor_data_;
-    case RecordedAllocationType::kPersistentTfLiteTensorData:
-      return recorded_persistent_tflite_tensor_data_;
-    case RecordedAllocationType::kPersistentTfLiteTensorQuantizationData:
-      return recorded_persistent_tflite_tensor_quantization_data_;
-    case RecordedAllocationType::kPersistentBufferData:
-      return recorded_persistent_buffer_data_;
-    case RecordedAllocationType::kTfLiteTensorVariableBufferData:
-      return recorded_tflite_tensor_variable_buffer_data_;
-    case RecordedAllocationType::kNodeAndRegistrationArray:
-      return recorded_node_and_registration_array_data_;
-    case RecordedAllocationType::kOpData:
-      return recorded_op_data_;
-  }
-  TF_LITE_REPORT_ERROR(error_reporter(), "Invalid allocation type supplied: %d",
-                       allocation_type);
-  return RecordedAllocation();
-}
-
-const RecordingSingleArenaBufferAllocator*
-RecordingMicroAllocator::GetSimpleMemoryAllocator() const {
-  return recording_memory_allocator_;
-}
-
-void RecordingMicroAllocator::PrintAllocations() const {
-  TF_LITE_REPORT_ERROR(
-      error_reporter(),
-      "[RecordingMicroAllocator] Arena allocation total %d bytes",
-      recording_memory_allocator_->GetUsedBytes());
-  TF_LITE_REPORT_ERROR(
-      error_reporter(),
-      "[RecordingMicroAllocator] Arena allocation head %d bytes",
-      recording_memory_allocator_->GetNonPersistentUsedBytes());
-  TF_LITE_REPORT_ERROR(
-      error_reporter(),
-      "[RecordingMicroAllocator] Arena allocation tail %d bytes",
-      recording_memory_allocator_->GetPersistentUsedBytes());
-  PrintRecordedAllocation(RecordedAllocationType::kTfLiteEvalTensorData,
-                          "TfLiteEvalTensor data", "allocations");
-  PrintRecordedAllocation(RecordedAllocationType::kPersistentTfLiteTensorData,
-                          "Persistent TfLiteTensor data", "tensors");
-  PrintRecordedAllocation(
-      RecordedAllocationType::kPersistentTfLiteTensorQuantizationData,
-      "Persistent TfLiteTensor quantization data", "allocations");
-  PrintRecordedAllocation(RecordedAllocationType::kPersistentBufferData,
-                          "Persistent buffer data", "allocations");
-  PrintRecordedAllocation(
-      RecordedAllocationType::kTfLiteTensorVariableBufferData,
-      "TfLiteTensor variable buffer data", "allocations");
-  PrintRecordedAllocation(RecordedAllocationType::kNodeAndRegistrationArray,
-                          "NodeAndRegistration struct",
-                          "NodeAndRegistration structs");
-  PrintRecordedAllocation(RecordedAllocationType::kOpData,
-                          "Operator runtime data", "OpData structs");
-}
-
-void* RecordingMicroAllocator::AllocatePersistentBuffer(size_t bytes) {
-  RecordedAllocation allocations = SnapshotAllocationUsage();
-  void* buffer = MicroAllocator::AllocatePersistentBuffer(bytes);
-  RecordAllocationUsage(allocations, recorded_persistent_buffer_data_);
-
-  return buffer;
-}
-
-void RecordingMicroAllocator::PrintRecordedAllocation(
-    RecordedAllocationType allocation_type, const char* allocation_name,
-    const char* allocation_description) const {
-#ifndef TF_LITE_STRIP_ERROR_STRINGS
-  RecordedAllocation allocation = GetRecordedAllocation(allocation_type);
-  if (allocation.used_bytes > 0 || allocation.requested_bytes > 0) {
-    TF_LITE_REPORT_ERROR(
-        error_reporter(),
-        "[RecordingMicroAllocator] '%s' used %d bytes with alignment overhead "
-        "(requested %d bytes for %d %s)",
-        allocation_name, allocation.used_bytes, allocation.requested_bytes,
-        allocation.count, allocation_description);
-  }
-#endif
-}
-
-TfLiteStatus RecordingMicroAllocator::AllocateNodeAndRegistrations(
-    const Model* model, SubgraphAllocations* subgraph_allocations) {
-  RecordedAllocation allocations = SnapshotAllocationUsage();
-
-  TfLiteStatus status =
-      MicroAllocator::AllocateNodeAndRegistrations(model, subgraph_allocations);
-
-  RecordAllocationUsage(allocations,
-                        recorded_node_and_registration_array_data_);
-
-  for (size_t subgraph_idx = 0; subgraph_idx < model->subgraphs()->size();
-       subgraph_idx++) {
-    // The allocation count in SingleArenaBufferAllocator will only be 1. To
-    // provide better logging, decrement by 1 and add in the actual number of
-    // operators used in the graph: The allocation for this recording will
-    // always be 1. This is because the parent class mallocs one large
-    // allocation for the number of nodes in the graph (e.g.
-    // sizeof(NodeAndRegistration) * num_nodes). To prevent extra overhead and
-    // potential for fragmentation, manually adjust the accounting by
-    // decrementing by 1 and adding the actual number of nodes used in the
-    // graph:
-    if (model->subgraphs()->Get(subgraph_idx)->operators()) {
-      recorded_node_and_registration_array_data_.count +=
-          model->subgraphs()->Get(subgraph_idx)->operators()->size() - 1;
-    } else {
-      recorded_node_and_registration_array_data_.count -= 1;
-    }
-  }
-  return status;
-}
-
-TfLiteStatus RecordingMicroAllocator::AllocateTfLiteEvalTensors(
-    const Model* model, SubgraphAllocations* subgraph_allocations) {
-  RecordedAllocation allocations = SnapshotAllocationUsage();
-
-  TfLiteStatus status =
-      MicroAllocator::AllocateTfLiteEvalTensors(model, subgraph_allocations);
-
-  RecordAllocationUsage(allocations, recorded_tflite_eval_tensor_data_);
-
-  for (size_t subgraph_idx = 0; subgraph_idx < model->subgraphs()->size();
-       subgraph_idx++) {
-    // The allocation for this recording will always be 1. This is because the
-    // parent class mallocs one large allocation for the number of tensors in
-    // the graph (e.g. sizeof(TfLiteEvalTensor) * num_tensors). To prevent extra
-    // overhead and potential for fragmentation, manually adjust the accounting
-    // by decrementing by 1 and adding the actual number of tensors used in the
-    // graph:
-    recorded_tflite_eval_tensor_data_.count +=
-        model->subgraphs()->Get(subgraph_idx)->tensors()->size() - 1;
-  }
-  return status;
-}
-
-TfLiteStatus RecordingMicroAllocator::AllocateVariables(
-    const SubGraph* subgraph, TfLiteEvalTensor* eval_tensors) {
-  RecordedAllocation allocations = SnapshotAllocationUsage();
-
-  TfLiteStatus status =
-      MicroAllocator::AllocateVariables(subgraph, eval_tensors);
-
-  RecordAllocationUsage(allocations,
-                        recorded_tflite_tensor_variable_buffer_data_);
-  return status;
-}
-
-TfLiteTensor*
-RecordingMicroAllocator::AllocatePersistentTfLiteTensorInternal() {
-  RecordedAllocation allocations = SnapshotAllocationUsage();
-
-  TfLiteTensor* result =
-      MicroAllocator::AllocatePersistentTfLiteTensorInternal();
-
-  RecordAllocationUsage(allocations, recorded_persistent_tflite_tensor_data_);
-  return result;
-}
-
-TfLiteStatus RecordingMicroAllocator::PopulateTfLiteTensorFromFlatbuffer(
-    const Model* model, TfLiteTensor* tensor, int tensor_index,
-    int subgraph_index, bool allocate_temp) {
-  RecordedAllocation allocations = SnapshotAllocationUsage();
-
-  TfLiteStatus status = MicroAllocator::PopulateTfLiteTensorFromFlatbuffer(
-      model, tensor, tensor_index, subgraph_index, allocate_temp);
-
-  RecordAllocationUsage(allocations,
-                        recorded_persistent_tflite_tensor_quantization_data_);
-  return status;
-}
-
-RecordedAllocation RecordingMicroAllocator::SnapshotAllocationUsage() const {
-  return {/*requested_bytes=*/recording_memory_allocator_->GetRequestedBytes(),
-          /*used_bytes=*/recording_memory_allocator_->GetUsedBytes(),
-          /*count=*/recording_memory_allocator_->GetAllocatedCount()};
-}
-
-void RecordingMicroAllocator::RecordAllocationUsage(
-    const RecordedAllocation& snapshotted_allocation,
-    RecordedAllocation& recorded_allocation) {
-  recorded_allocation.requested_bytes +=
-      recording_memory_allocator_->GetRequestedBytes() -
-      snapshotted_allocation.requested_bytes;
-  recorded_allocation.used_bytes +=
-      recording_memory_allocator_->GetUsedBytes() -
-      snapshotted_allocation.used_bytes;
-  recorded_allocation.count +=
-      recording_memory_allocator_->GetAllocatedCount() -
-      snapshotted_allocation.count;
-}
-
-}  // namespace tflite

code/components/tflite-lib/tensorflow/lite/schema/schema_generated.h

@@ -1,20136 +0,0 @@
-// automatically generated by the FlatBuffers compiler, do not modify
-
-
-#ifndef FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_
-#define FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_
-
-#include "flatbuffers/flatbuffers.h"
-
-// Ensure the included flatbuffers.h is the same version as when this file was
-// generated, otherwise it may not be compatible.
-static_assert(FLATBUFFERS_VERSION_MAJOR == 2 &&
-              FLATBUFFERS_VERSION_MINOR == 0 &&
-              FLATBUFFERS_VERSION_REVISION == 6,
-             "Non-compatible flatbuffers version included");
-
-namespace tflite {
-
-struct CustomQuantization;
-struct CustomQuantizationBuilder;
-struct CustomQuantizationT;
-
-struct QuantizationParameters;
-struct QuantizationParametersBuilder;
-struct QuantizationParametersT;
-
-struct Int32Vector;
-struct Int32VectorBuilder;
-struct Int32VectorT;
-
-struct Uint16Vector;
-struct Uint16VectorBuilder;
-struct Uint16VectorT;
-
-struct Uint8Vector;
-struct Uint8VectorBuilder;
-struct Uint8VectorT;
-
-struct DimensionMetadata;
-struct DimensionMetadataBuilder;
-struct DimensionMetadataT;
-
-struct SparsityParameters;
-struct SparsityParametersBuilder;
-struct SparsityParametersT;
-
-struct VariantSubType;
-struct VariantSubTypeBuilder;
-struct VariantSubTypeT;
-
-struct Tensor;
-struct TensorBuilder;
-struct TensorT;
-
-struct Conv2DOptions;
-struct Conv2DOptionsBuilder;
-struct Conv2DOptionsT;
-
-struct Conv3DOptions;
-struct Conv3DOptionsBuilder;
-struct Conv3DOptionsT;
-
-struct Pool2DOptions;
-struct Pool2DOptionsBuilder;
-struct Pool2DOptionsT;
-
-struct DepthwiseConv2DOptions;
-struct DepthwiseConv2DOptionsBuilder;
-struct DepthwiseConv2DOptionsT;
-
-struct ConcatEmbeddingsOptions;
-struct ConcatEmbeddingsOptionsBuilder;
-struct ConcatEmbeddingsOptionsT;
-
-struct LSHProjectionOptions;
-struct LSHProjectionOptionsBuilder;
-struct LSHProjectionOptionsT;
-
-struct SVDFOptions;
-struct SVDFOptionsBuilder;
-struct SVDFOptionsT;
-
-struct RNNOptions;
-struct RNNOptionsBuilder;
-struct RNNOptionsT;
-
-struct SequenceRNNOptions;
-struct SequenceRNNOptionsBuilder;
-struct SequenceRNNOptionsT;
-
-struct BidirectionalSequenceRNNOptions;
-struct BidirectionalSequenceRNNOptionsBuilder;
-struct BidirectionalSequenceRNNOptionsT;
-
-struct FullyConnectedOptions;
-struct FullyConnectedOptionsBuilder;
-struct FullyConnectedOptionsT;
-
-struct SoftmaxOptions;
-struct SoftmaxOptionsBuilder;
-struct SoftmaxOptionsT;
-
-struct ConcatenationOptions;
-struct ConcatenationOptionsBuilder;
-struct ConcatenationOptionsT;
-
-struct AddOptions;
-struct AddOptionsBuilder;
-struct AddOptionsT;
-
-struct MulOptions;
-struct MulOptionsBuilder;
-struct MulOptionsT;
-
-struct L2NormOptions;
-struct L2NormOptionsBuilder;
-struct L2NormOptionsT;
-
-struct LocalResponseNormalizationOptions;
-struct LocalResponseNormalizationOptionsBuilder;
-struct LocalResponseNormalizationOptionsT;
-
-struct LSTMOptions;
-struct LSTMOptionsBuilder;
-struct LSTMOptionsT;
-
-struct UnidirectionalSequenceLSTMOptions;
-struct UnidirectionalSequenceLSTMOptionsBuilder;
-struct UnidirectionalSequenceLSTMOptionsT;
-
-struct BidirectionalSequenceLSTMOptions;
-struct BidirectionalSequenceLSTMOptionsBuilder;
-struct BidirectionalSequenceLSTMOptionsT;
-
-struct ResizeBilinearOptions;
-struct ResizeBilinearOptionsBuilder;
-struct ResizeBilinearOptionsT;
-
-struct ResizeNearestNeighborOptions;
-struct ResizeNearestNeighborOptionsBuilder;
-struct ResizeNearestNeighborOptionsT;
-
-struct CallOptions;
-struct CallOptionsBuilder;
-struct CallOptionsT;
-
-struct PadOptions;
-struct PadOptionsBuilder;
-struct PadOptionsT;
-
-struct PadV2Options;
-struct PadV2OptionsBuilder;
-struct PadV2OptionsT;
-
-struct ReshapeOptions;
-struct ReshapeOptionsBuilder;
-struct ReshapeOptionsT;
-
-struct SpaceToBatchNDOptions;
-struct SpaceToBatchNDOptionsBuilder;
-struct SpaceToBatchNDOptionsT;
-
-struct BatchToSpaceNDOptions;
-struct BatchToSpaceNDOptionsBuilder;
-struct BatchToSpaceNDOptionsT;
-
-struct SkipGramOptions;
-struct SkipGramOptionsBuilder;
-struct SkipGramOptionsT;
-
-struct SpaceToDepthOptions;
-struct SpaceToDepthOptionsBuilder;
-struct SpaceToDepthOptionsT;
-
-struct DepthToSpaceOptions;
-struct DepthToSpaceOptionsBuilder;
-struct DepthToSpaceOptionsT;
-
-struct SubOptions;
-struct SubOptionsBuilder;
-struct SubOptionsT;
-
-struct DivOptions;
-struct DivOptionsBuilder;
-struct DivOptionsT;
-
-struct TopKV2Options;
-struct TopKV2OptionsBuilder;
-struct TopKV2OptionsT;
-
-struct EmbeddingLookupSparseOptions;
-struct EmbeddingLookupSparseOptionsBuilder;
-struct EmbeddingLookupSparseOptionsT;
-
-struct GatherOptions;
-struct GatherOptionsBuilder;
-struct GatherOptionsT;
-
-struct TransposeOptions;
-struct TransposeOptionsBuilder;
-struct TransposeOptionsT;
-
-struct ExpOptions;
-struct ExpOptionsBuilder;
-struct ExpOptionsT;
-
-struct CosOptions;
-struct CosOptionsBuilder;
-struct CosOptionsT;
-
-struct ReducerOptions;
-struct ReducerOptionsBuilder;
-struct ReducerOptionsT;
-
-struct SqueezeOptions;
-struct SqueezeOptionsBuilder;
-struct SqueezeOptionsT;
-
-struct SplitOptions;
-struct SplitOptionsBuilder;
-struct SplitOptionsT;
-
-struct SplitVOptions;
-struct SplitVOptionsBuilder;
-struct SplitVOptionsT;
-
-struct StridedSliceOptions;
-struct StridedSliceOptionsBuilder;
-struct StridedSliceOptionsT;
-
-struct LogSoftmaxOptions;
-struct LogSoftmaxOptionsBuilder;
-struct LogSoftmaxOptionsT;
-
-struct CastOptions;
-struct CastOptionsBuilder;
-struct CastOptionsT;
-
-struct DequantizeOptions;
-struct DequantizeOptionsBuilder;
-struct DequantizeOptionsT;
-
-struct MaximumMinimumOptions;
-struct MaximumMinimumOptionsBuilder;
-struct MaximumMinimumOptionsT;
-
-struct TileOptions;
-struct TileOptionsBuilder;
-struct TileOptionsT;
-
-struct ArgMaxOptions;
-struct ArgMaxOptionsBuilder;
-struct ArgMaxOptionsT;
-
-struct ArgMinOptions;
-struct ArgMinOptionsBuilder;
-struct ArgMinOptionsT;
-
-struct GreaterOptions;
-struct GreaterOptionsBuilder;
-struct GreaterOptionsT;
-
-struct GreaterEqualOptions;
-struct GreaterEqualOptionsBuilder;
-struct GreaterEqualOptionsT;
-
-struct LessOptions;
-struct LessOptionsBuilder;
-struct LessOptionsT;
-
-struct LessEqualOptions;
-struct LessEqualOptionsBuilder;
-struct LessEqualOptionsT;
-
-struct NegOptions;
-struct NegOptionsBuilder;
-struct NegOptionsT;
-
-struct SelectOptions;
-struct SelectOptionsBuilder;
-struct SelectOptionsT;
-
-struct SliceOptions;
-struct SliceOptionsBuilder;
-struct SliceOptionsT;
-
-struct TransposeConvOptions;
-struct TransposeConvOptionsBuilder;
-struct TransposeConvOptionsT;
-
-struct ExpandDimsOptions;
-struct ExpandDimsOptionsBuilder;
-struct ExpandDimsOptionsT;
-
-struct SparseToDenseOptions;
-struct SparseToDenseOptionsBuilder;
-struct SparseToDenseOptionsT;
-
-struct EqualOptions;
-struct EqualOptionsBuilder;
-struct EqualOptionsT;
-
-struct NotEqualOptions;
-struct NotEqualOptionsBuilder;
-struct NotEqualOptionsT;
-
-struct ShapeOptions;
-struct ShapeOptionsBuilder;
-struct ShapeOptionsT;
-
-struct RankOptions;
-struct RankOptionsBuilder;
-struct RankOptionsT;
-
-struct PowOptions;
-struct PowOptionsBuilder;
-struct PowOptionsT;
-
-struct FakeQuantOptions;
-struct FakeQuantOptionsBuilder;
-struct FakeQuantOptionsT;
-
-struct PackOptions;
-struct PackOptionsBuilder;
-struct PackOptionsT;
-
-struct LogicalOrOptions;
-struct LogicalOrOptionsBuilder;
-struct LogicalOrOptionsT;
-
-struct OneHotOptions;
-struct OneHotOptionsBuilder;
-struct OneHotOptionsT;
-
-struct AbsOptions;
-struct AbsOptionsBuilder;
-struct AbsOptionsT;
-
-struct HardSwishOptions;
-struct HardSwishOptionsBuilder;
-struct HardSwishOptionsT;
-
-struct LogicalAndOptions;
-struct LogicalAndOptionsBuilder;
-struct LogicalAndOptionsT;
-
-struct LogicalNotOptions;
-struct LogicalNotOptionsBuilder;
-struct LogicalNotOptionsT;
-
-struct UnpackOptions;
-struct UnpackOptionsBuilder;
-struct UnpackOptionsT;
-
-struct FloorDivOptions;
-struct FloorDivOptionsBuilder;
-struct FloorDivOptionsT;
-
-struct SquareOptions;
-struct SquareOptionsBuilder;
-struct SquareOptionsT;
-
-struct ZerosLikeOptions;
-struct ZerosLikeOptionsBuilder;
-struct ZerosLikeOptionsT;
-
-struct FillOptions;
-struct FillOptionsBuilder;
-struct FillOptionsT;
-
-struct FloorModOptions;
-struct FloorModOptionsBuilder;
-struct FloorModOptionsT;
-
-struct RangeOptions;
-struct RangeOptionsBuilder;
-struct RangeOptionsT;
-
-struct LeakyReluOptions;
-struct LeakyReluOptionsBuilder;
-struct LeakyReluOptionsT;
-
-struct SquaredDifferenceOptions;
-struct SquaredDifferenceOptionsBuilder;
-struct SquaredDifferenceOptionsT;
-
-struct MirrorPadOptions;
-struct MirrorPadOptionsBuilder;
-struct MirrorPadOptionsT;
-
-struct UniqueOptions;
-struct UniqueOptionsBuilder;
-struct UniqueOptionsT;
-
-struct ReverseV2Options;
-struct ReverseV2OptionsBuilder;
-struct ReverseV2OptionsT;
-
-struct AddNOptions;
-struct AddNOptionsBuilder;
-struct AddNOptionsT;
-
-struct GatherNdOptions;
-struct GatherNdOptionsBuilder;
-struct GatherNdOptionsT;
-
-struct WhereOptions;
-struct WhereOptionsBuilder;
-struct WhereOptionsT;
-
-struct ReverseSequenceOptions;
-struct ReverseSequenceOptionsBuilder;
-struct ReverseSequenceOptionsT;
-
-struct MatrixDiagOptions;
-struct MatrixDiagOptionsBuilder;
-struct MatrixDiagOptionsT;
-
-struct QuantizeOptions;
-struct QuantizeOptionsBuilder;
-struct QuantizeOptionsT;
-
-struct MatrixSetDiagOptions;
-struct MatrixSetDiagOptionsBuilder;
-struct MatrixSetDiagOptionsT;
-
-struct IfOptions;
-struct IfOptionsBuilder;
-struct IfOptionsT;
-
-struct CallOnceOptions;
-struct CallOnceOptionsBuilder;
-struct CallOnceOptionsT;
-
-struct WhileOptions;
-struct WhileOptionsBuilder;
-struct WhileOptionsT;
-
-struct NonMaxSuppressionV4Options;
-struct NonMaxSuppressionV4OptionsBuilder;
-struct NonMaxSuppressionV4OptionsT;
-
-struct NonMaxSuppressionV5Options;
-struct NonMaxSuppressionV5OptionsBuilder;
-struct NonMaxSuppressionV5OptionsT;
-
-struct ScatterNdOptions;
-struct ScatterNdOptionsBuilder;
-struct ScatterNdOptionsT;
-
-struct SelectV2Options;
-struct SelectV2OptionsBuilder;
-struct SelectV2OptionsT;
-
-struct DensifyOptions;
-struct DensifyOptionsBuilder;
-struct DensifyOptionsT;
-
-struct SegmentSumOptions;
-struct SegmentSumOptionsBuilder;
-struct SegmentSumOptionsT;
-
-struct BatchMatMulOptions;
-struct BatchMatMulOptionsBuilder;
-struct BatchMatMulOptionsT;
-
-struct CumsumOptions;
-struct CumsumOptionsBuilder;
-struct CumsumOptionsT;
-
-struct BroadcastToOptions;
-struct BroadcastToOptionsBuilder;
-struct BroadcastToOptionsT;
-
-struct Rfft2dOptions;
-struct Rfft2dOptionsBuilder;
-struct Rfft2dOptionsT;
-
-struct HashtableOptions;
-struct HashtableOptionsBuilder;
-struct HashtableOptionsT;
-
-struct HashtableFindOptions;
-struct HashtableFindOptionsBuilder;
-struct HashtableFindOptionsT;
-
-struct HashtableImportOptions;
-struct HashtableImportOptionsBuilder;
-struct HashtableImportOptionsT;
-
-struct HashtableSizeOptions;
-struct HashtableSizeOptionsBuilder;
-struct HashtableSizeOptionsT;
-
-struct VarHandleOptions;
-struct VarHandleOptionsBuilder;
-struct VarHandleOptionsT;
-
-struct ReadVariableOptions;
-struct ReadVariableOptionsBuilder;
-struct ReadVariableOptionsT;
-
-struct AssignVariableOptions;
-struct AssignVariableOptionsBuilder;
-struct AssignVariableOptionsT;
-
-struct RandomOptions;
-struct RandomOptionsBuilder;
-struct RandomOptionsT;
-
-struct BucketizeOptions;
-struct BucketizeOptionsBuilder;
-struct BucketizeOptionsT;
-
-struct GeluOptions;
-struct GeluOptionsBuilder;
-struct GeluOptionsT;
-
-struct DynamicUpdateSliceOptions;
-struct DynamicUpdateSliceOptionsBuilder;
-struct DynamicUpdateSliceOptionsT;
-
-struct UnsortedSegmentProdOptions;
-struct UnsortedSegmentProdOptionsBuilder;
-struct UnsortedSegmentProdOptionsT;
-
-struct UnsortedSegmentMaxOptions;
-struct UnsortedSegmentMaxOptionsBuilder;
-struct UnsortedSegmentMaxOptionsT;
-
-struct UnsortedSegmentSumOptions;
-struct UnsortedSegmentSumOptionsBuilder;
-struct UnsortedSegmentSumOptionsT;
-
-struct ATan2Options;
-struct ATan2OptionsBuilder;
-struct ATan2OptionsT;
-
-struct UnsortedSegmentMinOptions;
-struct UnsortedSegmentMinOptionsBuilder;
-struct UnsortedSegmentMinOptionsT;
-
-struct SignOptions;
-struct SignOptionsBuilder;
-struct SignOptionsT;
-
-struct OperatorCode;
-struct OperatorCodeBuilder;
-struct OperatorCodeT;
-
-struct Operator;
-struct OperatorBuilder;
-struct OperatorT;
-
-struct SubGraph;
-struct SubGraphBuilder;
-struct SubGraphT;
-
-struct Buffer;
-struct BufferBuilder;
-struct BufferT;
-
-struct Metadata;
-struct MetadataBuilder;
-struct MetadataT;
-
-struct TensorMap;
-struct TensorMapBuilder;
-struct TensorMapT;
-
-struct SignatureDef;
-struct SignatureDefBuilder;
-struct SignatureDefT;
-
-struct Model;
-struct ModelBuilder;
-struct ModelT;
-
-enum TensorType : int8_t {
-  TensorType_FLOAT32 = 0,
-  TensorType_FLOAT16 = 1,
-  TensorType_INT32 = 2,
-  TensorType_UINT8 = 3,
-  TensorType_INT64 = 4,
-  TensorType_STRING = 5,
-  TensorType_BOOL = 6,
-  TensorType_INT16 = 7,
-  TensorType_COMPLEX64 = 8,
-  TensorType_INT8 = 9,
-  TensorType_FLOAT64 = 10,
-  TensorType_COMPLEX128 = 11,
-  TensorType_UINT64 = 12,
-  TensorType_RESOURCE = 13,
-  TensorType_VARIANT = 14,
-  TensorType_UINT32 = 15,
-  TensorType_UINT16 = 16,
-  TensorType_MIN = TensorType_FLOAT32,
-  TensorType_MAX = TensorType_UINT16
-};
-
-inline const TensorType (&EnumValuesTensorType())[17] {
-  static const TensorType values[] = {
-    TensorType_FLOAT32,
-    TensorType_FLOAT16,
-    TensorType_INT32,
-    TensorType_UINT8,
-    TensorType_INT64,
-    TensorType_STRING,
-    TensorType_BOOL,
-    TensorType_INT16,
-    TensorType_COMPLEX64,
-    TensorType_INT8,
-    TensorType_FLOAT64,
-    TensorType_COMPLEX128,
-    TensorType_UINT64,
-    TensorType_RESOURCE,
-    TensorType_VARIANT,
-    TensorType_UINT32,
-    TensorType_UINT16
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesTensorType() {
-  static const char * const names[18] = {
-    "FLOAT32",
-    "FLOAT16",
-    "INT32",
-    "UINT8",
-    "INT64",
-    "STRING",
-    "BOOL",
-    "INT16",
-    "COMPLEX64",
-    "INT8",
-    "FLOAT64",
-    "COMPLEX128",
-    "UINT64",
-    "RESOURCE",
-    "VARIANT",
-    "UINT32",
-    "UINT16",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameTensorType(TensorType e) {
-  if (flatbuffers::IsOutRange(e, TensorType_FLOAT32, TensorType_UINT16)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesTensorType()[index];
-}
-
-enum QuantizationDetails : uint8_t {
-  QuantizationDetails_NONE = 0,
-  QuantizationDetails_CustomQuantization = 1,
-  QuantizationDetails_MIN = QuantizationDetails_NONE,
-  QuantizationDetails_MAX = QuantizationDetails_CustomQuantization
-};
-
-inline const QuantizationDetails (&EnumValuesQuantizationDetails())[2] {
-  static const QuantizationDetails values[] = {
-    QuantizationDetails_NONE,
-    QuantizationDetails_CustomQuantization
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesQuantizationDetails() {
-  static const char * const names[3] = {
-    "NONE",
-    "CustomQuantization",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameQuantizationDetails(QuantizationDetails e) {
-  if (flatbuffers::IsOutRange(e, QuantizationDetails_NONE, QuantizationDetails_CustomQuantization)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesQuantizationDetails()[index];
-}
-
-template<typename T> struct QuantizationDetailsTraits {
-  static const QuantizationDetails enum_value = QuantizationDetails_NONE;
-};
-
-template<> struct QuantizationDetailsTraits<tflite::CustomQuantization> {
-  static const QuantizationDetails enum_value = QuantizationDetails_CustomQuantization;
-};
-
-template<typename T> struct QuantizationDetailsUnionTraits {
-  static const QuantizationDetails enum_value = QuantizationDetails_NONE;
-};
-
-template<> struct QuantizationDetailsUnionTraits<tflite::CustomQuantizationT> {
-  static const QuantizationDetails enum_value = QuantizationDetails_CustomQuantization;
-};
-
-struct QuantizationDetailsUnion {
-  QuantizationDetails type;
-  void *value;
-
-  QuantizationDetailsUnion() : type(QuantizationDetails_NONE), value(nullptr) {}
-  QuantizationDetailsUnion(QuantizationDetailsUnion&& u) FLATBUFFERS_NOEXCEPT :
-    type(QuantizationDetails_NONE), value(nullptr)
-    { std::swap(type, u.type); std::swap(value, u.value); }
-  QuantizationDetailsUnion(const QuantizationDetailsUnion &);
-  QuantizationDetailsUnion &operator=(const QuantizationDetailsUnion &u)
-    { QuantizationDetailsUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; }
-  QuantizationDetailsUnion &operator=(QuantizationDetailsUnion &&u) FLATBUFFERS_NOEXCEPT
-    { std::swap(type, u.type); std::swap(value, u.value); return *this; }
-  ~QuantizationDetailsUnion() { Reset(); }
-
-  void Reset();
-
-  template <typename T>
-  void Set(T&& val) {
-    typedef typename std::remove_reference<T>::type RT;
-    Reset();
-    type = QuantizationDetailsUnionTraits<RT>::enum_value;
-    if (type != QuantizationDetails_NONE) {
-      value = new RT(std::forward<T>(val));
-    }
-  }
-
-  static void *UnPack(const void *obj, QuantizationDetails type, const flatbuffers::resolver_function_t *resolver);
-  flatbuffers::Offset<void> Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const;
-
-  tflite::CustomQuantizationT *AsCustomQuantization() {
-    return type == QuantizationDetails_CustomQuantization ?
-      reinterpret_cast<tflite::CustomQuantizationT *>(value) : nullptr;
-  }
-  const tflite::CustomQuantizationT *AsCustomQuantization() const {
-    return type == QuantizationDetails_CustomQuantization ?
-      reinterpret_cast<const tflite::CustomQuantizationT *>(value) : nullptr;
-  }
-};
-
-bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type);
-bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types);
-
-enum DimensionType : int8_t {
-  DimensionType_DENSE = 0,
-  DimensionType_SPARSE_CSR = 1,
-  DimensionType_MIN = DimensionType_DENSE,
-  DimensionType_MAX = DimensionType_SPARSE_CSR
-};
-
-inline const DimensionType (&EnumValuesDimensionType())[2] {
-  static const DimensionType values[] = {
-    DimensionType_DENSE,
-    DimensionType_SPARSE_CSR
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesDimensionType() {
-  static const char * const names[3] = {
-    "DENSE",
-    "SPARSE_CSR",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameDimensionType(DimensionType e) {
-  if (flatbuffers::IsOutRange(e, DimensionType_DENSE, DimensionType_SPARSE_CSR)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesDimensionType()[index];
-}
-
-enum SparseIndexVector : uint8_t {
-  SparseIndexVector_NONE = 0,
-  SparseIndexVector_Int32Vector = 1,
-  SparseIndexVector_Uint16Vector = 2,
-  SparseIndexVector_Uint8Vector = 3,
-  SparseIndexVector_MIN = SparseIndexVector_NONE,
-  SparseIndexVector_MAX = SparseIndexVector_Uint8Vector
-};
-
-inline const SparseIndexVector (&EnumValuesSparseIndexVector())[4] {
-  static const SparseIndexVector values[] = {
-    SparseIndexVector_NONE,
-    SparseIndexVector_Int32Vector,
-    SparseIndexVector_Uint16Vector,
-    SparseIndexVector_Uint8Vector
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesSparseIndexVector() {
-  static const char * const names[5] = {
-    "NONE",
-    "Int32Vector",
-    "Uint16Vector",
-    "Uint8Vector",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameSparseIndexVector(SparseIndexVector e) {
-  if (flatbuffers::IsOutRange(e, SparseIndexVector_NONE, SparseIndexVector_Uint8Vector)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesSparseIndexVector()[index];
-}
-
-template<typename T> struct SparseIndexVectorTraits {
-  static const SparseIndexVector enum_value = SparseIndexVector_NONE;
-};
-
-template<> struct SparseIndexVectorTraits<tflite::Int32Vector> {
-  static const SparseIndexVector enum_value = SparseIndexVector_Int32Vector;
-};
-
-template<> struct SparseIndexVectorTraits<tflite::Uint16Vector> {
-  static const SparseIndexVector enum_value = SparseIndexVector_Uint16Vector;
-};
-
-template<> struct SparseIndexVectorTraits<tflite::Uint8Vector> {
-  static const SparseIndexVector enum_value = SparseIndexVector_Uint8Vector;
-};
-
-template<typename T> struct SparseIndexVectorUnionTraits {
-  static const SparseIndexVector enum_value = SparseIndexVector_NONE;
-};
-
-template<> struct SparseIndexVectorUnionTraits<tflite::Int32VectorT> {
-  static const SparseIndexVector enum_value = SparseIndexVector_Int32Vector;
-};
-
-template<> struct SparseIndexVectorUnionTraits<tflite::Uint16VectorT> {
-  static const SparseIndexVector enum_value = SparseIndexVector_Uint16Vector;
-};
-
-template<> struct SparseIndexVectorUnionTraits<tflite::Uint8VectorT> {
-  static const SparseIndexVector enum_value = SparseIndexVector_Uint8Vector;
-};
-
-struct SparseIndexVectorUnion {
-  SparseIndexVector type;
-  void *value;
-
-  SparseIndexVectorUnion() : type(SparseIndexVector_NONE), value(nullptr) {}
-  SparseIndexVectorUnion(SparseIndexVectorUnion&& u) FLATBUFFERS_NOEXCEPT :
-    type(SparseIndexVector_NONE), value(nullptr)
-    { std::swap(type, u.type); std::swap(value, u.value); }
-  SparseIndexVectorUnion(const SparseIndexVectorUnion &);
-  SparseIndexVectorUnion &operator=(const SparseIndexVectorUnion &u)
-    { SparseIndexVectorUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; }
-  SparseIndexVectorUnion &operator=(SparseIndexVectorUnion &&u) FLATBUFFERS_NOEXCEPT
-    { std::swap(type, u.type); std::swap(value, u.value); return *this; }
-  ~SparseIndexVectorUnion() { Reset(); }
-
-  void Reset();
-
-  template <typename T>
-  void Set(T&& val) {
-    typedef typename std::remove_reference<T>::type RT;
-    Reset();
-    type = SparseIndexVectorUnionTraits<RT>::enum_value;
-    if (type != SparseIndexVector_NONE) {
-      value = new RT(std::forward<T>(val));
-    }
-  }
-
-  static void *UnPack(const void *obj, SparseIndexVector type, const flatbuffers::resolver_function_t *resolver);
-  flatbuffers::Offset<void> Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const;
-
-  tflite::Int32VectorT *AsInt32Vector() {
-    return type == SparseIndexVector_Int32Vector ?
-      reinterpret_cast<tflite::Int32VectorT *>(value) : nullptr;
-  }
-  const tflite::Int32VectorT *AsInt32Vector() const {
-    return type == SparseIndexVector_Int32Vector ?
-      reinterpret_cast<const tflite::Int32VectorT *>(value) : nullptr;
-  }
-  tflite::Uint16VectorT *AsUint16Vector() {
-    return type == SparseIndexVector_Uint16Vector ?
-      reinterpret_cast<tflite::Uint16VectorT *>(value) : nullptr;
-  }
-  const tflite::Uint16VectorT *AsUint16Vector() const {
-    return type == SparseIndexVector_Uint16Vector ?
-      reinterpret_cast<const tflite::Uint16VectorT *>(value) : nullptr;
-  }
-  tflite::Uint8VectorT *AsUint8Vector() {
-    return type == SparseIndexVector_Uint8Vector ?
-      reinterpret_cast<tflite::Uint8VectorT *>(value) : nullptr;
-  }
-  const tflite::Uint8VectorT *AsUint8Vector() const {
-    return type == SparseIndexVector_Uint8Vector ?
-      reinterpret_cast<const tflite::Uint8VectorT *>(value) : nullptr;
-  }
-};
-
-bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type);
-bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types);
-
-enum BuiltinOperator : int32_t {
-  BuiltinOperator_ADD = 0,
-  BuiltinOperator_AVERAGE_POOL_2D = 1,
-  BuiltinOperator_CONCATENATION = 2,
-  BuiltinOperator_CONV_2D = 3,
-  BuiltinOperator_DEPTHWISE_CONV_2D = 4,
-  BuiltinOperator_DEPTH_TO_SPACE = 5,
-  BuiltinOperator_DEQUANTIZE = 6,
-  BuiltinOperator_EMBEDDING_LOOKUP = 7,
-  BuiltinOperator_FLOOR = 8,
-  BuiltinOperator_FULLY_CONNECTED = 9,
-  BuiltinOperator_HASHTABLE_LOOKUP = 10,
-  BuiltinOperator_L2_NORMALIZATION = 11,
-  BuiltinOperator_L2_POOL_2D = 12,
-  BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION = 13,
-  BuiltinOperator_LOGISTIC = 14,
-  BuiltinOperator_LSH_PROJECTION = 15,
-  BuiltinOperator_LSTM = 16,
-  BuiltinOperator_MAX_POOL_2D = 17,
-  BuiltinOperator_MUL = 18,
-  BuiltinOperator_RELU = 19,
-  BuiltinOperator_RELU_N1_TO_1 = 20,
-  BuiltinOperator_RELU6 = 21,
-  BuiltinOperator_RESHAPE = 22,
-  BuiltinOperator_RESIZE_BILINEAR = 23,
-  BuiltinOperator_RNN = 24,
-  BuiltinOperator_SOFTMAX = 25,
-  BuiltinOperator_SPACE_TO_DEPTH = 26,
-  BuiltinOperator_SVDF = 27,
-  BuiltinOperator_TANH = 28,
-  BuiltinOperator_CONCAT_EMBEDDINGS = 29,
-  BuiltinOperator_SKIP_GRAM = 30,
-  BuiltinOperator_CALL = 31,
-  BuiltinOperator_CUSTOM = 32,
-  BuiltinOperator_EMBEDDING_LOOKUP_SPARSE = 33,
-  BuiltinOperator_PAD = 34,
-  BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN = 35,
-  BuiltinOperator_GATHER = 36,
-  BuiltinOperator_BATCH_TO_SPACE_ND = 37,
-  BuiltinOperator_SPACE_TO_BATCH_ND = 38,
-  BuiltinOperator_TRANSPOSE = 39,
-  BuiltinOperator_MEAN = 40,
-  BuiltinOperator_SUB = 41,
-  BuiltinOperator_DIV = 42,
-  BuiltinOperator_SQUEEZE = 43,
-  BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM = 44,
-  BuiltinOperator_STRIDED_SLICE = 45,
-  BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN = 46,
-  BuiltinOperator_EXP = 47,
-  BuiltinOperator_TOPK_V2 = 48,
-  BuiltinOperator_SPLIT = 49,
-  BuiltinOperator_LOG_SOFTMAX = 50,
-  BuiltinOperator_DELEGATE = 51,
-  BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM = 52,
-  BuiltinOperator_CAST = 53,
-  BuiltinOperator_PRELU = 54,
-  BuiltinOperator_MAXIMUM = 55,
-  BuiltinOperator_ARG_MAX = 56,
-  BuiltinOperator_MINIMUM = 57,
-  BuiltinOperator_LESS = 58,
-  BuiltinOperator_NEG = 59,
-  BuiltinOperator_PADV2 = 60,
-  BuiltinOperator_GREATER = 61,
-  BuiltinOperator_GREATER_EQUAL = 62,
-  BuiltinOperator_LESS_EQUAL = 63,
-  BuiltinOperator_SELECT = 64,
-  BuiltinOperator_SLICE = 65,
-  BuiltinOperator_SIN = 66,
-  BuiltinOperator_TRANSPOSE_CONV = 67,
-  BuiltinOperator_SPARSE_TO_DENSE = 68,
-  BuiltinOperator_TILE = 69,
-  BuiltinOperator_EXPAND_DIMS = 70,
-  BuiltinOperator_EQUAL = 71,
-  BuiltinOperator_NOT_EQUAL = 72,
-  BuiltinOperator_LOG = 73,
-  BuiltinOperator_SUM = 74,
-  BuiltinOperator_SQRT = 75,
-  BuiltinOperator_RSQRT = 76,
-  BuiltinOperator_SHAPE = 77,
-  BuiltinOperator_POW = 78,
-  BuiltinOperator_ARG_MIN = 79,
-  BuiltinOperator_FAKE_QUANT = 80,
-  BuiltinOperator_REDUCE_PROD = 81,
-  BuiltinOperator_REDUCE_MAX = 82,
-  BuiltinOperator_PACK = 83,
-  BuiltinOperator_LOGICAL_OR = 84,
-  BuiltinOperator_ONE_HOT = 85,
-  BuiltinOperator_LOGICAL_AND = 86,
-  BuiltinOperator_LOGICAL_NOT = 87,
-  BuiltinOperator_UNPACK = 88,
-  BuiltinOperator_REDUCE_MIN = 89,
-  BuiltinOperator_FLOOR_DIV = 90,
-  BuiltinOperator_REDUCE_ANY = 91,
-  BuiltinOperator_SQUARE = 92,
-  BuiltinOperator_ZEROS_LIKE = 93,
-  BuiltinOperator_FILL = 94,
-  BuiltinOperator_FLOOR_MOD = 95,
-  BuiltinOperator_RANGE = 96,
-  BuiltinOperator_RESIZE_NEAREST_NEIGHBOR = 97,
-  BuiltinOperator_LEAKY_RELU = 98,
-  BuiltinOperator_SQUARED_DIFFERENCE = 99,
-  BuiltinOperator_MIRROR_PAD = 100,
-  BuiltinOperator_ABS = 101,
-  BuiltinOperator_SPLIT_V = 102,
-  BuiltinOperator_UNIQUE = 103,
-  BuiltinOperator_CEIL = 104,
-  BuiltinOperator_REVERSE_V2 = 105,
-  BuiltinOperator_ADD_N = 106,
-  BuiltinOperator_GATHER_ND = 107,
-  BuiltinOperator_COS = 108,
-  BuiltinOperator_WHERE = 109,
-  BuiltinOperator_RANK = 110,
-  BuiltinOperator_ELU = 111,
-  BuiltinOperator_REVERSE_SEQUENCE = 112,
-  BuiltinOperator_MATRIX_DIAG = 113,
-  BuiltinOperator_QUANTIZE = 114,
-  BuiltinOperator_MATRIX_SET_DIAG = 115,
-  BuiltinOperator_ROUND = 116,
-  BuiltinOperator_HARD_SWISH = 117,
-  BuiltinOperator_IF = 118,
-  BuiltinOperator_WHILE = 119,
-  BuiltinOperator_NON_MAX_SUPPRESSION_V4 = 120,
-  BuiltinOperator_NON_MAX_SUPPRESSION_V5 = 121,
-  BuiltinOperator_SCATTER_ND = 122,
-  BuiltinOperator_SELECT_V2 = 123,
-  BuiltinOperator_DENSIFY = 124,
-  BuiltinOperator_SEGMENT_SUM = 125,
-  BuiltinOperator_BATCH_MATMUL = 126,
-  BuiltinOperator_PLACEHOLDER_FOR_GREATER_OP_CODES = 127,
-  BuiltinOperator_CUMSUM = 128,
-  BuiltinOperator_CALL_ONCE = 129,
-  BuiltinOperator_BROADCAST_TO = 130,
-  BuiltinOperator_RFFT2D = 131,
-  BuiltinOperator_CONV_3D = 132,
-  BuiltinOperator_IMAG = 133,
-  BuiltinOperator_REAL = 134,
-  BuiltinOperator_COMPLEX_ABS = 135,
-  BuiltinOperator_HASHTABLE = 136,
-  BuiltinOperator_HASHTABLE_FIND = 137,
-  BuiltinOperator_HASHTABLE_IMPORT = 138,
-  BuiltinOperator_HASHTABLE_SIZE = 139,
-  BuiltinOperator_REDUCE_ALL = 140,
-  BuiltinOperator_CONV_3D_TRANSPOSE = 141,
-  BuiltinOperator_VAR_HANDLE = 142,
-  BuiltinOperator_READ_VARIABLE = 143,
-  BuiltinOperator_ASSIGN_VARIABLE = 144,
-  BuiltinOperator_BROADCAST_ARGS = 145,
-  BuiltinOperator_RANDOM_STANDARD_NORMAL = 146,
-  BuiltinOperator_BUCKETIZE = 147,
-  BuiltinOperator_RANDOM_UNIFORM = 148,
-  BuiltinOperator_MULTINOMIAL = 149,
-  BuiltinOperator_GELU = 150,
-  BuiltinOperator_DYNAMIC_UPDATE_SLICE = 151,
-  BuiltinOperator_RELU_0_TO_1 = 152,
-  BuiltinOperator_UNSORTED_SEGMENT_PROD = 153,
-  BuiltinOperator_UNSORTED_SEGMENT_MAX = 154,
-  BuiltinOperator_UNSORTED_SEGMENT_SUM = 155,
-  BuiltinOperator_ATAN2 = 156,
-  BuiltinOperator_UNSORTED_SEGMENT_MIN = 157,
-  BuiltinOperator_SIGN = 158,
-  BuiltinOperator_MIN = BuiltinOperator_ADD,
-  BuiltinOperator_MAX = BuiltinOperator_SIGN
-};
-
-inline const BuiltinOperator (&EnumValuesBuiltinOperator())[159] {
-  static const BuiltinOperator values[] = {
-    BuiltinOperator_ADD,
-    BuiltinOperator_AVERAGE_POOL_2D,
-    BuiltinOperator_CONCATENATION,
-    BuiltinOperator_CONV_2D,
-    BuiltinOperator_DEPTHWISE_CONV_2D,
-    BuiltinOperator_DEPTH_TO_SPACE,
-    BuiltinOperator_DEQUANTIZE,
-    BuiltinOperator_EMBEDDING_LOOKUP,
-    BuiltinOperator_FLOOR,
-    BuiltinOperator_FULLY_CONNECTED,
-    BuiltinOperator_HASHTABLE_LOOKUP,
-    BuiltinOperator_L2_NORMALIZATION,
-    BuiltinOperator_L2_POOL_2D,
-    BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION,
-    BuiltinOperator_LOGISTIC,
-    BuiltinOperator_LSH_PROJECTION,
-    BuiltinOperator_LSTM,
-    BuiltinOperator_MAX_POOL_2D,
-    BuiltinOperator_MUL,
-    BuiltinOperator_RELU,
-    BuiltinOperator_RELU_N1_TO_1,
-    BuiltinOperator_RELU6,
-    BuiltinOperator_RESHAPE,
-    BuiltinOperator_RESIZE_BILINEAR,
-    BuiltinOperator_RNN,
-    BuiltinOperator_SOFTMAX,
-    BuiltinOperator_SPACE_TO_DEPTH,
-    BuiltinOperator_SVDF,
-    BuiltinOperator_TANH,
-    BuiltinOperator_CONCAT_EMBEDDINGS,
-    BuiltinOperator_SKIP_GRAM,
-    BuiltinOperator_CALL,
-    BuiltinOperator_CUSTOM,
-    BuiltinOperator_EMBEDDING_LOOKUP_SPARSE,
-    BuiltinOperator_PAD,
-    BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_RNN,
-    BuiltinOperator_GATHER,
-    BuiltinOperator_BATCH_TO_SPACE_ND,
-    BuiltinOperator_SPACE_TO_BATCH_ND,
-    BuiltinOperator_TRANSPOSE,
-    BuiltinOperator_MEAN,
-    BuiltinOperator_SUB,
-    BuiltinOperator_DIV,
-    BuiltinOperator_SQUEEZE,
-    BuiltinOperator_UNIDIRECTIONAL_SEQUENCE_LSTM,
-    BuiltinOperator_STRIDED_SLICE,
-    BuiltinOperator_BIDIRECTIONAL_SEQUENCE_RNN,
-    BuiltinOperator_EXP,
-    BuiltinOperator_TOPK_V2,
-    BuiltinOperator_SPLIT,
-    BuiltinOperator_LOG_SOFTMAX,
-    BuiltinOperator_DELEGATE,
-    BuiltinOperator_BIDIRECTIONAL_SEQUENCE_LSTM,
-    BuiltinOperator_CAST,
-    BuiltinOperator_PRELU,
-    BuiltinOperator_MAXIMUM,
-    BuiltinOperator_ARG_MAX,
-    BuiltinOperator_MINIMUM,
-    BuiltinOperator_LESS,
-    BuiltinOperator_NEG,
-    BuiltinOperator_PADV2,
-    BuiltinOperator_GREATER,
-    BuiltinOperator_GREATER_EQUAL,
-    BuiltinOperator_LESS_EQUAL,
-    BuiltinOperator_SELECT,
-    BuiltinOperator_SLICE,
-    BuiltinOperator_SIN,
-    BuiltinOperator_TRANSPOSE_CONV,
-    BuiltinOperator_SPARSE_TO_DENSE,
-    BuiltinOperator_TILE,
-    BuiltinOperator_EXPAND_DIMS,
-    BuiltinOperator_EQUAL,
-    BuiltinOperator_NOT_EQUAL,
-    BuiltinOperator_LOG,
-    BuiltinOperator_SUM,
-    BuiltinOperator_SQRT,
-    BuiltinOperator_RSQRT,
-    BuiltinOperator_SHAPE,
-    BuiltinOperator_POW,
-    BuiltinOperator_ARG_MIN,
-    BuiltinOperator_FAKE_QUANT,
-    BuiltinOperator_REDUCE_PROD,
-    BuiltinOperator_REDUCE_MAX,
-    BuiltinOperator_PACK,
-    BuiltinOperator_LOGICAL_OR,
-    BuiltinOperator_ONE_HOT,
-    BuiltinOperator_LOGICAL_AND,
-    BuiltinOperator_LOGICAL_NOT,
-    BuiltinOperator_UNPACK,
-    BuiltinOperator_REDUCE_MIN,
-    BuiltinOperator_FLOOR_DIV,
-    BuiltinOperator_REDUCE_ANY,
-    BuiltinOperator_SQUARE,
-    BuiltinOperator_ZEROS_LIKE,
-    BuiltinOperator_FILL,
-    BuiltinOperator_FLOOR_MOD,
-    BuiltinOperator_RANGE,
-    BuiltinOperator_RESIZE_NEAREST_NEIGHBOR,
-    BuiltinOperator_LEAKY_RELU,
-    BuiltinOperator_SQUARED_DIFFERENCE,
-    BuiltinOperator_MIRROR_PAD,
-    BuiltinOperator_ABS,
-    BuiltinOperator_SPLIT_V,
-    BuiltinOperator_UNIQUE,
-    BuiltinOperator_CEIL,
-    BuiltinOperator_REVERSE_V2,
-    BuiltinOperator_ADD_N,
-    BuiltinOperator_GATHER_ND,
-    BuiltinOperator_COS,
-    BuiltinOperator_WHERE,
-    BuiltinOperator_RANK,
-    BuiltinOperator_ELU,
-    BuiltinOperator_REVERSE_SEQUENCE,
-    BuiltinOperator_MATRIX_DIAG,
-    BuiltinOperator_QUANTIZE,
-    BuiltinOperator_MATRIX_SET_DIAG,
-    BuiltinOperator_ROUND,
-    BuiltinOperator_HARD_SWISH,
-    BuiltinOperator_IF,
-    BuiltinOperator_WHILE,
-    BuiltinOperator_NON_MAX_SUPPRESSION_V4,
-    BuiltinOperator_NON_MAX_SUPPRESSION_V5,
-    BuiltinOperator_SCATTER_ND,
-    BuiltinOperator_SELECT_V2,
-    BuiltinOperator_DENSIFY,
-    BuiltinOperator_SEGMENT_SUM,
-    BuiltinOperator_BATCH_MATMUL,
-    BuiltinOperator_PLACEHOLDER_FOR_GREATER_OP_CODES,
-    BuiltinOperator_CUMSUM,
-    BuiltinOperator_CALL_ONCE,
-    BuiltinOperator_BROADCAST_TO,
-    BuiltinOperator_RFFT2D,
-    BuiltinOperator_CONV_3D,
-    BuiltinOperator_IMAG,
-    BuiltinOperator_REAL,
-    BuiltinOperator_COMPLEX_ABS,
-    BuiltinOperator_HASHTABLE,
-    BuiltinOperator_HASHTABLE_FIND,
-    BuiltinOperator_HASHTABLE_IMPORT,
-    BuiltinOperator_HASHTABLE_SIZE,
-    BuiltinOperator_REDUCE_ALL,
-    BuiltinOperator_CONV_3D_TRANSPOSE,
-    BuiltinOperator_VAR_HANDLE,
-    BuiltinOperator_READ_VARIABLE,
-    BuiltinOperator_ASSIGN_VARIABLE,
-    BuiltinOperator_BROADCAST_ARGS,
-    BuiltinOperator_RANDOM_STANDARD_NORMAL,
-    BuiltinOperator_BUCKETIZE,
-    BuiltinOperator_RANDOM_UNIFORM,
-    BuiltinOperator_MULTINOMIAL,
-    BuiltinOperator_GELU,
-    BuiltinOperator_DYNAMIC_UPDATE_SLICE,
-    BuiltinOperator_RELU_0_TO_1,
-    BuiltinOperator_UNSORTED_SEGMENT_PROD,
-    BuiltinOperator_UNSORTED_SEGMENT_MAX,
-    BuiltinOperator_UNSORTED_SEGMENT_SUM,
-    BuiltinOperator_ATAN2,
-    BuiltinOperator_UNSORTED_SEGMENT_MIN,
-    BuiltinOperator_SIGN
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesBuiltinOperator() {
-  static const char * const names[160] = {
-    "ADD",
-    "AVERAGE_POOL_2D",
-    "CONCATENATION",
-    "CONV_2D",
-    "DEPTHWISE_CONV_2D",
-    "DEPTH_TO_SPACE",
-    "DEQUANTIZE",
-    "EMBEDDING_LOOKUP",
-    "FLOOR",
-    "FULLY_CONNECTED",
-    "HASHTABLE_LOOKUP",
-    "L2_NORMALIZATION",
-    "L2_POOL_2D",
-    "LOCAL_RESPONSE_NORMALIZATION",
-    "LOGISTIC",
-    "LSH_PROJECTION",
-    "LSTM",
-    "MAX_POOL_2D",
-    "MUL",
-    "RELU",
-    "RELU_N1_TO_1",
-    "RELU6",
-    "RESHAPE",
-    "RESIZE_BILINEAR",
-    "RNN",
-    "SOFTMAX",
-    "SPACE_TO_DEPTH",
-    "SVDF",
-    "TANH",
-    "CONCAT_EMBEDDINGS",
-    "SKIP_GRAM",
-    "CALL",
-    "CUSTOM",
-    "EMBEDDING_LOOKUP_SPARSE",
-    "PAD",
-    "UNIDIRECTIONAL_SEQUENCE_RNN",
-    "GATHER",
-    "BATCH_TO_SPACE_ND",
-    "SPACE_TO_BATCH_ND",
-    "TRANSPOSE",
-    "MEAN",
-    "SUB",
-    "DIV",
-    "SQUEEZE",
-    "UNIDIRECTIONAL_SEQUENCE_LSTM",
-    "STRIDED_SLICE",
-    "BIDIRECTIONAL_SEQUENCE_RNN",
-    "EXP",
-    "TOPK_V2",
-    "SPLIT",
-    "LOG_SOFTMAX",
-    "DELEGATE",
-    "BIDIRECTIONAL_SEQUENCE_LSTM",
-    "CAST",
-    "PRELU",
-    "MAXIMUM",
-    "ARG_MAX",
-    "MINIMUM",
-    "LESS",
-    "NEG",
-    "PADV2",
-    "GREATER",
-    "GREATER_EQUAL",
-    "LESS_EQUAL",
-    "SELECT",
-    "SLICE",
-    "SIN",
-    "TRANSPOSE_CONV",
-    "SPARSE_TO_DENSE",
-    "TILE",
-    "EXPAND_DIMS",
-    "EQUAL",
-    "NOT_EQUAL",
-    "LOG",
-    "SUM",
-    "SQRT",
-    "RSQRT",
-    "SHAPE",
-    "POW",
-    "ARG_MIN",
-    "FAKE_QUANT",
-    "REDUCE_PROD",
-    "REDUCE_MAX",
-    "PACK",
-    "LOGICAL_OR",
-    "ONE_HOT",
-    "LOGICAL_AND",
-    "LOGICAL_NOT",
-    "UNPACK",
-    "REDUCE_MIN",
-    "FLOOR_DIV",
-    "REDUCE_ANY",
-    "SQUARE",
-    "ZEROS_LIKE",
-    "FILL",
-    "FLOOR_MOD",
-    "RANGE",
-    "RESIZE_NEAREST_NEIGHBOR",
-    "LEAKY_RELU",
-    "SQUARED_DIFFERENCE",
-    "MIRROR_PAD",
-    "ABS",
-    "SPLIT_V",
-    "UNIQUE",
-    "CEIL",
-    "REVERSE_V2",
-    "ADD_N",
-    "GATHER_ND",
-    "COS",
-    "WHERE",
-    "RANK",
-    "ELU",
-    "REVERSE_SEQUENCE",
-    "MATRIX_DIAG",
-    "QUANTIZE",
-    "MATRIX_SET_DIAG",
-    "ROUND",
-    "HARD_SWISH",
-    "IF",
-    "WHILE",
-    "NON_MAX_SUPPRESSION_V4",
-    "NON_MAX_SUPPRESSION_V5",
-    "SCATTER_ND",
-    "SELECT_V2",
-    "DENSIFY",
-    "SEGMENT_SUM",
-    "BATCH_MATMUL",
-    "PLACEHOLDER_FOR_GREATER_OP_CODES",
-    "CUMSUM",
-    "CALL_ONCE",
-    "BROADCAST_TO",
-    "RFFT2D",
-    "CONV_3D",
-    "IMAG",
-    "REAL",
-    "COMPLEX_ABS",
-    "HASHTABLE",
-    "HASHTABLE_FIND",
-    "HASHTABLE_IMPORT",
-    "HASHTABLE_SIZE",
-    "REDUCE_ALL",
-    "CONV_3D_TRANSPOSE",
-    "VAR_HANDLE",
-    "READ_VARIABLE",
-    "ASSIGN_VARIABLE",
-    "BROADCAST_ARGS",
-    "RANDOM_STANDARD_NORMAL",
-    "BUCKETIZE",
-    "RANDOM_UNIFORM",
-    "MULTINOMIAL",
-    "GELU",
-    "DYNAMIC_UPDATE_SLICE",
-    "RELU_0_TO_1",
-    "UNSORTED_SEGMENT_PROD",
-    "UNSORTED_SEGMENT_MAX",
-    "UNSORTED_SEGMENT_SUM",
-    "ATAN2",
-    "UNSORTED_SEGMENT_MIN",
-    "SIGN",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameBuiltinOperator(BuiltinOperator e) {
-  if (flatbuffers::IsOutRange(e, BuiltinOperator_ADD, BuiltinOperator_SIGN)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesBuiltinOperator()[index];
-}
-
-enum BuiltinOptions : uint8_t {
-  BuiltinOptions_NONE = 0,
-  BuiltinOptions_Conv2DOptions = 1,
-  BuiltinOptions_DepthwiseConv2DOptions = 2,
-  BuiltinOptions_ConcatEmbeddingsOptions = 3,
-  BuiltinOptions_LSHProjectionOptions = 4,
-  BuiltinOptions_Pool2DOptions = 5,
-  BuiltinOptions_SVDFOptions = 6,
-  BuiltinOptions_RNNOptions = 7,
-  BuiltinOptions_FullyConnectedOptions = 8,
-  BuiltinOptions_SoftmaxOptions = 9,
-  BuiltinOptions_ConcatenationOptions = 10,
-  BuiltinOptions_AddOptions = 11,
-  BuiltinOptions_L2NormOptions = 12,
-  BuiltinOptions_LocalResponseNormalizationOptions = 13,
-  BuiltinOptions_LSTMOptions = 14,
-  BuiltinOptions_ResizeBilinearOptions = 15,
-  BuiltinOptions_CallOptions = 16,
-  BuiltinOptions_ReshapeOptions = 17,
-  BuiltinOptions_SkipGramOptions = 18,
-  BuiltinOptions_SpaceToDepthOptions = 19,
-  BuiltinOptions_EmbeddingLookupSparseOptions = 20,
-  BuiltinOptions_MulOptions = 21,
-  BuiltinOptions_PadOptions = 22,
-  BuiltinOptions_GatherOptions = 23,
-  BuiltinOptions_BatchToSpaceNDOptions = 24,
-  BuiltinOptions_SpaceToBatchNDOptions = 25,
-  BuiltinOptions_TransposeOptions = 26,
-  BuiltinOptions_ReducerOptions = 27,
-  BuiltinOptions_SubOptions = 28,
-  BuiltinOptions_DivOptions = 29,
-  BuiltinOptions_SqueezeOptions = 30,
-  BuiltinOptions_SequenceRNNOptions = 31,
-  BuiltinOptions_StridedSliceOptions = 32,
-  BuiltinOptions_ExpOptions = 33,
-  BuiltinOptions_TopKV2Options = 34,
-  BuiltinOptions_SplitOptions = 35,
-  BuiltinOptions_LogSoftmaxOptions = 36,
-  BuiltinOptions_CastOptions = 37,
-  BuiltinOptions_DequantizeOptions = 38,
-  BuiltinOptions_MaximumMinimumOptions = 39,
-  BuiltinOptions_ArgMaxOptions = 40,
-  BuiltinOptions_LessOptions = 41,
-  BuiltinOptions_NegOptions = 42,
-  BuiltinOptions_PadV2Options = 43,
-  BuiltinOptions_GreaterOptions = 44,
-  BuiltinOptions_GreaterEqualOptions = 45,
-  BuiltinOptions_LessEqualOptions = 46,
-  BuiltinOptions_SelectOptions = 47,
-  BuiltinOptions_SliceOptions = 48,
-  BuiltinOptions_TransposeConvOptions = 49,
-  BuiltinOptions_SparseToDenseOptions = 50,
-  BuiltinOptions_TileOptions = 51,
-  BuiltinOptions_ExpandDimsOptions = 52,
-  BuiltinOptions_EqualOptions = 53,
-  BuiltinOptions_NotEqualOptions = 54,
-  BuiltinOptions_ShapeOptions = 55,
-  BuiltinOptions_PowOptions = 56,
-  BuiltinOptions_ArgMinOptions = 57,
-  BuiltinOptions_FakeQuantOptions = 58,
-  BuiltinOptions_PackOptions = 59,
-  BuiltinOptions_LogicalOrOptions = 60,
-  BuiltinOptions_OneHotOptions = 61,
-  BuiltinOptions_LogicalAndOptions = 62,
-  BuiltinOptions_LogicalNotOptions = 63,
-  BuiltinOptions_UnpackOptions = 64,
-  BuiltinOptions_FloorDivOptions = 65,
-  BuiltinOptions_SquareOptions = 66,
-  BuiltinOptions_ZerosLikeOptions = 67,
-  BuiltinOptions_FillOptions = 68,
-  BuiltinOptions_BidirectionalSequenceLSTMOptions = 69,
-  BuiltinOptions_BidirectionalSequenceRNNOptions = 70,
-  BuiltinOptions_UnidirectionalSequenceLSTMOptions = 71,
-  BuiltinOptions_FloorModOptions = 72,
-  BuiltinOptions_RangeOptions = 73,
-  BuiltinOptions_ResizeNearestNeighborOptions = 74,
-  BuiltinOptions_LeakyReluOptions = 75,
-  BuiltinOptions_SquaredDifferenceOptions = 76,
-  BuiltinOptions_MirrorPadOptions = 77,
-  BuiltinOptions_AbsOptions = 78,
-  BuiltinOptions_SplitVOptions = 79,
-  BuiltinOptions_UniqueOptions = 80,
-  BuiltinOptions_ReverseV2Options = 81,
-  BuiltinOptions_AddNOptions = 82,
-  BuiltinOptions_GatherNdOptions = 83,
-  BuiltinOptions_CosOptions = 84,
-  BuiltinOptions_WhereOptions = 85,
-  BuiltinOptions_RankOptions = 86,
-  BuiltinOptions_ReverseSequenceOptions = 87,
-  BuiltinOptions_MatrixDiagOptions = 88,
-  BuiltinOptions_QuantizeOptions = 89,
-  BuiltinOptions_MatrixSetDiagOptions = 90,
-  BuiltinOptions_HardSwishOptions = 91,
-  BuiltinOptions_IfOptions = 92,
-  BuiltinOptions_WhileOptions = 93,
-  BuiltinOptions_DepthToSpaceOptions = 94,
-  BuiltinOptions_NonMaxSuppressionV4Options = 95,
-  BuiltinOptions_NonMaxSuppressionV5Options = 96,
-  BuiltinOptions_ScatterNdOptions = 97,
-  BuiltinOptions_SelectV2Options = 98,
-  BuiltinOptions_DensifyOptions = 99,
-  BuiltinOptions_SegmentSumOptions = 100,
-  BuiltinOptions_BatchMatMulOptions = 101,
-  BuiltinOptions_CumsumOptions = 102,
-  BuiltinOptions_CallOnceOptions = 103,
-  BuiltinOptions_BroadcastToOptions = 104,
-  BuiltinOptions_Rfft2dOptions = 105,
-  BuiltinOptions_Conv3DOptions = 106,
-  BuiltinOptions_HashtableOptions = 107,
-  BuiltinOptions_HashtableFindOptions = 108,
-  BuiltinOptions_HashtableImportOptions = 109,
-  BuiltinOptions_HashtableSizeOptions = 110,
-  BuiltinOptions_VarHandleOptions = 111,
-  BuiltinOptions_ReadVariableOptions = 112,
-  BuiltinOptions_AssignVariableOptions = 113,
-  BuiltinOptions_RandomOptions = 114,
-  BuiltinOptions_BucketizeOptions = 115,
-  BuiltinOptions_GeluOptions = 116,
-  BuiltinOptions_DynamicUpdateSliceOptions = 117,
-  BuiltinOptions_UnsortedSegmentProdOptions = 118,
-  BuiltinOptions_UnsortedSegmentMaxOptions = 119,
-  BuiltinOptions_UnsortedSegmentMinOptions = 120,
-  BuiltinOptions_UnsortedSegmentSumOptions = 121,
-  BuiltinOptions_ATan2Options = 122,
-  BuiltinOptions_SignOptions = 123,
-  BuiltinOptions_MIN = BuiltinOptions_NONE,
-  BuiltinOptions_MAX = BuiltinOptions_SignOptions
-};
-
-inline const BuiltinOptions (&EnumValuesBuiltinOptions())[124] {
-  static const BuiltinOptions values[] = {
-    BuiltinOptions_NONE,
-    BuiltinOptions_Conv2DOptions,
-    BuiltinOptions_DepthwiseConv2DOptions,
-    BuiltinOptions_ConcatEmbeddingsOptions,
-    BuiltinOptions_LSHProjectionOptions,
-    BuiltinOptions_Pool2DOptions,
-    BuiltinOptions_SVDFOptions,
-    BuiltinOptions_RNNOptions,
-    BuiltinOptions_FullyConnectedOptions,
-    BuiltinOptions_SoftmaxOptions,
-    BuiltinOptions_ConcatenationOptions,
-    BuiltinOptions_AddOptions,
-    BuiltinOptions_L2NormOptions,
-    BuiltinOptions_LocalResponseNormalizationOptions,
-    BuiltinOptions_LSTMOptions,
-    BuiltinOptions_ResizeBilinearOptions,
-    BuiltinOptions_CallOptions,
-    BuiltinOptions_ReshapeOptions,
-    BuiltinOptions_SkipGramOptions,
-    BuiltinOptions_SpaceToDepthOptions,
-    BuiltinOptions_EmbeddingLookupSparseOptions,
-    BuiltinOptions_MulOptions,
-    BuiltinOptions_PadOptions,
-    BuiltinOptions_GatherOptions,
-    BuiltinOptions_BatchToSpaceNDOptions,
-    BuiltinOptions_SpaceToBatchNDOptions,
-    BuiltinOptions_TransposeOptions,
-    BuiltinOptions_ReducerOptions,
-    BuiltinOptions_SubOptions,
-    BuiltinOptions_DivOptions,
-    BuiltinOptions_SqueezeOptions,
-    BuiltinOptions_SequenceRNNOptions,
-    BuiltinOptions_StridedSliceOptions,
-    BuiltinOptions_ExpOptions,
-    BuiltinOptions_TopKV2Options,
-    BuiltinOptions_SplitOptions,
-    BuiltinOptions_LogSoftmaxOptions,
-    BuiltinOptions_CastOptions,
-    BuiltinOptions_DequantizeOptions,
-    BuiltinOptions_MaximumMinimumOptions,
-    BuiltinOptions_ArgMaxOptions,
-    BuiltinOptions_LessOptions,
-    BuiltinOptions_NegOptions,
-    BuiltinOptions_PadV2Options,
-    BuiltinOptions_GreaterOptions,
-    BuiltinOptions_GreaterEqualOptions,
-    BuiltinOptions_LessEqualOptions,
-    BuiltinOptions_SelectOptions,
-    BuiltinOptions_SliceOptions,
-    BuiltinOptions_TransposeConvOptions,
-    BuiltinOptions_SparseToDenseOptions,
-    BuiltinOptions_TileOptions,
-    BuiltinOptions_ExpandDimsOptions,
-    BuiltinOptions_EqualOptions,
-    BuiltinOptions_NotEqualOptions,
-    BuiltinOptions_ShapeOptions,
-    BuiltinOptions_PowOptions,
-    BuiltinOptions_ArgMinOptions,
-    BuiltinOptions_FakeQuantOptions,
-    BuiltinOptions_PackOptions,
-    BuiltinOptions_LogicalOrOptions,
-    BuiltinOptions_OneHotOptions,
-    BuiltinOptions_LogicalAndOptions,
-    BuiltinOptions_LogicalNotOptions,
-    BuiltinOptions_UnpackOptions,
-    BuiltinOptions_FloorDivOptions,
-    BuiltinOptions_SquareOptions,
-    BuiltinOptions_ZerosLikeOptions,
-    BuiltinOptions_FillOptions,
-    BuiltinOptions_BidirectionalSequenceLSTMOptions,
-    BuiltinOptions_BidirectionalSequenceRNNOptions,
-    BuiltinOptions_UnidirectionalSequenceLSTMOptions,
-    BuiltinOptions_FloorModOptions,
-    BuiltinOptions_RangeOptions,
-    BuiltinOptions_ResizeNearestNeighborOptions,
-    BuiltinOptions_LeakyReluOptions,
-    BuiltinOptions_SquaredDifferenceOptions,
-    BuiltinOptions_MirrorPadOptions,
-    BuiltinOptions_AbsOptions,
-    BuiltinOptions_SplitVOptions,
-    BuiltinOptions_UniqueOptions,
-    BuiltinOptions_ReverseV2Options,
-    BuiltinOptions_AddNOptions,
-    BuiltinOptions_GatherNdOptions,
-    BuiltinOptions_CosOptions,
-    BuiltinOptions_WhereOptions,
-    BuiltinOptions_RankOptions,
-    BuiltinOptions_ReverseSequenceOptions,
-    BuiltinOptions_MatrixDiagOptions,
-    BuiltinOptions_QuantizeOptions,
-    BuiltinOptions_MatrixSetDiagOptions,
-    BuiltinOptions_HardSwishOptions,
-    BuiltinOptions_IfOptions,
-    BuiltinOptions_WhileOptions,
-    BuiltinOptions_DepthToSpaceOptions,
-    BuiltinOptions_NonMaxSuppressionV4Options,
-    BuiltinOptions_NonMaxSuppressionV5Options,
-    BuiltinOptions_ScatterNdOptions,
-    BuiltinOptions_SelectV2Options,
-    BuiltinOptions_DensifyOptions,
-    BuiltinOptions_SegmentSumOptions,
-    BuiltinOptions_BatchMatMulOptions,
-    BuiltinOptions_CumsumOptions,
-    BuiltinOptions_CallOnceOptions,
-    BuiltinOptions_BroadcastToOptions,
-    BuiltinOptions_Rfft2dOptions,
-    BuiltinOptions_Conv3DOptions,
-    BuiltinOptions_HashtableOptions,
-    BuiltinOptions_HashtableFindOptions,
-    BuiltinOptions_HashtableImportOptions,
-    BuiltinOptions_HashtableSizeOptions,
-    BuiltinOptions_VarHandleOptions,
-    BuiltinOptions_ReadVariableOptions,
-    BuiltinOptions_AssignVariableOptions,
-    BuiltinOptions_RandomOptions,
-    BuiltinOptions_BucketizeOptions,
-    BuiltinOptions_GeluOptions,
-    BuiltinOptions_DynamicUpdateSliceOptions,
-    BuiltinOptions_UnsortedSegmentProdOptions,
-    BuiltinOptions_UnsortedSegmentMaxOptions,
-    BuiltinOptions_UnsortedSegmentMinOptions,
-    BuiltinOptions_UnsortedSegmentSumOptions,
-    BuiltinOptions_ATan2Options,
-    BuiltinOptions_SignOptions
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesBuiltinOptions() {
-  static const char * const names[125] = {
-    "NONE",
-    "Conv2DOptions",
-    "DepthwiseConv2DOptions",
-    "ConcatEmbeddingsOptions",
-    "LSHProjectionOptions",
-    "Pool2DOptions",
-    "SVDFOptions",
-    "RNNOptions",
-    "FullyConnectedOptions",
-    "SoftmaxOptions",
-    "ConcatenationOptions",
-    "AddOptions",
-    "L2NormOptions",
-    "LocalResponseNormalizationOptions",
-    "LSTMOptions",
-    "ResizeBilinearOptions",
-    "CallOptions",
-    "ReshapeOptions",
-    "SkipGramOptions",
-    "SpaceToDepthOptions",
-    "EmbeddingLookupSparseOptions",
-    "MulOptions",
-    "PadOptions",
-    "GatherOptions",
-    "BatchToSpaceNDOptions",
-    "SpaceToBatchNDOptions",
-    "TransposeOptions",
-    "ReducerOptions",
-    "SubOptions",
-    "DivOptions",
-    "SqueezeOptions",
-    "SequenceRNNOptions",
-    "StridedSliceOptions",
-    "ExpOptions",
-    "TopKV2Options",
-    "SplitOptions",
-    "LogSoftmaxOptions",
-    "CastOptions",
-    "DequantizeOptions",
-    "MaximumMinimumOptions",
-    "ArgMaxOptions",
-    "LessOptions",
-    "NegOptions",
-    "PadV2Options",
-    "GreaterOptions",
-    "GreaterEqualOptions",
-    "LessEqualOptions",
-    "SelectOptions",
-    "SliceOptions",
-    "TransposeConvOptions",
-    "SparseToDenseOptions",
-    "TileOptions",
-    "ExpandDimsOptions",
-    "EqualOptions",
-    "NotEqualOptions",
-    "ShapeOptions",
-    "PowOptions",
-    "ArgMinOptions",
-    "FakeQuantOptions",
-    "PackOptions",
-    "LogicalOrOptions",
-    "OneHotOptions",
-    "LogicalAndOptions",
-    "LogicalNotOptions",
-    "UnpackOptions",
-    "FloorDivOptions",
-    "SquareOptions",
-    "ZerosLikeOptions",
-    "FillOptions",
-    "BidirectionalSequenceLSTMOptions",
-    "BidirectionalSequenceRNNOptions",
-    "UnidirectionalSequenceLSTMOptions",
-    "FloorModOptions",
-    "RangeOptions",
-    "ResizeNearestNeighborOptions",
-    "LeakyReluOptions",
-    "SquaredDifferenceOptions",
-    "MirrorPadOptions",
-    "AbsOptions",
-    "SplitVOptions",
-    "UniqueOptions",
-    "ReverseV2Options",
-    "AddNOptions",
-    "GatherNdOptions",
-    "CosOptions",
-    "WhereOptions",
-    "RankOptions",
-    "ReverseSequenceOptions",
-    "MatrixDiagOptions",
-    "QuantizeOptions",
-    "MatrixSetDiagOptions",
-    "HardSwishOptions",
-    "IfOptions",
-    "WhileOptions",
-    "DepthToSpaceOptions",
-    "NonMaxSuppressionV4Options",
-    "NonMaxSuppressionV5Options",
-    "ScatterNdOptions",
-    "SelectV2Options",
-    "DensifyOptions",
-    "SegmentSumOptions",
-    "BatchMatMulOptions",
-    "CumsumOptions",
-    "CallOnceOptions",
-    "BroadcastToOptions",
-    "Rfft2dOptions",
-    "Conv3DOptions",
-    "HashtableOptions",
-    "HashtableFindOptions",
-    "HashtableImportOptions",
-    "HashtableSizeOptions",
-    "VarHandleOptions",
-    "ReadVariableOptions",
-    "AssignVariableOptions",
-    "RandomOptions",
-    "BucketizeOptions",
-    "GeluOptions",
-    "DynamicUpdateSliceOptions",
-    "UnsortedSegmentProdOptions",
-    "UnsortedSegmentMaxOptions",
-    "UnsortedSegmentMinOptions",
-    "UnsortedSegmentSumOptions",
-    "ATan2Options",
-    "SignOptions",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameBuiltinOptions(BuiltinOptions e) {
-  if (flatbuffers::IsOutRange(e, BuiltinOptions_NONE, BuiltinOptions_SignOptions)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesBuiltinOptions()[index];
-}
-
-template<typename T> struct BuiltinOptionsTraits {
-  static const BuiltinOptions enum_value = BuiltinOptions_NONE;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::Conv2DOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::DepthwiseConv2DOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ConcatEmbeddingsOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::LSHProjectionOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::Pool2DOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SVDFOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::RNNOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::FullyConnectedOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SoftmaxOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ConcatenationOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::AddOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_AddOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::L2NormOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::LocalResponseNormalizationOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::LSTMOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ResizeBilinearOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::CallOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_CallOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ReshapeOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SkipGramOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SpaceToDepthOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::EmbeddingLookupSparseOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::MulOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_MulOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::PadOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_PadOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::GatherOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::BatchToSpaceNDOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SpaceToBatchNDOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::TransposeOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ReducerOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SubOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SubOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::DivOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_DivOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SqueezeOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SequenceRNNOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::StridedSliceOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ExpOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::TopKV2Options> {
-  static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SplitOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::LogSoftmaxOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::CastOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_CastOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::DequantizeOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::MaximumMinimumOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_MaximumMinimumOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ArgMaxOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::LessOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LessOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::NegOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_NegOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::PadV2Options> {
-  static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::GreaterOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::GreaterEqualOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::LessEqualOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SelectOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SliceOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::TransposeConvOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_TransposeConvOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SparseToDenseOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SparseToDenseOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::TileOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_TileOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ExpandDimsOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ExpandDimsOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::EqualOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_EqualOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::NotEqualOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ShapeOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::PowOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_PowOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ArgMinOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::FakeQuantOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::PackOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_PackOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::LogicalOrOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::OneHotOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::LogicalAndOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LogicalAndOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::LogicalNotOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LogicalNotOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::UnpackOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UnpackOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::FloorDivOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_FloorDivOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SquareOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SquareOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ZerosLikeOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ZerosLikeOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::FillOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_FillOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::BidirectionalSequenceLSTMOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceLSTMOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::BidirectionalSequenceRNNOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceRNNOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::UnidirectionalSequenceLSTMOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UnidirectionalSequenceLSTMOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::FloorModOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_FloorModOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::RangeOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_RangeOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ResizeNearestNeighborOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ResizeNearestNeighborOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::LeakyReluOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LeakyReluOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SquaredDifferenceOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SquaredDifferenceOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::MirrorPadOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_MirrorPadOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::AbsOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_AbsOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SplitVOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SplitVOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::UniqueOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UniqueOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ReverseV2Options> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ReverseV2Options;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::AddNOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_AddNOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::GatherNdOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_GatherNdOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::CosOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_CosOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::WhereOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_WhereOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::RankOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_RankOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ReverseSequenceOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ReverseSequenceOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::MatrixDiagOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_MatrixDiagOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::QuantizeOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_QuantizeOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::MatrixSetDiagOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_MatrixSetDiagOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::HardSwishOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_HardSwishOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::IfOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_IfOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::WhileOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_WhileOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::DepthToSpaceOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_DepthToSpaceOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::NonMaxSuppressionV4Options> {
-  static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV4Options;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::NonMaxSuppressionV5Options> {
-  static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV5Options;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ScatterNdOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ScatterNdOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SelectV2Options> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SelectV2Options;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::DensifyOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_DensifyOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SegmentSumOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SegmentSumOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::BatchMatMulOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_BatchMatMulOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::CumsumOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_CumsumOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::CallOnceOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_CallOnceOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::BroadcastToOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_BroadcastToOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::Rfft2dOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_Rfft2dOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::Conv3DOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_Conv3DOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::HashtableOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_HashtableOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::HashtableFindOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_HashtableFindOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::HashtableImportOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_HashtableImportOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::HashtableSizeOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_HashtableSizeOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::VarHandleOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_VarHandleOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ReadVariableOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ReadVariableOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::AssignVariableOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_AssignVariableOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::RandomOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_RandomOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::BucketizeOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_BucketizeOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::GeluOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_GeluOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::DynamicUpdateSliceOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_DynamicUpdateSliceOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::UnsortedSegmentProdOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentProdOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::UnsortedSegmentMaxOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentMaxOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::UnsortedSegmentMinOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentMinOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::UnsortedSegmentSumOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentSumOptions;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::ATan2Options> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ATan2Options;
-};
-
-template<> struct BuiltinOptionsTraits<tflite::SignOptions> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SignOptions;
-};
-
-template<typename T> struct BuiltinOptionsUnionTraits {
-  static const BuiltinOptions enum_value = BuiltinOptions_NONE;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::Conv2DOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_Conv2DOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::DepthwiseConv2DOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_DepthwiseConv2DOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ConcatEmbeddingsOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ConcatEmbeddingsOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::LSHProjectionOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LSHProjectionOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::Pool2DOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_Pool2DOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SVDFOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SVDFOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::RNNOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_RNNOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::FullyConnectedOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_FullyConnectedOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SoftmaxOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SoftmaxOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ConcatenationOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ConcatenationOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::AddOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_AddOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::L2NormOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_L2NormOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::LocalResponseNormalizationOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LocalResponseNormalizationOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::LSTMOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LSTMOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ResizeBilinearOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ResizeBilinearOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::CallOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_CallOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ReshapeOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ReshapeOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SkipGramOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SkipGramOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SpaceToDepthOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SpaceToDepthOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::EmbeddingLookupSparseOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_EmbeddingLookupSparseOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::MulOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_MulOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::PadOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_PadOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::GatherOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_GatherOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::BatchToSpaceNDOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_BatchToSpaceNDOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SpaceToBatchNDOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SpaceToBatchNDOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::TransposeOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_TransposeOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ReducerOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ReducerOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SubOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SubOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::DivOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_DivOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SqueezeOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SqueezeOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SequenceRNNOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SequenceRNNOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::StridedSliceOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_StridedSliceOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ExpOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ExpOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::TopKV2OptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_TopKV2Options;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SplitOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SplitOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::LogSoftmaxOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LogSoftmaxOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::CastOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_CastOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::DequantizeOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_DequantizeOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::MaximumMinimumOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_MaximumMinimumOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ArgMaxOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ArgMaxOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::LessOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LessOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::NegOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_NegOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::PadV2OptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_PadV2Options;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::GreaterOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_GreaterOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::GreaterEqualOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_GreaterEqualOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::LessEqualOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LessEqualOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SelectOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SelectOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SliceOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SliceOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::TransposeConvOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_TransposeConvOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SparseToDenseOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SparseToDenseOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::TileOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_TileOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ExpandDimsOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ExpandDimsOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::EqualOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_EqualOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::NotEqualOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_NotEqualOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ShapeOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ShapeOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::PowOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_PowOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ArgMinOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ArgMinOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::FakeQuantOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_FakeQuantOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::PackOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_PackOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::LogicalOrOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::OneHotOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::LogicalAndOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LogicalAndOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::LogicalNotOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LogicalNotOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::UnpackOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UnpackOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::FloorDivOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_FloorDivOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SquareOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SquareOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ZerosLikeOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ZerosLikeOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::FillOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_FillOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::BidirectionalSequenceLSTMOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceLSTMOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::BidirectionalSequenceRNNOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_BidirectionalSequenceRNNOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::UnidirectionalSequenceLSTMOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UnidirectionalSequenceLSTMOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::FloorModOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_FloorModOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::RangeOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_RangeOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ResizeNearestNeighborOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ResizeNearestNeighborOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::LeakyReluOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_LeakyReluOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SquaredDifferenceOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SquaredDifferenceOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::MirrorPadOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_MirrorPadOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::AbsOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_AbsOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SplitVOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SplitVOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::UniqueOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UniqueOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ReverseV2OptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ReverseV2Options;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::AddNOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_AddNOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::GatherNdOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_GatherNdOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::CosOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_CosOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::WhereOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_WhereOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::RankOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_RankOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ReverseSequenceOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ReverseSequenceOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::MatrixDiagOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_MatrixDiagOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::QuantizeOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_QuantizeOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::MatrixSetDiagOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_MatrixSetDiagOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::HardSwishOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_HardSwishOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::IfOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_IfOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::WhileOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_WhileOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::DepthToSpaceOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_DepthToSpaceOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::NonMaxSuppressionV4OptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV4Options;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::NonMaxSuppressionV5OptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_NonMaxSuppressionV5Options;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ScatterNdOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ScatterNdOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SelectV2OptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SelectV2Options;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::DensifyOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_DensifyOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SegmentSumOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SegmentSumOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::BatchMatMulOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_BatchMatMulOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::CumsumOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_CumsumOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::CallOnceOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_CallOnceOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::BroadcastToOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_BroadcastToOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::Rfft2dOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_Rfft2dOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::Conv3DOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_Conv3DOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::HashtableOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_HashtableOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::HashtableFindOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_HashtableFindOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::HashtableImportOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_HashtableImportOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::HashtableSizeOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_HashtableSizeOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::VarHandleOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_VarHandleOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ReadVariableOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ReadVariableOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::AssignVariableOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_AssignVariableOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::RandomOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_RandomOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::BucketizeOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_BucketizeOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::GeluOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_GeluOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::DynamicUpdateSliceOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_DynamicUpdateSliceOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::UnsortedSegmentProdOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentProdOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::UnsortedSegmentMaxOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentMaxOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::UnsortedSegmentMinOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentMinOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::UnsortedSegmentSumOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_UnsortedSegmentSumOptions;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::ATan2OptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_ATan2Options;
-};
-
-template<> struct BuiltinOptionsUnionTraits<tflite::SignOptionsT> {
-  static const BuiltinOptions enum_value = BuiltinOptions_SignOptions;
-};
-
-struct BuiltinOptionsUnion {
-  BuiltinOptions type;
-  void *value;
-
-  BuiltinOptionsUnion() : type(BuiltinOptions_NONE), value(nullptr) {}
-  BuiltinOptionsUnion(BuiltinOptionsUnion&& u) FLATBUFFERS_NOEXCEPT :
-    type(BuiltinOptions_NONE), value(nullptr)
-    { std::swap(type, u.type); std::swap(value, u.value); }
-  BuiltinOptionsUnion(const BuiltinOptionsUnion &);
-  BuiltinOptionsUnion &operator=(const BuiltinOptionsUnion &u)
-    { BuiltinOptionsUnion t(u); std::swap(type, t.type); std::swap(value, t.value); return *this; }
-  BuiltinOptionsUnion &operator=(BuiltinOptionsUnion &&u) FLATBUFFERS_NOEXCEPT
-    { std::swap(type, u.type); std::swap(value, u.value); return *this; }
-  ~BuiltinOptionsUnion() { Reset(); }
-
-  void Reset();
-
-  template <typename T>
-  void Set(T&& val) {
-    typedef typename std::remove_reference<T>::type RT;
-    Reset();
-    type = BuiltinOptionsUnionTraits<RT>::enum_value;
-    if (type != BuiltinOptions_NONE) {
-      value = new RT(std::forward<T>(val));
-    }
-  }
-
-  static void *UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver);
-  flatbuffers::Offset<void> Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher = nullptr) const;
-
-  tflite::Conv2DOptionsT *AsConv2DOptions() {
-    return type == BuiltinOptions_Conv2DOptions ?
-      reinterpret_cast<tflite::Conv2DOptionsT *>(value) : nullptr;
-  }
-  const tflite::Conv2DOptionsT *AsConv2DOptions() const {
-    return type == BuiltinOptions_Conv2DOptions ?
-      reinterpret_cast<const tflite::Conv2DOptionsT *>(value) : nullptr;
-  }
-  tflite::DepthwiseConv2DOptionsT *AsDepthwiseConv2DOptions() {
-    return type == BuiltinOptions_DepthwiseConv2DOptions ?
-      reinterpret_cast<tflite::DepthwiseConv2DOptionsT *>(value) : nullptr;
-  }
-  const tflite::DepthwiseConv2DOptionsT *AsDepthwiseConv2DOptions() const {
-    return type == BuiltinOptions_DepthwiseConv2DOptions ?
-      reinterpret_cast<const tflite::DepthwiseConv2DOptionsT *>(value) : nullptr;
-  }
-  tflite::ConcatEmbeddingsOptionsT *AsConcatEmbeddingsOptions() {
-    return type == BuiltinOptions_ConcatEmbeddingsOptions ?
-      reinterpret_cast<tflite::ConcatEmbeddingsOptionsT *>(value) : nullptr;
-  }
-  const tflite::ConcatEmbeddingsOptionsT *AsConcatEmbeddingsOptions() const {
-    return type == BuiltinOptions_ConcatEmbeddingsOptions ?
-      reinterpret_cast<const tflite::ConcatEmbeddingsOptionsT *>(value) : nullptr;
-  }
-  tflite::LSHProjectionOptionsT *AsLSHProjectionOptions() {
-    return type == BuiltinOptions_LSHProjectionOptions ?
-      reinterpret_cast<tflite::LSHProjectionOptionsT *>(value) : nullptr;
-  }
-  const tflite::LSHProjectionOptionsT *AsLSHProjectionOptions() const {
-    return type == BuiltinOptions_LSHProjectionOptions ?
-      reinterpret_cast<const tflite::LSHProjectionOptionsT *>(value) : nullptr;
-  }
-  tflite::Pool2DOptionsT *AsPool2DOptions() {
-    return type == BuiltinOptions_Pool2DOptions ?
-      reinterpret_cast<tflite::Pool2DOptionsT *>(value) : nullptr;
-  }
-  const tflite::Pool2DOptionsT *AsPool2DOptions() const {
-    return type == BuiltinOptions_Pool2DOptions ?
-      reinterpret_cast<const tflite::Pool2DOptionsT *>(value) : nullptr;
-  }
-  tflite::SVDFOptionsT *AsSVDFOptions() {
-    return type == BuiltinOptions_SVDFOptions ?
-      reinterpret_cast<tflite::SVDFOptionsT *>(value) : nullptr;
-  }
-  const tflite::SVDFOptionsT *AsSVDFOptions() const {
-    return type == BuiltinOptions_SVDFOptions ?
-      reinterpret_cast<const tflite::SVDFOptionsT *>(value) : nullptr;
-  }
-  tflite::RNNOptionsT *AsRNNOptions() {
-    return type == BuiltinOptions_RNNOptions ?
-      reinterpret_cast<tflite::RNNOptionsT *>(value) : nullptr;
-  }
-  const tflite::RNNOptionsT *AsRNNOptions() const {
-    return type == BuiltinOptions_RNNOptions ?
-      reinterpret_cast<const tflite::RNNOptionsT *>(value) : nullptr;
-  }
-  tflite::FullyConnectedOptionsT *AsFullyConnectedOptions() {
-    return type == BuiltinOptions_FullyConnectedOptions ?
-      reinterpret_cast<tflite::FullyConnectedOptionsT *>(value) : nullptr;
-  }
-  const tflite::FullyConnectedOptionsT *AsFullyConnectedOptions() const {
-    return type == BuiltinOptions_FullyConnectedOptions ?
-      reinterpret_cast<const tflite::FullyConnectedOptionsT *>(value) : nullptr;
-  }
-  tflite::SoftmaxOptionsT *AsSoftmaxOptions() {
-    return type == BuiltinOptions_SoftmaxOptions ?
-      reinterpret_cast<tflite::SoftmaxOptionsT *>(value) : nullptr;
-  }
-  const tflite::SoftmaxOptionsT *AsSoftmaxOptions() const {
-    return type == BuiltinOptions_SoftmaxOptions ?
-      reinterpret_cast<const tflite::SoftmaxOptionsT *>(value) : nullptr;
-  }
-  tflite::ConcatenationOptionsT *AsConcatenationOptions() {
-    return type == BuiltinOptions_ConcatenationOptions ?
-      reinterpret_cast<tflite::ConcatenationOptionsT *>(value) : nullptr;
-  }
-  const tflite::ConcatenationOptionsT *AsConcatenationOptions() const {
-    return type == BuiltinOptions_ConcatenationOptions ?
-      reinterpret_cast<const tflite::ConcatenationOptionsT *>(value) : nullptr;
-  }
-  tflite::AddOptionsT *AsAddOptions() {
-    return type == BuiltinOptions_AddOptions ?
-      reinterpret_cast<tflite::AddOptionsT *>(value) : nullptr;
-  }
-  const tflite::AddOptionsT *AsAddOptions() const {
-    return type == BuiltinOptions_AddOptions ?
-      reinterpret_cast<const tflite::AddOptionsT *>(value) : nullptr;
-  }
-  tflite::L2NormOptionsT *AsL2NormOptions() {
-    return type == BuiltinOptions_L2NormOptions ?
-      reinterpret_cast<tflite::L2NormOptionsT *>(value) : nullptr;
-  }
-  const tflite::L2NormOptionsT *AsL2NormOptions() const {
-    return type == BuiltinOptions_L2NormOptions ?
-      reinterpret_cast<const tflite::L2NormOptionsT *>(value) : nullptr;
-  }
-  tflite::LocalResponseNormalizationOptionsT *AsLocalResponseNormalizationOptions() {
-    return type == BuiltinOptions_LocalResponseNormalizationOptions ?
-      reinterpret_cast<tflite::LocalResponseNormalizationOptionsT *>(value) : nullptr;
-  }
-  const tflite::LocalResponseNormalizationOptionsT *AsLocalResponseNormalizationOptions() const {
-    return type == BuiltinOptions_LocalResponseNormalizationOptions ?
-      reinterpret_cast<const tflite::LocalResponseNormalizationOptionsT *>(value) : nullptr;
-  }
-  tflite::LSTMOptionsT *AsLSTMOptions() {
-    return type == BuiltinOptions_LSTMOptions ?
-      reinterpret_cast<tflite::LSTMOptionsT *>(value) : nullptr;
-  }
-  const tflite::LSTMOptionsT *AsLSTMOptions() const {
-    return type == BuiltinOptions_LSTMOptions ?
-      reinterpret_cast<const tflite::LSTMOptionsT *>(value) : nullptr;
-  }
-  tflite::ResizeBilinearOptionsT *AsResizeBilinearOptions() {
-    return type == BuiltinOptions_ResizeBilinearOptions ?
-      reinterpret_cast<tflite::ResizeBilinearOptionsT *>(value) : nullptr;
-  }
-  const tflite::ResizeBilinearOptionsT *AsResizeBilinearOptions() const {
-    return type == BuiltinOptions_ResizeBilinearOptions ?
-      reinterpret_cast<const tflite::ResizeBilinearOptionsT *>(value) : nullptr;
-  }
-  tflite::CallOptionsT *AsCallOptions() {
-    return type == BuiltinOptions_CallOptions ?
-      reinterpret_cast<tflite::CallOptionsT *>(value) : nullptr;
-  }
-  const tflite::CallOptionsT *AsCallOptions() const {
-    return type == BuiltinOptions_CallOptions ?
-      reinterpret_cast<const tflite::CallOptionsT *>(value) : nullptr;
-  }
-  tflite::ReshapeOptionsT *AsReshapeOptions() {
-    return type == BuiltinOptions_ReshapeOptions ?
-      reinterpret_cast<tflite::ReshapeOptionsT *>(value) : nullptr;
-  }
-  const tflite::ReshapeOptionsT *AsReshapeOptions() const {
-    return type == BuiltinOptions_ReshapeOptions ?
-      reinterpret_cast<const tflite::ReshapeOptionsT *>(value) : nullptr;
-  }
-  tflite::SkipGramOptionsT *AsSkipGramOptions() {
-    return type == BuiltinOptions_SkipGramOptions ?
-      reinterpret_cast<tflite::SkipGramOptionsT *>(value) : nullptr;
-  }
-  const tflite::SkipGramOptionsT *AsSkipGramOptions() const {
-    return type == BuiltinOptions_SkipGramOptions ?
-      reinterpret_cast<const tflite::SkipGramOptionsT *>(value) : nullptr;
-  }
-  tflite::SpaceToDepthOptionsT *AsSpaceToDepthOptions() {
-    return type == BuiltinOptions_SpaceToDepthOptions ?
-      reinterpret_cast<tflite::SpaceToDepthOptionsT *>(value) : nullptr;
-  }
-  const tflite::SpaceToDepthOptionsT *AsSpaceToDepthOptions() const {
-    return type == BuiltinOptions_SpaceToDepthOptions ?
-      reinterpret_cast<const tflite::SpaceToDepthOptionsT *>(value) : nullptr;
-  }
-  tflite::EmbeddingLookupSparseOptionsT *AsEmbeddingLookupSparseOptions() {
-    return type == BuiltinOptions_EmbeddingLookupSparseOptions ?
-      reinterpret_cast<tflite::EmbeddingLookupSparseOptionsT *>(value) : nullptr;
-  }
-  const tflite::EmbeddingLookupSparseOptionsT *AsEmbeddingLookupSparseOptions() const {
-    return type == BuiltinOptions_EmbeddingLookupSparseOptions ?
-      reinterpret_cast<const tflite::EmbeddingLookupSparseOptionsT *>(value) : nullptr;
-  }
-  tflite::MulOptionsT *AsMulOptions() {
-    return type == BuiltinOptions_MulOptions ?
-      reinterpret_cast<tflite::MulOptionsT *>(value) : nullptr;
-  }
-  const tflite::MulOptionsT *AsMulOptions() const {
-    return type == BuiltinOptions_MulOptions ?
-      reinterpret_cast<const tflite::MulOptionsT *>(value) : nullptr;
-  }
-  tflite::PadOptionsT *AsPadOptions() {
-    return type == BuiltinOptions_PadOptions ?
-      reinterpret_cast<tflite::PadOptionsT *>(value) : nullptr;
-  }
-  const tflite::PadOptionsT *AsPadOptions() const {
-    return type == BuiltinOptions_PadOptions ?
-      reinterpret_cast<const tflite::PadOptionsT *>(value) : nullptr;
-  }
-  tflite::GatherOptionsT *AsGatherOptions() {
-    return type == BuiltinOptions_GatherOptions ?
-      reinterpret_cast<tflite::GatherOptionsT *>(value) : nullptr;
-  }
-  const tflite::GatherOptionsT *AsGatherOptions() const {
-    return type == BuiltinOptions_GatherOptions ?
-      reinterpret_cast<const tflite::GatherOptionsT *>(value) : nullptr;
-  }
-  tflite::BatchToSpaceNDOptionsT *AsBatchToSpaceNDOptions() {
-    return type == BuiltinOptions_BatchToSpaceNDOptions ?
-      reinterpret_cast<tflite::BatchToSpaceNDOptionsT *>(value) : nullptr;
-  }
-  const tflite::BatchToSpaceNDOptionsT *AsBatchToSpaceNDOptions() const {
-    return type == BuiltinOptions_BatchToSpaceNDOptions ?
-      reinterpret_cast<const tflite::BatchToSpaceNDOptionsT *>(value) : nullptr;
-  }
-  tflite::SpaceToBatchNDOptionsT *AsSpaceToBatchNDOptions() {
-    return type == BuiltinOptions_SpaceToBatchNDOptions ?
-      reinterpret_cast<tflite::SpaceToBatchNDOptionsT *>(value) : nullptr;
-  }
-  const tflite::SpaceToBatchNDOptionsT *AsSpaceToBatchNDOptions() const {
-    return type == BuiltinOptions_SpaceToBatchNDOptions ?
-      reinterpret_cast<const tflite::SpaceToBatchNDOptionsT *>(value) : nullptr;
-  }
-  tflite::TransposeOptionsT *AsTransposeOptions() {
-    return type == BuiltinOptions_TransposeOptions ?
-      reinterpret_cast<tflite::TransposeOptionsT *>(value) : nullptr;
-  }
-  const tflite::TransposeOptionsT *AsTransposeOptions() const {
-    return type == BuiltinOptions_TransposeOptions ?
-      reinterpret_cast<const tflite::TransposeOptionsT *>(value) : nullptr;
-  }
-  tflite::ReducerOptionsT *AsReducerOptions() {
-    return type == BuiltinOptions_ReducerOptions ?
-      reinterpret_cast<tflite::ReducerOptionsT *>(value) : nullptr;
-  }
-  const tflite::ReducerOptionsT *AsReducerOptions() const {
-    return type == BuiltinOptions_ReducerOptions ?
-      reinterpret_cast<const tflite::ReducerOptionsT *>(value) : nullptr;
-  }
-  tflite::SubOptionsT *AsSubOptions() {
-    return type == BuiltinOptions_SubOptions ?
-      reinterpret_cast<tflite::SubOptionsT *>(value) : nullptr;
-  }
-  const tflite::SubOptionsT *AsSubOptions() const {
-    return type == BuiltinOptions_SubOptions ?
-      reinterpret_cast<const tflite::SubOptionsT *>(value) : nullptr;
-  }
-  tflite::DivOptionsT *AsDivOptions() {
-    return type == BuiltinOptions_DivOptions ?
-      reinterpret_cast<tflite::DivOptionsT *>(value) : nullptr;
-  }
-  const tflite::DivOptionsT *AsDivOptions() const {
-    return type == BuiltinOptions_DivOptions ?
-      reinterpret_cast<const tflite::DivOptionsT *>(value) : nullptr;
-  }
-  tflite::SqueezeOptionsT *AsSqueezeOptions() {
-    return type == BuiltinOptions_SqueezeOptions ?
-      reinterpret_cast<tflite::SqueezeOptionsT *>(value) : nullptr;
-  }
-  const tflite::SqueezeOptionsT *AsSqueezeOptions() const {
-    return type == BuiltinOptions_SqueezeOptions ?
-      reinterpret_cast<const tflite::SqueezeOptionsT *>(value) : nullptr;
-  }
-  tflite::SequenceRNNOptionsT *AsSequenceRNNOptions() {
-    return type == BuiltinOptions_SequenceRNNOptions ?
-      reinterpret_cast<tflite::SequenceRNNOptionsT *>(value) : nullptr;
-  }
-  const tflite::SequenceRNNOptionsT *AsSequenceRNNOptions() const {
-    return type == BuiltinOptions_SequenceRNNOptions ?
-      reinterpret_cast<const tflite::SequenceRNNOptionsT *>(value) : nullptr;
-  }
-  tflite::StridedSliceOptionsT *AsStridedSliceOptions() {
-    return type == BuiltinOptions_StridedSliceOptions ?
-      reinterpret_cast<tflite::StridedSliceOptionsT *>(value) : nullptr;
-  }
-  const tflite::StridedSliceOptionsT *AsStridedSliceOptions() const {
-    return type == BuiltinOptions_StridedSliceOptions ?
-      reinterpret_cast<const tflite::StridedSliceOptionsT *>(value) : nullptr;
-  }
-  tflite::ExpOptionsT *AsExpOptions() {
-    return type == BuiltinOptions_ExpOptions ?
-      reinterpret_cast<tflite::ExpOptionsT *>(value) : nullptr;
-  }
-  const tflite::ExpOptionsT *AsExpOptions() const {
-    return type == BuiltinOptions_ExpOptions ?
-      reinterpret_cast<const tflite::ExpOptionsT *>(value) : nullptr;
-  }
-  tflite::TopKV2OptionsT *AsTopKV2Options() {
-    return type == BuiltinOptions_TopKV2Options ?
-      reinterpret_cast<tflite::TopKV2OptionsT *>(value) : nullptr;
-  }
-  const tflite::TopKV2OptionsT *AsTopKV2Options() const {
-    return type == BuiltinOptions_TopKV2Options ?
-      reinterpret_cast<const tflite::TopKV2OptionsT *>(value) : nullptr;
-  }
-  tflite::SplitOptionsT *AsSplitOptions() {
-    return type == BuiltinOptions_SplitOptions ?
-      reinterpret_cast<tflite::SplitOptionsT *>(value) : nullptr;
-  }
-  const tflite::SplitOptionsT *AsSplitOptions() const {
-    return type == BuiltinOptions_SplitOptions ?
-      reinterpret_cast<const tflite::SplitOptionsT *>(value) : nullptr;
-  }
-  tflite::LogSoftmaxOptionsT *AsLogSoftmaxOptions() {
-    return type == BuiltinOptions_LogSoftmaxOptions ?
-      reinterpret_cast<tflite::LogSoftmaxOptionsT *>(value) : nullptr;
-  }
-  const tflite::LogSoftmaxOptionsT *AsLogSoftmaxOptions() const {
-    return type == BuiltinOptions_LogSoftmaxOptions ?
-      reinterpret_cast<const tflite::LogSoftmaxOptionsT *>(value) : nullptr;
-  }
-  tflite::CastOptionsT *AsCastOptions() {
-    return type == BuiltinOptions_CastOptions ?
-      reinterpret_cast<tflite::CastOptionsT *>(value) : nullptr;
-  }
-  const tflite::CastOptionsT *AsCastOptions() const {
-    return type == BuiltinOptions_CastOptions ?
-      reinterpret_cast<const tflite::CastOptionsT *>(value) : nullptr;
-  }
-  tflite::DequantizeOptionsT *AsDequantizeOptions() {
-    return type == BuiltinOptions_DequantizeOptions ?
-      reinterpret_cast<tflite::DequantizeOptionsT *>(value) : nullptr;
-  }
-  const tflite::DequantizeOptionsT *AsDequantizeOptions() const {
-    return type == BuiltinOptions_DequantizeOptions ?
-      reinterpret_cast<const tflite::DequantizeOptionsT *>(value) : nullptr;
-  }
-  tflite::MaximumMinimumOptionsT *AsMaximumMinimumOptions() {
-    return type == BuiltinOptions_MaximumMinimumOptions ?
-      reinterpret_cast<tflite::MaximumMinimumOptionsT *>(value) : nullptr;
-  }
-  const tflite::MaximumMinimumOptionsT *AsMaximumMinimumOptions() const {
-    return type == BuiltinOptions_MaximumMinimumOptions ?
-      reinterpret_cast<const tflite::MaximumMinimumOptionsT *>(value) : nullptr;
-  }
-  tflite::ArgMaxOptionsT *AsArgMaxOptions() {
-    return type == BuiltinOptions_ArgMaxOptions ?
-      reinterpret_cast<tflite::ArgMaxOptionsT *>(value) : nullptr;
-  }
-  const tflite::ArgMaxOptionsT *AsArgMaxOptions() const {
-    return type == BuiltinOptions_ArgMaxOptions ?
-      reinterpret_cast<const tflite::ArgMaxOptionsT *>(value) : nullptr;
-  }
-  tflite::LessOptionsT *AsLessOptions() {
-    return type == BuiltinOptions_LessOptions ?
-      reinterpret_cast<tflite::LessOptionsT *>(value) : nullptr;
-  }
-  const tflite::LessOptionsT *AsLessOptions() const {
-    return type == BuiltinOptions_LessOptions ?
-      reinterpret_cast<const tflite::LessOptionsT *>(value) : nullptr;
-  }
-  tflite::NegOptionsT *AsNegOptions() {
-    return type == BuiltinOptions_NegOptions ?
-      reinterpret_cast<tflite::NegOptionsT *>(value) : nullptr;
-  }
-  const tflite::NegOptionsT *AsNegOptions() const {
-    return type == BuiltinOptions_NegOptions ?
-      reinterpret_cast<const tflite::NegOptionsT *>(value) : nullptr;
-  }
-  tflite::PadV2OptionsT *AsPadV2Options() {
-    return type == BuiltinOptions_PadV2Options ?
-      reinterpret_cast<tflite::PadV2OptionsT *>(value) : nullptr;
-  }
-  const tflite::PadV2OptionsT *AsPadV2Options() const {
-    return type == BuiltinOptions_PadV2Options ?
-      reinterpret_cast<const tflite::PadV2OptionsT *>(value) : nullptr;
-  }
-  tflite::GreaterOptionsT *AsGreaterOptions() {
-    return type == BuiltinOptions_GreaterOptions ?
-      reinterpret_cast<tflite::GreaterOptionsT *>(value) : nullptr;
-  }
-  const tflite::GreaterOptionsT *AsGreaterOptions() const {
-    return type == BuiltinOptions_GreaterOptions ?
-      reinterpret_cast<const tflite::GreaterOptionsT *>(value) : nullptr;
-  }
-  tflite::GreaterEqualOptionsT *AsGreaterEqualOptions() {
-    return type == BuiltinOptions_GreaterEqualOptions ?
-      reinterpret_cast<tflite::GreaterEqualOptionsT *>(value) : nullptr;
-  }
-  const tflite::GreaterEqualOptionsT *AsGreaterEqualOptions() const {
-    return type == BuiltinOptions_GreaterEqualOptions ?
-      reinterpret_cast<const tflite::GreaterEqualOptionsT *>(value) : nullptr;
-  }
-  tflite::LessEqualOptionsT *AsLessEqualOptions() {
-    return type == BuiltinOptions_LessEqualOptions ?
-      reinterpret_cast<tflite::LessEqualOptionsT *>(value) : nullptr;
-  }
-  const tflite::LessEqualOptionsT *AsLessEqualOptions() const {
-    return type == BuiltinOptions_LessEqualOptions ?
-      reinterpret_cast<const tflite::LessEqualOptionsT *>(value) : nullptr;
-  }
-  tflite::SelectOptionsT *AsSelectOptions() {
-    return type == BuiltinOptions_SelectOptions ?
-      reinterpret_cast<tflite::SelectOptionsT *>(value) : nullptr;
-  }
-  const tflite::SelectOptionsT *AsSelectOptions() const {
-    return type == BuiltinOptions_SelectOptions ?
-      reinterpret_cast<const tflite::SelectOptionsT *>(value) : nullptr;
-  }
-  tflite::SliceOptionsT *AsSliceOptions() {
-    return type == BuiltinOptions_SliceOptions ?
-      reinterpret_cast<tflite::SliceOptionsT *>(value) : nullptr;
-  }
-  const tflite::SliceOptionsT *AsSliceOptions() const {
-    return type == BuiltinOptions_SliceOptions ?
-      reinterpret_cast<const tflite::SliceOptionsT *>(value) : nullptr;
-  }
-  tflite::TransposeConvOptionsT *AsTransposeConvOptions() {
-    return type == BuiltinOptions_TransposeConvOptions ?
-      reinterpret_cast<tflite::TransposeConvOptionsT *>(value) : nullptr;
-  }
-  const tflite::TransposeConvOptionsT *AsTransposeConvOptions() const {
-    return type == BuiltinOptions_TransposeConvOptions ?
-      reinterpret_cast<const tflite::TransposeConvOptionsT *>(value) : nullptr;
-  }
-  tflite::SparseToDenseOptionsT *AsSparseToDenseOptions() {
-    return type == BuiltinOptions_SparseToDenseOptions ?
-      reinterpret_cast<tflite::SparseToDenseOptionsT *>(value) : nullptr;
-  }
-  const tflite::SparseToDenseOptionsT *AsSparseToDenseOptions() const {
-    return type == BuiltinOptions_SparseToDenseOptions ?
-      reinterpret_cast<const tflite::SparseToDenseOptionsT *>(value) : nullptr;
-  }
-  tflite::TileOptionsT *AsTileOptions() {
-    return type == BuiltinOptions_TileOptions ?
-      reinterpret_cast<tflite::TileOptionsT *>(value) : nullptr;
-  }
-  const tflite::TileOptionsT *AsTileOptions() const {
-    return type == BuiltinOptions_TileOptions ?
-      reinterpret_cast<const tflite::TileOptionsT *>(value) : nullptr;
-  }
-  tflite::ExpandDimsOptionsT *AsExpandDimsOptions() {
-    return type == BuiltinOptions_ExpandDimsOptions ?
-      reinterpret_cast<tflite::ExpandDimsOptionsT *>(value) : nullptr;
-  }
-  const tflite::ExpandDimsOptionsT *AsExpandDimsOptions() const {
-    return type == BuiltinOptions_ExpandDimsOptions ?
-      reinterpret_cast<const tflite::ExpandDimsOptionsT *>(value) : nullptr;
-  }
-  tflite::EqualOptionsT *AsEqualOptions() {
-    return type == BuiltinOptions_EqualOptions ?
-      reinterpret_cast<tflite::EqualOptionsT *>(value) : nullptr;
-  }
-  const tflite::EqualOptionsT *AsEqualOptions() const {
-    return type == BuiltinOptions_EqualOptions ?
-      reinterpret_cast<const tflite::EqualOptionsT *>(value) : nullptr;
-  }
-  tflite::NotEqualOptionsT *AsNotEqualOptions() {
-    return type == BuiltinOptions_NotEqualOptions ?
-      reinterpret_cast<tflite::NotEqualOptionsT *>(value) : nullptr;
-  }
-  const tflite::NotEqualOptionsT *AsNotEqualOptions() const {
-    return type == BuiltinOptions_NotEqualOptions ?
-      reinterpret_cast<const tflite::NotEqualOptionsT *>(value) : nullptr;
-  }
-  tflite::ShapeOptionsT *AsShapeOptions() {
-    return type == BuiltinOptions_ShapeOptions ?
-      reinterpret_cast<tflite::ShapeOptionsT *>(value) : nullptr;
-  }
-  const tflite::ShapeOptionsT *AsShapeOptions() const {
-    return type == BuiltinOptions_ShapeOptions ?
-      reinterpret_cast<const tflite::ShapeOptionsT *>(value) : nullptr;
-  }
-  tflite::PowOptionsT *AsPowOptions() {
-    return type == BuiltinOptions_PowOptions ?
-      reinterpret_cast<tflite::PowOptionsT *>(value) : nullptr;
-  }
-  const tflite::PowOptionsT *AsPowOptions() const {
-    return type == BuiltinOptions_PowOptions ?
-      reinterpret_cast<const tflite::PowOptionsT *>(value) : nullptr;
-  }
-  tflite::ArgMinOptionsT *AsArgMinOptions() {
-    return type == BuiltinOptions_ArgMinOptions ?
-      reinterpret_cast<tflite::ArgMinOptionsT *>(value) : nullptr;
-  }
-  const tflite::ArgMinOptionsT *AsArgMinOptions() const {
-    return type == BuiltinOptions_ArgMinOptions ?
-      reinterpret_cast<const tflite::ArgMinOptionsT *>(value) : nullptr;
-  }
-  tflite::FakeQuantOptionsT *AsFakeQuantOptions() {
-    return type == BuiltinOptions_FakeQuantOptions ?
-      reinterpret_cast<tflite::FakeQuantOptionsT *>(value) : nullptr;
-  }
-  const tflite::FakeQuantOptionsT *AsFakeQuantOptions() const {
-    return type == BuiltinOptions_FakeQuantOptions ?
-      reinterpret_cast<const tflite::FakeQuantOptionsT *>(value) : nullptr;
-  }
-  tflite::PackOptionsT *AsPackOptions() {
-    return type == BuiltinOptions_PackOptions ?
-      reinterpret_cast<tflite::PackOptionsT *>(value) : nullptr;
-  }
-  const tflite::PackOptionsT *AsPackOptions() const {
-    return type == BuiltinOptions_PackOptions ?
-      reinterpret_cast<const tflite::PackOptionsT *>(value) : nullptr;
-  }
-  tflite::LogicalOrOptionsT *AsLogicalOrOptions() {
-    return type == BuiltinOptions_LogicalOrOptions ?
-      reinterpret_cast<tflite::LogicalOrOptionsT *>(value) : nullptr;
-  }
-  const tflite::LogicalOrOptionsT *AsLogicalOrOptions() const {
-    return type == BuiltinOptions_LogicalOrOptions ?
-      reinterpret_cast<const tflite::LogicalOrOptionsT *>(value) : nullptr;
-  }
-  tflite::OneHotOptionsT *AsOneHotOptions() {
-    return type == BuiltinOptions_OneHotOptions ?
-      reinterpret_cast<tflite::OneHotOptionsT *>(value) : nullptr;
-  }
-  const tflite::OneHotOptionsT *AsOneHotOptions() const {
-    return type == BuiltinOptions_OneHotOptions ?
-      reinterpret_cast<const tflite::OneHotOptionsT *>(value) : nullptr;
-  }
-  tflite::LogicalAndOptionsT *AsLogicalAndOptions() {
-    return type == BuiltinOptions_LogicalAndOptions ?
-      reinterpret_cast<tflite::LogicalAndOptionsT *>(value) : nullptr;
-  }
-  const tflite::LogicalAndOptionsT *AsLogicalAndOptions() const {
-    return type == BuiltinOptions_LogicalAndOptions ?
-      reinterpret_cast<const tflite::LogicalAndOptionsT *>(value) : nullptr;
-  }
-  tflite::LogicalNotOptionsT *AsLogicalNotOptions() {
-    return type == BuiltinOptions_LogicalNotOptions ?
-      reinterpret_cast<tflite::LogicalNotOptionsT *>(value) : nullptr;
-  }
-  const tflite::LogicalNotOptionsT *AsLogicalNotOptions() const {
-    return type == BuiltinOptions_LogicalNotOptions ?
-      reinterpret_cast<const tflite::LogicalNotOptionsT *>(value) : nullptr;
-  }
-  tflite::UnpackOptionsT *AsUnpackOptions() {
-    return type == BuiltinOptions_UnpackOptions ?
-      reinterpret_cast<tflite::UnpackOptionsT *>(value) : nullptr;
-  }
-  const tflite::UnpackOptionsT *AsUnpackOptions() const {
-    return type == BuiltinOptions_UnpackOptions ?
-      reinterpret_cast<const tflite::UnpackOptionsT *>(value) : nullptr;
-  }
-  tflite::FloorDivOptionsT *AsFloorDivOptions() {
-    return type == BuiltinOptions_FloorDivOptions ?
-      reinterpret_cast<tflite::FloorDivOptionsT *>(value) : nullptr;
-  }
-  const tflite::FloorDivOptionsT *AsFloorDivOptions() const {
-    return type == BuiltinOptions_FloorDivOptions ?
-      reinterpret_cast<const tflite::FloorDivOptionsT *>(value) : nullptr;
-  }
-  tflite::SquareOptionsT *AsSquareOptions() {
-    return type == BuiltinOptions_SquareOptions ?
-      reinterpret_cast<tflite::SquareOptionsT *>(value) : nullptr;
-  }
-  const tflite::SquareOptionsT *AsSquareOptions() const {
-    return type == BuiltinOptions_SquareOptions ?
-      reinterpret_cast<const tflite::SquareOptionsT *>(value) : nullptr;
-  }
-  tflite::ZerosLikeOptionsT *AsZerosLikeOptions() {
-    return type == BuiltinOptions_ZerosLikeOptions ?
-      reinterpret_cast<tflite::ZerosLikeOptionsT *>(value) : nullptr;
-  }
-  const tflite::ZerosLikeOptionsT *AsZerosLikeOptions() const {
-    return type == BuiltinOptions_ZerosLikeOptions ?
-      reinterpret_cast<const tflite::ZerosLikeOptionsT *>(value) : nullptr;
-  }
-  tflite::FillOptionsT *AsFillOptions() {
-    return type == BuiltinOptions_FillOptions ?
-      reinterpret_cast<tflite::FillOptionsT *>(value) : nullptr;
-  }
-  const tflite::FillOptionsT *AsFillOptions() const {
-    return type == BuiltinOptions_FillOptions ?
-      reinterpret_cast<const tflite::FillOptionsT *>(value) : nullptr;
-  }
-  tflite::BidirectionalSequenceLSTMOptionsT *AsBidirectionalSequenceLSTMOptions() {
-    return type == BuiltinOptions_BidirectionalSequenceLSTMOptions ?
-      reinterpret_cast<tflite::BidirectionalSequenceLSTMOptionsT *>(value) : nullptr;
-  }
-  const tflite::BidirectionalSequenceLSTMOptionsT *AsBidirectionalSequenceLSTMOptions() const {
-    return type == BuiltinOptions_BidirectionalSequenceLSTMOptions ?
-      reinterpret_cast<const tflite::BidirectionalSequenceLSTMOptionsT *>(value) : nullptr;
-  }
-  tflite::BidirectionalSequenceRNNOptionsT *AsBidirectionalSequenceRNNOptions() {
-    return type == BuiltinOptions_BidirectionalSequenceRNNOptions ?
-      reinterpret_cast<tflite::BidirectionalSequenceRNNOptionsT *>(value) : nullptr;
-  }
-  const tflite::BidirectionalSequenceRNNOptionsT *AsBidirectionalSequenceRNNOptions() const {
-    return type == BuiltinOptions_BidirectionalSequenceRNNOptions ?
-      reinterpret_cast<const tflite::BidirectionalSequenceRNNOptionsT *>(value) : nullptr;
-  }
-  tflite::UnidirectionalSequenceLSTMOptionsT *AsUnidirectionalSequenceLSTMOptions() {
-    return type == BuiltinOptions_UnidirectionalSequenceLSTMOptions ?
-      reinterpret_cast<tflite::UnidirectionalSequenceLSTMOptionsT *>(value) : nullptr;
-  }
-  const tflite::UnidirectionalSequenceLSTMOptionsT *AsUnidirectionalSequenceLSTMOptions() const {
-    return type == BuiltinOptions_UnidirectionalSequenceLSTMOptions ?
-      reinterpret_cast<const tflite::UnidirectionalSequenceLSTMOptionsT *>(value) : nullptr;
-  }
-  tflite::FloorModOptionsT *AsFloorModOptions() {
-    return type == BuiltinOptions_FloorModOptions ?
-      reinterpret_cast<tflite::FloorModOptionsT *>(value) : nullptr;
-  }
-  const tflite::FloorModOptionsT *AsFloorModOptions() const {
-    return type == BuiltinOptions_FloorModOptions ?
-      reinterpret_cast<const tflite::FloorModOptionsT *>(value) : nullptr;
-  }
-  tflite::RangeOptionsT *AsRangeOptions() {
-    return type == BuiltinOptions_RangeOptions ?
-      reinterpret_cast<tflite::RangeOptionsT *>(value) : nullptr;
-  }
-  const tflite::RangeOptionsT *AsRangeOptions() const {
-    return type == BuiltinOptions_RangeOptions ?
-      reinterpret_cast<const tflite::RangeOptionsT *>(value) : nullptr;
-  }
-  tflite::ResizeNearestNeighborOptionsT *AsResizeNearestNeighborOptions() {
-    return type == BuiltinOptions_ResizeNearestNeighborOptions ?
-      reinterpret_cast<tflite::ResizeNearestNeighborOptionsT *>(value) : nullptr;
-  }
-  const tflite::ResizeNearestNeighborOptionsT *AsResizeNearestNeighborOptions() const {
-    return type == BuiltinOptions_ResizeNearestNeighborOptions ?
-      reinterpret_cast<const tflite::ResizeNearestNeighborOptionsT *>(value) : nullptr;
-  }
-  tflite::LeakyReluOptionsT *AsLeakyReluOptions() {
-    return type == BuiltinOptions_LeakyReluOptions ?
-      reinterpret_cast<tflite::LeakyReluOptionsT *>(value) : nullptr;
-  }
-  const tflite::LeakyReluOptionsT *AsLeakyReluOptions() const {
-    return type == BuiltinOptions_LeakyReluOptions ?
-      reinterpret_cast<const tflite::LeakyReluOptionsT *>(value) : nullptr;
-  }
-  tflite::SquaredDifferenceOptionsT *AsSquaredDifferenceOptions() {
-    return type == BuiltinOptions_SquaredDifferenceOptions ?
-      reinterpret_cast<tflite::SquaredDifferenceOptionsT *>(value) : nullptr;
-  }
-  const tflite::SquaredDifferenceOptionsT *AsSquaredDifferenceOptions() const {
-    return type == BuiltinOptions_SquaredDifferenceOptions ?
-      reinterpret_cast<const tflite::SquaredDifferenceOptionsT *>(value) : nullptr;
-  }
-  tflite::MirrorPadOptionsT *AsMirrorPadOptions() {
-    return type == BuiltinOptions_MirrorPadOptions ?
-      reinterpret_cast<tflite::MirrorPadOptionsT *>(value) : nullptr;
-  }
-  const tflite::MirrorPadOptionsT *AsMirrorPadOptions() const {
-    return type == BuiltinOptions_MirrorPadOptions ?
-      reinterpret_cast<const tflite::MirrorPadOptionsT *>(value) : nullptr;
-  }
-  tflite::AbsOptionsT *AsAbsOptions() {
-    return type == BuiltinOptions_AbsOptions ?
-      reinterpret_cast<tflite::AbsOptionsT *>(value) : nullptr;
-  }
-  const tflite::AbsOptionsT *AsAbsOptions() const {
-    return type == BuiltinOptions_AbsOptions ?
-      reinterpret_cast<const tflite::AbsOptionsT *>(value) : nullptr;
-  }
-  tflite::SplitVOptionsT *AsSplitVOptions() {
-    return type == BuiltinOptions_SplitVOptions ?
-      reinterpret_cast<tflite::SplitVOptionsT *>(value) : nullptr;
-  }
-  const tflite::SplitVOptionsT *AsSplitVOptions() const {
-    return type == BuiltinOptions_SplitVOptions ?
-      reinterpret_cast<const tflite::SplitVOptionsT *>(value) : nullptr;
-  }
-  tflite::UniqueOptionsT *AsUniqueOptions() {
-    return type == BuiltinOptions_UniqueOptions ?
-      reinterpret_cast<tflite::UniqueOptionsT *>(value) : nullptr;
-  }
-  const tflite::UniqueOptionsT *AsUniqueOptions() const {
-    return type == BuiltinOptions_UniqueOptions ?
-      reinterpret_cast<const tflite::UniqueOptionsT *>(value) : nullptr;
-  }
-  tflite::ReverseV2OptionsT *AsReverseV2Options() {
-    return type == BuiltinOptions_ReverseV2Options ?
-      reinterpret_cast<tflite::ReverseV2OptionsT *>(value) : nullptr;
-  }
-  const tflite::ReverseV2OptionsT *AsReverseV2Options() const {
-    return type == BuiltinOptions_ReverseV2Options ?
-      reinterpret_cast<const tflite::ReverseV2OptionsT *>(value) : nullptr;
-  }
-  tflite::AddNOptionsT *AsAddNOptions() {
-    return type == BuiltinOptions_AddNOptions ?
-      reinterpret_cast<tflite::AddNOptionsT *>(value) : nullptr;
-  }
-  const tflite::AddNOptionsT *AsAddNOptions() const {
-    return type == BuiltinOptions_AddNOptions ?
-      reinterpret_cast<const tflite::AddNOptionsT *>(value) : nullptr;
-  }
-  tflite::GatherNdOptionsT *AsGatherNdOptions() {
-    return type == BuiltinOptions_GatherNdOptions ?
-      reinterpret_cast<tflite::GatherNdOptionsT *>(value) : nullptr;
-  }
-  const tflite::GatherNdOptionsT *AsGatherNdOptions() const {
-    return type == BuiltinOptions_GatherNdOptions ?
-      reinterpret_cast<const tflite::GatherNdOptionsT *>(value) : nullptr;
-  }
-  tflite::CosOptionsT *AsCosOptions() {
-    return type == BuiltinOptions_CosOptions ?
-      reinterpret_cast<tflite::CosOptionsT *>(value) : nullptr;
-  }
-  const tflite::CosOptionsT *AsCosOptions() const {
-    return type == BuiltinOptions_CosOptions ?
-      reinterpret_cast<const tflite::CosOptionsT *>(value) : nullptr;
-  }
-  tflite::WhereOptionsT *AsWhereOptions() {
-    return type == BuiltinOptions_WhereOptions ?
-      reinterpret_cast<tflite::WhereOptionsT *>(value) : nullptr;
-  }
-  const tflite::WhereOptionsT *AsWhereOptions() const {
-    return type == BuiltinOptions_WhereOptions ?
-      reinterpret_cast<const tflite::WhereOptionsT *>(value) : nullptr;
-  }
-  tflite::RankOptionsT *AsRankOptions() {
-    return type == BuiltinOptions_RankOptions ?
-      reinterpret_cast<tflite::RankOptionsT *>(value) : nullptr;
-  }
-  const tflite::RankOptionsT *AsRankOptions() const {
-    return type == BuiltinOptions_RankOptions ?
-      reinterpret_cast<const tflite::RankOptionsT *>(value) : nullptr;
-  }
-  tflite::ReverseSequenceOptionsT *AsReverseSequenceOptions() {
-    return type == BuiltinOptions_ReverseSequenceOptions ?
-      reinterpret_cast<tflite::ReverseSequenceOptionsT *>(value) : nullptr;
-  }
-  const tflite::ReverseSequenceOptionsT *AsReverseSequenceOptions() const {
-    return type == BuiltinOptions_ReverseSequenceOptions ?
-      reinterpret_cast<const tflite::ReverseSequenceOptionsT *>(value) : nullptr;
-  }
-  tflite::MatrixDiagOptionsT *AsMatrixDiagOptions() {
-    return type == BuiltinOptions_MatrixDiagOptions ?
-      reinterpret_cast<tflite::MatrixDiagOptionsT *>(value) : nullptr;
-  }
-  const tflite::MatrixDiagOptionsT *AsMatrixDiagOptions() const {
-    return type == BuiltinOptions_MatrixDiagOptions ?
-      reinterpret_cast<const tflite::MatrixDiagOptionsT *>(value) : nullptr;
-  }
-  tflite::QuantizeOptionsT *AsQuantizeOptions() {
-    return type == BuiltinOptions_QuantizeOptions ?
-      reinterpret_cast<tflite::QuantizeOptionsT *>(value) : nullptr;
-  }
-  const tflite::QuantizeOptionsT *AsQuantizeOptions() const {
-    return type == BuiltinOptions_QuantizeOptions ?
-      reinterpret_cast<const tflite::QuantizeOptionsT *>(value) : nullptr;
-  }
-  tflite::MatrixSetDiagOptionsT *AsMatrixSetDiagOptions() {
-    return type == BuiltinOptions_MatrixSetDiagOptions ?
-      reinterpret_cast<tflite::MatrixSetDiagOptionsT *>(value) : nullptr;
-  }
-  const tflite::MatrixSetDiagOptionsT *AsMatrixSetDiagOptions() const {
-    return type == BuiltinOptions_MatrixSetDiagOptions ?
-      reinterpret_cast<const tflite::MatrixSetDiagOptionsT *>(value) : nullptr;
-  }
-  tflite::HardSwishOptionsT *AsHardSwishOptions() {
-    return type == BuiltinOptions_HardSwishOptions ?
-      reinterpret_cast<tflite::HardSwishOptionsT *>(value) : nullptr;
-  }
-  const tflite::HardSwishOptionsT *AsHardSwishOptions() const {
-    return type == BuiltinOptions_HardSwishOptions ?
-      reinterpret_cast<const tflite::HardSwishOptionsT *>(value) : nullptr;
-  }
-  tflite::IfOptionsT *AsIfOptions() {
-    return type == BuiltinOptions_IfOptions ?
-      reinterpret_cast<tflite::IfOptionsT *>(value) : nullptr;
-  }
-  const tflite::IfOptionsT *AsIfOptions() const {
-    return type == BuiltinOptions_IfOptions ?
-      reinterpret_cast<const tflite::IfOptionsT *>(value) : nullptr;
-  }
-  tflite::WhileOptionsT *AsWhileOptions() {
-    return type == BuiltinOptions_WhileOptions ?
-      reinterpret_cast<tflite::WhileOptionsT *>(value) : nullptr;
-  }
-  const tflite::WhileOptionsT *AsWhileOptions() const {
-    return type == BuiltinOptions_WhileOptions ?
-      reinterpret_cast<const tflite::WhileOptionsT *>(value) : nullptr;
-  }
-  tflite::DepthToSpaceOptionsT *AsDepthToSpaceOptions() {
-    return type == BuiltinOptions_DepthToSpaceOptions ?
-      reinterpret_cast<tflite::DepthToSpaceOptionsT *>(value) : nullptr;
-  }
-  const tflite::DepthToSpaceOptionsT *AsDepthToSpaceOptions() const {
-    return type == BuiltinOptions_DepthToSpaceOptions ?
-      reinterpret_cast<const tflite::DepthToSpaceOptionsT *>(value) : nullptr;
-  }
-  tflite::NonMaxSuppressionV4OptionsT *AsNonMaxSuppressionV4Options() {
-    return type == BuiltinOptions_NonMaxSuppressionV4Options ?
-      reinterpret_cast<tflite::NonMaxSuppressionV4OptionsT *>(value) : nullptr;
-  }
-  const tflite::NonMaxSuppressionV4OptionsT *AsNonMaxSuppressionV4Options() const {
-    return type == BuiltinOptions_NonMaxSuppressionV4Options ?
-      reinterpret_cast<const tflite::NonMaxSuppressionV4OptionsT *>(value) : nullptr;
-  }
-  tflite::NonMaxSuppressionV5OptionsT *AsNonMaxSuppressionV5Options() {
-    return type == BuiltinOptions_NonMaxSuppressionV5Options ?
-      reinterpret_cast<tflite::NonMaxSuppressionV5OptionsT *>(value) : nullptr;
-  }
-  const tflite::NonMaxSuppressionV5OptionsT *AsNonMaxSuppressionV5Options() const {
-    return type == BuiltinOptions_NonMaxSuppressionV5Options ?
-      reinterpret_cast<const tflite::NonMaxSuppressionV5OptionsT *>(value) : nullptr;
-  }
-  tflite::ScatterNdOptionsT *AsScatterNdOptions() {
-    return type == BuiltinOptions_ScatterNdOptions ?
-      reinterpret_cast<tflite::ScatterNdOptionsT *>(value) : nullptr;
-  }
-  const tflite::ScatterNdOptionsT *AsScatterNdOptions() const {
-    return type == BuiltinOptions_ScatterNdOptions ?
-      reinterpret_cast<const tflite::ScatterNdOptionsT *>(value) : nullptr;
-  }
-  tflite::SelectV2OptionsT *AsSelectV2Options() {
-    return type == BuiltinOptions_SelectV2Options ?
-      reinterpret_cast<tflite::SelectV2OptionsT *>(value) : nullptr;
-  }
-  const tflite::SelectV2OptionsT *AsSelectV2Options() const {
-    return type == BuiltinOptions_SelectV2Options ?
-      reinterpret_cast<const tflite::SelectV2OptionsT *>(value) : nullptr;
-  }
-  tflite::DensifyOptionsT *AsDensifyOptions() {
-    return type == BuiltinOptions_DensifyOptions ?
-      reinterpret_cast<tflite::DensifyOptionsT *>(value) : nullptr;
-  }
-  const tflite::DensifyOptionsT *AsDensifyOptions() const {
-    return type == BuiltinOptions_DensifyOptions ?
-      reinterpret_cast<const tflite::DensifyOptionsT *>(value) : nullptr;
-  }
-  tflite::SegmentSumOptionsT *AsSegmentSumOptions() {
-    return type == BuiltinOptions_SegmentSumOptions ?
-      reinterpret_cast<tflite::SegmentSumOptionsT *>(value) : nullptr;
-  }
-  const tflite::SegmentSumOptionsT *AsSegmentSumOptions() const {
-    return type == BuiltinOptions_SegmentSumOptions ?
-      reinterpret_cast<const tflite::SegmentSumOptionsT *>(value) : nullptr;
-  }
-  tflite::BatchMatMulOptionsT *AsBatchMatMulOptions() {
-    return type == BuiltinOptions_BatchMatMulOptions ?
-      reinterpret_cast<tflite::BatchMatMulOptionsT *>(value) : nullptr;
-  }
-  const tflite::BatchMatMulOptionsT *AsBatchMatMulOptions() const {
-    return type == BuiltinOptions_BatchMatMulOptions ?
-      reinterpret_cast<const tflite::BatchMatMulOptionsT *>(value) : nullptr;
-  }
-  tflite::CumsumOptionsT *AsCumsumOptions() {
-    return type == BuiltinOptions_CumsumOptions ?
-      reinterpret_cast<tflite::CumsumOptionsT *>(value) : nullptr;
-  }
-  const tflite::CumsumOptionsT *AsCumsumOptions() const {
-    return type == BuiltinOptions_CumsumOptions ?
-      reinterpret_cast<const tflite::CumsumOptionsT *>(value) : nullptr;
-  }
-  tflite::CallOnceOptionsT *AsCallOnceOptions() {
-    return type == BuiltinOptions_CallOnceOptions ?
-      reinterpret_cast<tflite::CallOnceOptionsT *>(value) : nullptr;
-  }
-  const tflite::CallOnceOptionsT *AsCallOnceOptions() const {
-    return type == BuiltinOptions_CallOnceOptions ?
-      reinterpret_cast<const tflite::CallOnceOptionsT *>(value) : nullptr;
-  }
-  tflite::BroadcastToOptionsT *AsBroadcastToOptions() {
-    return type == BuiltinOptions_BroadcastToOptions ?
-      reinterpret_cast<tflite::BroadcastToOptionsT *>(value) : nullptr;
-  }
-  const tflite::BroadcastToOptionsT *AsBroadcastToOptions() const {
-    return type == BuiltinOptions_BroadcastToOptions ?
-      reinterpret_cast<const tflite::BroadcastToOptionsT *>(value) : nullptr;
-  }
-  tflite::Rfft2dOptionsT *AsRfft2dOptions() {
-    return type == BuiltinOptions_Rfft2dOptions ?
-      reinterpret_cast<tflite::Rfft2dOptionsT *>(value) : nullptr;
-  }
-  const tflite::Rfft2dOptionsT *AsRfft2dOptions() const {
-    return type == BuiltinOptions_Rfft2dOptions ?
-      reinterpret_cast<const tflite::Rfft2dOptionsT *>(value) : nullptr;
-  }
-  tflite::Conv3DOptionsT *AsConv3DOptions() {
-    return type == BuiltinOptions_Conv3DOptions ?
-      reinterpret_cast<tflite::Conv3DOptionsT *>(value) : nullptr;
-  }
-  const tflite::Conv3DOptionsT *AsConv3DOptions() const {
-    return type == BuiltinOptions_Conv3DOptions ?
-      reinterpret_cast<const tflite::Conv3DOptionsT *>(value) : nullptr;
-  }
-  tflite::HashtableOptionsT *AsHashtableOptions() {
-    return type == BuiltinOptions_HashtableOptions ?
-      reinterpret_cast<tflite::HashtableOptionsT *>(value) : nullptr;
-  }
-  const tflite::HashtableOptionsT *AsHashtableOptions() const {
-    return type == BuiltinOptions_HashtableOptions ?
-      reinterpret_cast<const tflite::HashtableOptionsT *>(value) : nullptr;
-  }
-  tflite::HashtableFindOptionsT *AsHashtableFindOptions() {
-    return type == BuiltinOptions_HashtableFindOptions ?
-      reinterpret_cast<tflite::HashtableFindOptionsT *>(value) : nullptr;
-  }
-  const tflite::HashtableFindOptionsT *AsHashtableFindOptions() const {
-    return type == BuiltinOptions_HashtableFindOptions ?
-      reinterpret_cast<const tflite::HashtableFindOptionsT *>(value) : nullptr;
-  }
-  tflite::HashtableImportOptionsT *AsHashtableImportOptions() {
-    return type == BuiltinOptions_HashtableImportOptions ?
-      reinterpret_cast<tflite::HashtableImportOptionsT *>(value) : nullptr;
-  }
-  const tflite::HashtableImportOptionsT *AsHashtableImportOptions() const {
-    return type == BuiltinOptions_HashtableImportOptions ?
-      reinterpret_cast<const tflite::HashtableImportOptionsT *>(value) : nullptr;
-  }
-  tflite::HashtableSizeOptionsT *AsHashtableSizeOptions() {
-    return type == BuiltinOptions_HashtableSizeOptions ?
-      reinterpret_cast<tflite::HashtableSizeOptionsT *>(value) : nullptr;
-  }
-  const tflite::HashtableSizeOptionsT *AsHashtableSizeOptions() const {
-    return type == BuiltinOptions_HashtableSizeOptions ?
-      reinterpret_cast<const tflite::HashtableSizeOptionsT *>(value) : nullptr;
-  }
-  tflite::VarHandleOptionsT *AsVarHandleOptions() {
-    return type == BuiltinOptions_VarHandleOptions ?
-      reinterpret_cast<tflite::VarHandleOptionsT *>(value) : nullptr;
-  }
-  const tflite::VarHandleOptionsT *AsVarHandleOptions() const {
-    return type == BuiltinOptions_VarHandleOptions ?
-      reinterpret_cast<const tflite::VarHandleOptionsT *>(value) : nullptr;
-  }
-  tflite::ReadVariableOptionsT *AsReadVariableOptions() {
-    return type == BuiltinOptions_ReadVariableOptions ?
-      reinterpret_cast<tflite::ReadVariableOptionsT *>(value) : nullptr;
-  }
-  const tflite::ReadVariableOptionsT *AsReadVariableOptions() const {
-    return type == BuiltinOptions_ReadVariableOptions ?
-      reinterpret_cast<const tflite::ReadVariableOptionsT *>(value) : nullptr;
-  }
-  tflite::AssignVariableOptionsT *AsAssignVariableOptions() {
-    return type == BuiltinOptions_AssignVariableOptions ?
-      reinterpret_cast<tflite::AssignVariableOptionsT *>(value) : nullptr;
-  }
-  const tflite::AssignVariableOptionsT *AsAssignVariableOptions() const {
-    return type == BuiltinOptions_AssignVariableOptions ?
-      reinterpret_cast<const tflite::AssignVariableOptionsT *>(value) : nullptr;
-  }
-  tflite::RandomOptionsT *AsRandomOptions() {
-    return type == BuiltinOptions_RandomOptions ?
-      reinterpret_cast<tflite::RandomOptionsT *>(value) : nullptr;
-  }
-  const tflite::RandomOptionsT *AsRandomOptions() const {
-    return type == BuiltinOptions_RandomOptions ?
-      reinterpret_cast<const tflite::RandomOptionsT *>(value) : nullptr;
-  }
-  tflite::BucketizeOptionsT *AsBucketizeOptions() {
-    return type == BuiltinOptions_BucketizeOptions ?
-      reinterpret_cast<tflite::BucketizeOptionsT *>(value) : nullptr;
-  }
-  const tflite::BucketizeOptionsT *AsBucketizeOptions() const {
-    return type == BuiltinOptions_BucketizeOptions ?
-      reinterpret_cast<const tflite::BucketizeOptionsT *>(value) : nullptr;
-  }
-  tflite::GeluOptionsT *AsGeluOptions() {
-    return type == BuiltinOptions_GeluOptions ?
-      reinterpret_cast<tflite::GeluOptionsT *>(value) : nullptr;
-  }
-  const tflite::GeluOptionsT *AsGeluOptions() const {
-    return type == BuiltinOptions_GeluOptions ?
-      reinterpret_cast<const tflite::GeluOptionsT *>(value) : nullptr;
-  }
-  tflite::DynamicUpdateSliceOptionsT *AsDynamicUpdateSliceOptions() {
-    return type == BuiltinOptions_DynamicUpdateSliceOptions ?
-      reinterpret_cast<tflite::DynamicUpdateSliceOptionsT *>(value) : nullptr;
-  }
-  const tflite::DynamicUpdateSliceOptionsT *AsDynamicUpdateSliceOptions() const {
-    return type == BuiltinOptions_DynamicUpdateSliceOptions ?
-      reinterpret_cast<const tflite::DynamicUpdateSliceOptionsT *>(value) : nullptr;
-  }
-  tflite::UnsortedSegmentProdOptionsT *AsUnsortedSegmentProdOptions() {
-    return type == BuiltinOptions_UnsortedSegmentProdOptions ?
-      reinterpret_cast<tflite::UnsortedSegmentProdOptionsT *>(value) : nullptr;
-  }
-  const tflite::UnsortedSegmentProdOptionsT *AsUnsortedSegmentProdOptions() const {
-    return type == BuiltinOptions_UnsortedSegmentProdOptions ?
-      reinterpret_cast<const tflite::UnsortedSegmentProdOptionsT *>(value) : nullptr;
-  }
-  tflite::UnsortedSegmentMaxOptionsT *AsUnsortedSegmentMaxOptions() {
-    return type == BuiltinOptions_UnsortedSegmentMaxOptions ?
-      reinterpret_cast<tflite::UnsortedSegmentMaxOptionsT *>(value) : nullptr;
-  }
-  const tflite::UnsortedSegmentMaxOptionsT *AsUnsortedSegmentMaxOptions() const {
-    return type == BuiltinOptions_UnsortedSegmentMaxOptions ?
-      reinterpret_cast<const tflite::UnsortedSegmentMaxOptionsT *>(value) : nullptr;
-  }
-  tflite::UnsortedSegmentMinOptionsT *AsUnsortedSegmentMinOptions() {
-    return type == BuiltinOptions_UnsortedSegmentMinOptions ?
-      reinterpret_cast<tflite::UnsortedSegmentMinOptionsT *>(value) : nullptr;
-  }
-  const tflite::UnsortedSegmentMinOptionsT *AsUnsortedSegmentMinOptions() const {
-    return type == BuiltinOptions_UnsortedSegmentMinOptions ?
-      reinterpret_cast<const tflite::UnsortedSegmentMinOptionsT *>(value) : nullptr;
-  }
-  tflite::UnsortedSegmentSumOptionsT *AsUnsortedSegmentSumOptions() {
-    return type == BuiltinOptions_UnsortedSegmentSumOptions ?
-      reinterpret_cast<tflite::UnsortedSegmentSumOptionsT *>(value) : nullptr;
-  }
-  const tflite::UnsortedSegmentSumOptionsT *AsUnsortedSegmentSumOptions() const {
-    return type == BuiltinOptions_UnsortedSegmentSumOptions ?
-      reinterpret_cast<const tflite::UnsortedSegmentSumOptionsT *>(value) : nullptr;
-  }
-  tflite::ATan2OptionsT *AsATan2Options() {
-    return type == BuiltinOptions_ATan2Options ?
-      reinterpret_cast<tflite::ATan2OptionsT *>(value) : nullptr;
-  }
-  const tflite::ATan2OptionsT *AsATan2Options() const {
-    return type == BuiltinOptions_ATan2Options ?
-      reinterpret_cast<const tflite::ATan2OptionsT *>(value) : nullptr;
-  }
-  tflite::SignOptionsT *AsSignOptions() {
-    return type == BuiltinOptions_SignOptions ?
-      reinterpret_cast<tflite::SignOptionsT *>(value) : nullptr;
-  }
-  const tflite::SignOptionsT *AsSignOptions() const {
-    return type == BuiltinOptions_SignOptions ?
-      reinterpret_cast<const tflite::SignOptionsT *>(value) : nullptr;
-  }
-};
-
-bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type);
-bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types);
-
-enum Padding : int8_t {
-  Padding_SAME = 0,
-  Padding_VALID = 1,
-  Padding_MIN = Padding_SAME,
-  Padding_MAX = Padding_VALID
-};
-
-inline const Padding (&EnumValuesPadding())[2] {
-  static const Padding values[] = {
-    Padding_SAME,
-    Padding_VALID
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesPadding() {
-  static const char * const names[3] = {
-    "SAME",
-    "VALID",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNamePadding(Padding e) {
-  if (flatbuffers::IsOutRange(e, Padding_SAME, Padding_VALID)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesPadding()[index];
-}
-
-enum ActivationFunctionType : int8_t {
-  ActivationFunctionType_NONE = 0,
-  ActivationFunctionType_RELU = 1,
-  ActivationFunctionType_RELU_N1_TO_1 = 2,
-  ActivationFunctionType_RELU6 = 3,
-  ActivationFunctionType_TANH = 4,
-  ActivationFunctionType_SIGN_BIT = 5,
-  ActivationFunctionType_MIN = ActivationFunctionType_NONE,
-  ActivationFunctionType_MAX = ActivationFunctionType_SIGN_BIT
-};
-
-inline const ActivationFunctionType (&EnumValuesActivationFunctionType())[6] {
-  static const ActivationFunctionType values[] = {
-    ActivationFunctionType_NONE,
-    ActivationFunctionType_RELU,
-    ActivationFunctionType_RELU_N1_TO_1,
-    ActivationFunctionType_RELU6,
-    ActivationFunctionType_TANH,
-    ActivationFunctionType_SIGN_BIT
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesActivationFunctionType() {
-  static const char * const names[7] = {
-    "NONE",
-    "RELU",
-    "RELU_N1_TO_1",
-    "RELU6",
-    "TANH",
-    "SIGN_BIT",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameActivationFunctionType(ActivationFunctionType e) {
-  if (flatbuffers::IsOutRange(e, ActivationFunctionType_NONE, ActivationFunctionType_SIGN_BIT)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesActivationFunctionType()[index];
-}
-
-enum LSHProjectionType : int8_t {
-  LSHProjectionType_UNKNOWN = 0,
-  LSHProjectionType_SPARSE = 1,
-  LSHProjectionType_DENSE = 2,
-  LSHProjectionType_MIN = LSHProjectionType_UNKNOWN,
-  LSHProjectionType_MAX = LSHProjectionType_DENSE
-};
-
-inline const LSHProjectionType (&EnumValuesLSHProjectionType())[3] {
-  static const LSHProjectionType values[] = {
-    LSHProjectionType_UNKNOWN,
-    LSHProjectionType_SPARSE,
-    LSHProjectionType_DENSE
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesLSHProjectionType() {
-  static const char * const names[4] = {
-    "UNKNOWN",
-    "SPARSE",
-    "DENSE",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameLSHProjectionType(LSHProjectionType e) {
-  if (flatbuffers::IsOutRange(e, LSHProjectionType_UNKNOWN, LSHProjectionType_DENSE)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesLSHProjectionType()[index];
-}
-
-enum FullyConnectedOptionsWeightsFormat : int8_t {
-  FullyConnectedOptionsWeightsFormat_DEFAULT = 0,
-  FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8 = 1,
-  FullyConnectedOptionsWeightsFormat_MIN = FullyConnectedOptionsWeightsFormat_DEFAULT,
-  FullyConnectedOptionsWeightsFormat_MAX = FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8
-};
-
-inline const FullyConnectedOptionsWeightsFormat (&EnumValuesFullyConnectedOptionsWeightsFormat())[2] {
-  static const FullyConnectedOptionsWeightsFormat values[] = {
-    FullyConnectedOptionsWeightsFormat_DEFAULT,
-    FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesFullyConnectedOptionsWeightsFormat() {
-  static const char * const names[3] = {
-    "DEFAULT",
-    "SHUFFLED4x16INT8",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameFullyConnectedOptionsWeightsFormat(FullyConnectedOptionsWeightsFormat e) {
-  if (flatbuffers::IsOutRange(e, FullyConnectedOptionsWeightsFormat_DEFAULT, FullyConnectedOptionsWeightsFormat_SHUFFLED4x16INT8)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesFullyConnectedOptionsWeightsFormat()[index];
-}
-
-enum LSTMKernelType : int8_t {
-  LSTMKernelType_FULL = 0,
-  LSTMKernelType_BASIC = 1,
-  LSTMKernelType_MIN = LSTMKernelType_FULL,
-  LSTMKernelType_MAX = LSTMKernelType_BASIC
-};
-
-inline const LSTMKernelType (&EnumValuesLSTMKernelType())[2] {
-  static const LSTMKernelType values[] = {
-    LSTMKernelType_FULL,
-    LSTMKernelType_BASIC
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesLSTMKernelType() {
-  static const char * const names[3] = {
-    "FULL",
-    "BASIC",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameLSTMKernelType(LSTMKernelType e) {
-  if (flatbuffers::IsOutRange(e, LSTMKernelType_FULL, LSTMKernelType_BASIC)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesLSTMKernelType()[index];
-}
-
-enum CombinerType : int8_t {
-  CombinerType_SUM = 0,
-  CombinerType_MEAN = 1,
-  CombinerType_SQRTN = 2,
-  CombinerType_MIN = CombinerType_SUM,
-  CombinerType_MAX = CombinerType_SQRTN
-};
-
-inline const CombinerType (&EnumValuesCombinerType())[3] {
-  static const CombinerType values[] = {
-    CombinerType_SUM,
-    CombinerType_MEAN,
-    CombinerType_SQRTN
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesCombinerType() {
-  static const char * const names[4] = {
-    "SUM",
-    "MEAN",
-    "SQRTN",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameCombinerType(CombinerType e) {
-  if (flatbuffers::IsOutRange(e, CombinerType_SUM, CombinerType_SQRTN)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesCombinerType()[index];
-}
-
-enum MirrorPadMode : int8_t {
-  MirrorPadMode_REFLECT = 0,
-  MirrorPadMode_SYMMETRIC = 1,
-  MirrorPadMode_MIN = MirrorPadMode_REFLECT,
-  MirrorPadMode_MAX = MirrorPadMode_SYMMETRIC
-};
-
-inline const MirrorPadMode (&EnumValuesMirrorPadMode())[2] {
-  static const MirrorPadMode values[] = {
-    MirrorPadMode_REFLECT,
-    MirrorPadMode_SYMMETRIC
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesMirrorPadMode() {
-  static const char * const names[3] = {
-    "REFLECT",
-    "SYMMETRIC",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameMirrorPadMode(MirrorPadMode e) {
-  if (flatbuffers::IsOutRange(e, MirrorPadMode_REFLECT, MirrorPadMode_SYMMETRIC)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesMirrorPadMode()[index];
-}
-
-enum CustomOptionsFormat : int8_t {
-  CustomOptionsFormat_FLEXBUFFERS = 0,
-  CustomOptionsFormat_MIN = CustomOptionsFormat_FLEXBUFFERS,
-  CustomOptionsFormat_MAX = CustomOptionsFormat_FLEXBUFFERS
-};
-
-inline const CustomOptionsFormat (&EnumValuesCustomOptionsFormat())[1] {
-  static const CustomOptionsFormat values[] = {
-    CustomOptionsFormat_FLEXBUFFERS
-  };
-  return values;
-}
-
-inline const char * const *EnumNamesCustomOptionsFormat() {
-  static const char * const names[2] = {
-    "FLEXBUFFERS",
-    nullptr
-  };
-  return names;
-}
-
-inline const char *EnumNameCustomOptionsFormat(CustomOptionsFormat e) {
-  if (flatbuffers::IsOutRange(e, CustomOptionsFormat_FLEXBUFFERS, CustomOptionsFormat_FLEXBUFFERS)) return "";
-  const size_t index = static_cast<size_t>(e);
-  return EnumNamesCustomOptionsFormat()[index];
-}
-
-struct CustomQuantizationT : public flatbuffers::NativeTable {
-  typedef CustomQuantization TableType;
-  std::vector<uint8_t> custom{};
-};
-
-struct CustomQuantization FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef CustomQuantizationT NativeTableType;
-  typedef CustomQuantizationBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_CUSTOM = 4
-  };
-  const flatbuffers::Vector<uint8_t> *custom() const {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_CUSTOM) &&
-           verifier.VerifyVector(custom()) &&
-           verifier.EndTable();
-  }
-  CustomQuantizationT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(CustomQuantizationT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<CustomQuantization> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct CustomQuantizationBuilder {
-  typedef CustomQuantization Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_custom(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom) {
-    fbb_.AddOffset(CustomQuantization::VT_CUSTOM, custom);
-  }
-  explicit CustomQuantizationBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<CustomQuantization> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<CustomQuantization>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<CustomQuantization> CreateCustomQuantization(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom = 0) {
-  CustomQuantizationBuilder builder_(_fbb);
-  builder_.add_custom(custom);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<CustomQuantization> CreateCustomQuantizationDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<uint8_t> *custom = nullptr) {
-  if (custom) { _fbb.ForceVectorAlignment(custom->size(), sizeof(uint8_t), 16); }
-  auto custom__ = custom ? _fbb.CreateVector<uint8_t>(*custom) : 0;
-  return tflite::CreateCustomQuantization(
-      _fbb,
-      custom__);
-}
-
-flatbuffers::Offset<CustomQuantization> CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct QuantizationParametersT : public flatbuffers::NativeTable {
-  typedef QuantizationParameters TableType;
-  std::vector<float> min{};
-  std::vector<float> max{};
-  std::vector<float> scale{};
-  std::vector<int64_t> zero_point{};
-  tflite::QuantizationDetailsUnion details{};
-  int32_t quantized_dimension = 0;
-};
-
-struct QuantizationParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef QuantizationParametersT NativeTableType;
-  typedef QuantizationParametersBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_MIN = 4,
-    VT_MAX = 6,
-    VT_SCALE = 8,
-    VT_ZERO_POINT = 10,
-    VT_DETAILS_TYPE = 12,
-    VT_DETAILS = 14,
-    VT_QUANTIZED_DIMENSION = 16
-  };
-  const flatbuffers::Vector<float> *min() const {
-    return GetPointer<const flatbuffers::Vector<float> *>(VT_MIN);
-  }
-  const flatbuffers::Vector<float> *max() const {
-    return GetPointer<const flatbuffers::Vector<float> *>(VT_MAX);
-  }
-  const flatbuffers::Vector<float> *scale() const {
-    return GetPointer<const flatbuffers::Vector<float> *>(VT_SCALE);
-  }
-  const flatbuffers::Vector<int64_t> *zero_point() const {
-    return GetPointer<const flatbuffers::Vector<int64_t> *>(VT_ZERO_POINT);
-  }
-  tflite::QuantizationDetails details_type() const {
-    return static_cast<tflite::QuantizationDetails>(GetField<uint8_t>(VT_DETAILS_TYPE, 0));
-  }
-  const void *details() const {
-    return GetPointer<const void *>(VT_DETAILS);
-  }
-  template<typename T> const T *details_as() const;
-  const tflite::CustomQuantization *details_as_CustomQuantization() const {
-    return details_type() == tflite::QuantizationDetails_CustomQuantization ? static_cast<const tflite::CustomQuantization *>(details()) : nullptr;
-  }
-  int32_t quantized_dimension() const {
-    return GetField<int32_t>(VT_QUANTIZED_DIMENSION, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_MIN) &&
-           verifier.VerifyVector(min()) &&
-           VerifyOffset(verifier, VT_MAX) &&
-           verifier.VerifyVector(max()) &&
-           VerifyOffset(verifier, VT_SCALE) &&
-           verifier.VerifyVector(scale()) &&
-           VerifyOffset(verifier, VT_ZERO_POINT) &&
-           verifier.VerifyVector(zero_point()) &&
-           VerifyField<uint8_t>(verifier, VT_DETAILS_TYPE, 1) &&
-           VerifyOffset(verifier, VT_DETAILS) &&
-           VerifyQuantizationDetails(verifier, details(), details_type()) &&
-           VerifyField<int32_t>(verifier, VT_QUANTIZED_DIMENSION, 4) &&
-           verifier.EndTable();
-  }
-  QuantizationParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<QuantizationParameters> Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-template<> inline const tflite::CustomQuantization *QuantizationParameters::details_as<tflite::CustomQuantization>() const {
-  return details_as_CustomQuantization();
-}
-
-struct QuantizationParametersBuilder {
-  typedef QuantizationParameters Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_min(flatbuffers::Offset<flatbuffers::Vector<float>> min) {
-    fbb_.AddOffset(QuantizationParameters::VT_MIN, min);
-  }
-  void add_max(flatbuffers::Offset<flatbuffers::Vector<float>> max) {
-    fbb_.AddOffset(QuantizationParameters::VT_MAX, max);
-  }
-  void add_scale(flatbuffers::Offset<flatbuffers::Vector<float>> scale) {
-    fbb_.AddOffset(QuantizationParameters::VT_SCALE, scale);
-  }
-  void add_zero_point(flatbuffers::Offset<flatbuffers::Vector<int64_t>> zero_point) {
-    fbb_.AddOffset(QuantizationParameters::VT_ZERO_POINT, zero_point);
-  }
-  void add_details_type(tflite::QuantizationDetails details_type) {
-    fbb_.AddElement<uint8_t>(QuantizationParameters::VT_DETAILS_TYPE, static_cast<uint8_t>(details_type), 0);
-  }
-  void add_details(flatbuffers::Offset<void> details) {
-    fbb_.AddOffset(QuantizationParameters::VT_DETAILS, details);
-  }
-  void add_quantized_dimension(int32_t quantized_dimension) {
-    fbb_.AddElement<int32_t>(QuantizationParameters::VT_QUANTIZED_DIMENSION, quantized_dimension, 0);
-  }
-  explicit QuantizationParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<QuantizationParameters> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<QuantizationParameters>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<QuantizationParameters> CreateQuantizationParameters(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<float>> min = 0,
-    flatbuffers::Offset<flatbuffers::Vector<float>> max = 0,
-    flatbuffers::Offset<flatbuffers::Vector<float>> scale = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int64_t>> zero_point = 0,
-    tflite::QuantizationDetails details_type = tflite::QuantizationDetails_NONE,
-    flatbuffers::Offset<void> details = 0,
-    int32_t quantized_dimension = 0) {
-  QuantizationParametersBuilder builder_(_fbb);
-  builder_.add_quantized_dimension(quantized_dimension);
-  builder_.add_details(details);
-  builder_.add_zero_point(zero_point);
-  builder_.add_scale(scale);
-  builder_.add_max(max);
-  builder_.add_min(min);
-  builder_.add_details_type(details_type);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<QuantizationParameters> CreateQuantizationParametersDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<float> *min = nullptr,
-    const std::vector<float> *max = nullptr,
-    const std::vector<float> *scale = nullptr,
-    const std::vector<int64_t> *zero_point = nullptr,
-    tflite::QuantizationDetails details_type = tflite::QuantizationDetails_NONE,
-    flatbuffers::Offset<void> details = 0,
-    int32_t quantized_dimension = 0) {
-  auto min__ = min ? _fbb.CreateVector<float>(*min) : 0;
-  auto max__ = max ? _fbb.CreateVector<float>(*max) : 0;
-  auto scale__ = scale ? _fbb.CreateVector<float>(*scale) : 0;
-  auto zero_point__ = zero_point ? _fbb.CreateVector<int64_t>(*zero_point) : 0;
-  return tflite::CreateQuantizationParameters(
-      _fbb,
-      min__,
-      max__,
-      scale__,
-      zero_point__,
-      details_type,
-      details,
-      quantized_dimension);
-}
-
-flatbuffers::Offset<QuantizationParameters> CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct Int32VectorT : public flatbuffers::NativeTable {
-  typedef Int32Vector TableType;
-  std::vector<int32_t> values{};
-};
-
-struct Int32Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef Int32VectorT NativeTableType;
-  typedef Int32VectorBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_VALUES = 4
-  };
-  const flatbuffers::Vector<int32_t> *values() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_VALUES);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_VALUES) &&
-           verifier.VerifyVector(values()) &&
-           verifier.EndTable();
-  }
-  Int32VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(Int32VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<Int32Vector> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct Int32VectorBuilder {
-  typedef Int32Vector Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_values(flatbuffers::Offset<flatbuffers::Vector<int32_t>> values) {
-    fbb_.AddOffset(Int32Vector::VT_VALUES, values);
-  }
-  explicit Int32VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<Int32Vector> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Int32Vector>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Int32Vector> CreateInt32Vector(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> values = 0) {
-  Int32VectorBuilder builder_(_fbb);
-  builder_.add_values(values);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Int32Vector> CreateInt32VectorDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<int32_t> *values = nullptr) {
-  auto values__ = values ? _fbb.CreateVector<int32_t>(*values) : 0;
-  return tflite::CreateInt32Vector(
-      _fbb,
-      values__);
-}
-
-flatbuffers::Offset<Int32Vector> CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct Uint16VectorT : public flatbuffers::NativeTable {
-  typedef Uint16Vector TableType;
-  std::vector<uint16_t> values{};
-};
-
-struct Uint16Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef Uint16VectorT NativeTableType;
-  typedef Uint16VectorBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_VALUES = 4
-  };
-  const flatbuffers::Vector<uint16_t> *values() const {
-    return GetPointer<const flatbuffers::Vector<uint16_t> *>(VT_VALUES);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_VALUES) &&
-           verifier.VerifyVector(values()) &&
-           verifier.EndTable();
-  }
-  Uint16VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(Uint16VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<Uint16Vector> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct Uint16VectorBuilder {
-  typedef Uint16Vector Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_values(flatbuffers::Offset<flatbuffers::Vector<uint16_t>> values) {
-    fbb_.AddOffset(Uint16Vector::VT_VALUES, values);
-  }
-  explicit Uint16VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<Uint16Vector> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Uint16Vector>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Uint16Vector> CreateUint16Vector(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<uint16_t>> values = 0) {
-  Uint16VectorBuilder builder_(_fbb);
-  builder_.add_values(values);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Uint16Vector> CreateUint16VectorDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<uint16_t> *values = nullptr) {
-  if (values) { _fbb.ForceVectorAlignment(values->size(), sizeof(uint16_t), 4); }
-  auto values__ = values ? _fbb.CreateVector<uint16_t>(*values) : 0;
-  return tflite::CreateUint16Vector(
-      _fbb,
-      values__);
-}
-
-flatbuffers::Offset<Uint16Vector> CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct Uint8VectorT : public flatbuffers::NativeTable {
-  typedef Uint8Vector TableType;
-  std::vector<uint8_t> values{};
-};
-
-struct Uint8Vector FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef Uint8VectorT NativeTableType;
-  typedef Uint8VectorBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_VALUES = 4
-  };
-  const flatbuffers::Vector<uint8_t> *values() const {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_VALUES);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_VALUES) &&
-           verifier.VerifyVector(values()) &&
-           verifier.EndTable();
-  }
-  Uint8VectorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(Uint8VectorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<Uint8Vector> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct Uint8VectorBuilder {
-  typedef Uint8Vector Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_values(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> values) {
-    fbb_.AddOffset(Uint8Vector::VT_VALUES, values);
-  }
-  explicit Uint8VectorBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<Uint8Vector> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Uint8Vector>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Uint8Vector> CreateUint8Vector(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<uint8_t>> values = 0) {
-  Uint8VectorBuilder builder_(_fbb);
-  builder_.add_values(values);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Uint8Vector> CreateUint8VectorDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<uint8_t> *values = nullptr) {
-  if (values) { _fbb.ForceVectorAlignment(values->size(), sizeof(uint8_t), 4); }
-  auto values__ = values ? _fbb.CreateVector<uint8_t>(*values) : 0;
-  return tflite::CreateUint8Vector(
-      _fbb,
-      values__);
-}
-
-flatbuffers::Offset<Uint8Vector> CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct DimensionMetadataT : public flatbuffers::NativeTable {
-  typedef DimensionMetadata TableType;
-  tflite::DimensionType format = tflite::DimensionType_DENSE;
-  int32_t dense_size = 0;
-  tflite::SparseIndexVectorUnion array_segments{};
-  tflite::SparseIndexVectorUnion array_indices{};
-};
-
-struct DimensionMetadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef DimensionMetadataT NativeTableType;
-  typedef DimensionMetadataBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_FORMAT = 4,
-    VT_DENSE_SIZE = 6,
-    VT_ARRAY_SEGMENTS_TYPE = 8,
-    VT_ARRAY_SEGMENTS = 10,
-    VT_ARRAY_INDICES_TYPE = 12,
-    VT_ARRAY_INDICES = 14
-  };
-  tflite::DimensionType format() const {
-    return static_cast<tflite::DimensionType>(GetField<int8_t>(VT_FORMAT, 0));
-  }
-  int32_t dense_size() const {
-    return GetField<int32_t>(VT_DENSE_SIZE, 0);
-  }
-  tflite::SparseIndexVector array_segments_type() const {
-    return static_cast<tflite::SparseIndexVector>(GetField<uint8_t>(VT_ARRAY_SEGMENTS_TYPE, 0));
-  }
-  const void *array_segments() const {
-    return GetPointer<const void *>(VT_ARRAY_SEGMENTS);
-  }
-  template<typename T> const T *array_segments_as() const;
-  const tflite::Int32Vector *array_segments_as_Int32Vector() const {
-    return array_segments_type() == tflite::SparseIndexVector_Int32Vector ? static_cast<const tflite::Int32Vector *>(array_segments()) : nullptr;
-  }
-  const tflite::Uint16Vector *array_segments_as_Uint16Vector() const {
-    return array_segments_type() == tflite::SparseIndexVector_Uint16Vector ? static_cast<const tflite::Uint16Vector *>(array_segments()) : nullptr;
-  }
-  const tflite::Uint8Vector *array_segments_as_Uint8Vector() const {
-    return array_segments_type() == tflite::SparseIndexVector_Uint8Vector ? static_cast<const tflite::Uint8Vector *>(array_segments()) : nullptr;
-  }
-  tflite::SparseIndexVector array_indices_type() const {
-    return static_cast<tflite::SparseIndexVector>(GetField<uint8_t>(VT_ARRAY_INDICES_TYPE, 0));
-  }
-  const void *array_indices() const {
-    return GetPointer<const void *>(VT_ARRAY_INDICES);
-  }
-  template<typename T> const T *array_indices_as() const;
-  const tflite::Int32Vector *array_indices_as_Int32Vector() const {
-    return array_indices_type() == tflite::SparseIndexVector_Int32Vector ? static_cast<const tflite::Int32Vector *>(array_indices()) : nullptr;
-  }
-  const tflite::Uint16Vector *array_indices_as_Uint16Vector() const {
-    return array_indices_type() == tflite::SparseIndexVector_Uint16Vector ? static_cast<const tflite::Uint16Vector *>(array_indices()) : nullptr;
-  }
-  const tflite::Uint8Vector *array_indices_as_Uint8Vector() const {
-    return array_indices_type() == tflite::SparseIndexVector_Uint8Vector ? static_cast<const tflite::Uint8Vector *>(array_indices()) : nullptr;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FORMAT, 1) &&
-           VerifyField<int32_t>(verifier, VT_DENSE_SIZE, 4) &&
-           VerifyField<uint8_t>(verifier, VT_ARRAY_SEGMENTS_TYPE, 1) &&
-           VerifyOffset(verifier, VT_ARRAY_SEGMENTS) &&
-           VerifySparseIndexVector(verifier, array_segments(), array_segments_type()) &&
-           VerifyField<uint8_t>(verifier, VT_ARRAY_INDICES_TYPE, 1) &&
-           VerifyOffset(verifier, VT_ARRAY_INDICES) &&
-           VerifySparseIndexVector(verifier, array_indices(), array_indices_type()) &&
-           verifier.EndTable();
-  }
-  DimensionMetadataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(DimensionMetadataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<DimensionMetadata> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-template<> inline const tflite::Int32Vector *DimensionMetadata::array_segments_as<tflite::Int32Vector>() const {
-  return array_segments_as_Int32Vector();
-}
-
-template<> inline const tflite::Uint16Vector *DimensionMetadata::array_segments_as<tflite::Uint16Vector>() const {
-  return array_segments_as_Uint16Vector();
-}
-
-template<> inline const tflite::Uint8Vector *DimensionMetadata::array_segments_as<tflite::Uint8Vector>() const {
-  return array_segments_as_Uint8Vector();
-}
-
-template<> inline const tflite::Int32Vector *DimensionMetadata::array_indices_as<tflite::Int32Vector>() const {
-  return array_indices_as_Int32Vector();
-}
-
-template<> inline const tflite::Uint16Vector *DimensionMetadata::array_indices_as<tflite::Uint16Vector>() const {
-  return array_indices_as_Uint16Vector();
-}
-
-template<> inline const tflite::Uint8Vector *DimensionMetadata::array_indices_as<tflite::Uint8Vector>() const {
-  return array_indices_as_Uint8Vector();
-}
-
-struct DimensionMetadataBuilder {
-  typedef DimensionMetadata Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_format(tflite::DimensionType format) {
-    fbb_.AddElement<int8_t>(DimensionMetadata::VT_FORMAT, static_cast<int8_t>(format), 0);
-  }
-  void add_dense_size(int32_t dense_size) {
-    fbb_.AddElement<int32_t>(DimensionMetadata::VT_DENSE_SIZE, dense_size, 0);
-  }
-  void add_array_segments_type(tflite::SparseIndexVector array_segments_type) {
-    fbb_.AddElement<uint8_t>(DimensionMetadata::VT_ARRAY_SEGMENTS_TYPE, static_cast<uint8_t>(array_segments_type), 0);
-  }
-  void add_array_segments(flatbuffers::Offset<void> array_segments) {
-    fbb_.AddOffset(DimensionMetadata::VT_ARRAY_SEGMENTS, array_segments);
-  }
-  void add_array_indices_type(tflite::SparseIndexVector array_indices_type) {
-    fbb_.AddElement<uint8_t>(DimensionMetadata::VT_ARRAY_INDICES_TYPE, static_cast<uint8_t>(array_indices_type), 0);
-  }
-  void add_array_indices(flatbuffers::Offset<void> array_indices) {
-    fbb_.AddOffset(DimensionMetadata::VT_ARRAY_INDICES, array_indices);
-  }
-  explicit DimensionMetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<DimensionMetadata> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DimensionMetadata>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DimensionMetadata> CreateDimensionMetadata(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::DimensionType format = tflite::DimensionType_DENSE,
-    int32_t dense_size = 0,
-    tflite::SparseIndexVector array_segments_type = tflite::SparseIndexVector_NONE,
-    flatbuffers::Offset<void> array_segments = 0,
-    tflite::SparseIndexVector array_indices_type = tflite::SparseIndexVector_NONE,
-    flatbuffers::Offset<void> array_indices = 0) {
-  DimensionMetadataBuilder builder_(_fbb);
-  builder_.add_array_indices(array_indices);
-  builder_.add_array_segments(array_segments);
-  builder_.add_dense_size(dense_size);
-  builder_.add_array_indices_type(array_indices_type);
-  builder_.add_array_segments_type(array_segments_type);
-  builder_.add_format(format);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<DimensionMetadata> CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SparsityParametersT : public flatbuffers::NativeTable {
-  typedef SparsityParameters TableType;
-  std::vector<int32_t> traversal_order{};
-  std::vector<int32_t> block_map{};
-  std::vector<std::unique_ptr<tflite::DimensionMetadataT>> dim_metadata{};
-  SparsityParametersT() = default;
-  SparsityParametersT(const SparsityParametersT &o);
-  SparsityParametersT(SparsityParametersT&&) FLATBUFFERS_NOEXCEPT = default;
-  SparsityParametersT &operator=(SparsityParametersT o) FLATBUFFERS_NOEXCEPT;
-};
-
-struct SparsityParameters FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SparsityParametersT NativeTableType;
-  typedef SparsityParametersBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_TRAVERSAL_ORDER = 4,
-    VT_BLOCK_MAP = 6,
-    VT_DIM_METADATA = 8
-  };
-  const flatbuffers::Vector<int32_t> *traversal_order() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_TRAVERSAL_ORDER);
-  }
-  const flatbuffers::Vector<int32_t> *block_map() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_BLOCK_MAP);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<tflite::DimensionMetadata>> *dim_metadata() const {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::DimensionMetadata>> *>(VT_DIM_METADATA);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_TRAVERSAL_ORDER) &&
-           verifier.VerifyVector(traversal_order()) &&
-           VerifyOffset(verifier, VT_BLOCK_MAP) &&
-           verifier.VerifyVector(block_map()) &&
-           VerifyOffset(verifier, VT_DIM_METADATA) &&
-           verifier.VerifyVector(dim_metadata()) &&
-           verifier.VerifyVectorOfTables(dim_metadata()) &&
-           verifier.EndTable();
-  }
-  SparsityParametersT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SparsityParametersT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SparsityParameters> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SparsityParametersBuilder {
-  typedef SparsityParameters Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_traversal_order(flatbuffers::Offset<flatbuffers::Vector<int32_t>> traversal_order) {
-    fbb_.AddOffset(SparsityParameters::VT_TRAVERSAL_ORDER, traversal_order);
-  }
-  void add_block_map(flatbuffers::Offset<flatbuffers::Vector<int32_t>> block_map) {
-    fbb_.AddOffset(SparsityParameters::VT_BLOCK_MAP, block_map);
-  }
-  void add_dim_metadata(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::DimensionMetadata>>> dim_metadata) {
-    fbb_.AddOffset(SparsityParameters::VT_DIM_METADATA, dim_metadata);
-  }
-  explicit SparsityParametersBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SparsityParameters> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SparsityParameters>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SparsityParameters> CreateSparsityParameters(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> traversal_order = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> block_map = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::DimensionMetadata>>> dim_metadata = 0) {
-  SparsityParametersBuilder builder_(_fbb);
-  builder_.add_dim_metadata(dim_metadata);
-  builder_.add_block_map(block_map);
-  builder_.add_traversal_order(traversal_order);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<SparsityParameters> CreateSparsityParametersDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<int32_t> *traversal_order = nullptr,
-    const std::vector<int32_t> *block_map = nullptr,
-    const std::vector<flatbuffers::Offset<tflite::DimensionMetadata>> *dim_metadata = nullptr) {
-  auto traversal_order__ = traversal_order ? _fbb.CreateVector<int32_t>(*traversal_order) : 0;
-  auto block_map__ = block_map ? _fbb.CreateVector<int32_t>(*block_map) : 0;
-  auto dim_metadata__ = dim_metadata ? _fbb.CreateVector<flatbuffers::Offset<tflite::DimensionMetadata>>(*dim_metadata) : 0;
-  return tflite::CreateSparsityParameters(
-      _fbb,
-      traversal_order__,
-      block_map__,
-      dim_metadata__);
-}
-
-flatbuffers::Offset<SparsityParameters> CreateSparsityParameters(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct VariantSubTypeT : public flatbuffers::NativeTable {
-  typedef VariantSubType TableType;
-  std::vector<int32_t> shape{};
-  tflite::TensorType type = tflite::TensorType_FLOAT32;
-  bool has_rank = false;
-};
-
-struct VariantSubType FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef VariantSubTypeT NativeTableType;
-  typedef VariantSubTypeBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_SHAPE = 4,
-    VT_TYPE = 6,
-    VT_HAS_RANK = 8
-  };
-  const flatbuffers::Vector<int32_t> *shape() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE);
-  }
-  tflite::TensorType type() const {
-    return static_cast<tflite::TensorType>(GetField<int8_t>(VT_TYPE, 0));
-  }
-  bool has_rank() const {
-    return GetField<uint8_t>(VT_HAS_RANK, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_SHAPE) &&
-           verifier.VerifyVector(shape()) &&
-           VerifyField<int8_t>(verifier, VT_TYPE, 1) &&
-           VerifyField<uint8_t>(verifier, VT_HAS_RANK, 1) &&
-           verifier.EndTable();
-  }
-  VariantSubTypeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(VariantSubTypeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<VariantSubType> Pack(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct VariantSubTypeBuilder {
-  typedef VariantSubType Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape) {
-    fbb_.AddOffset(VariantSubType::VT_SHAPE, shape);
-  }
-  void add_type(tflite::TensorType type) {
-    fbb_.AddElement<int8_t>(VariantSubType::VT_TYPE, static_cast<int8_t>(type), 0);
-  }
-  void add_has_rank(bool has_rank) {
-    fbb_.AddElement<uint8_t>(VariantSubType::VT_HAS_RANK, static_cast<uint8_t>(has_rank), 0);
-  }
-  explicit VariantSubTypeBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<VariantSubType> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<VariantSubType>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<VariantSubType> CreateVariantSubType(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape = 0,
-    tflite::TensorType type = tflite::TensorType_FLOAT32,
-    bool has_rank = false) {
-  VariantSubTypeBuilder builder_(_fbb);
-  builder_.add_shape(shape);
-  builder_.add_has_rank(has_rank);
-  builder_.add_type(type);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<VariantSubType> CreateVariantSubTypeDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<int32_t> *shape = nullptr,
-    tflite::TensorType type = tflite::TensorType_FLOAT32,
-    bool has_rank = false) {
-  auto shape__ = shape ? _fbb.CreateVector<int32_t>(*shape) : 0;
-  return tflite::CreateVariantSubType(
-      _fbb,
-      shape__,
-      type,
-      has_rank);
-}
-
-flatbuffers::Offset<VariantSubType> CreateVariantSubType(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct TensorT : public flatbuffers::NativeTable {
-  typedef Tensor TableType;
-  std::vector<int32_t> shape{};
-  tflite::TensorType type = tflite::TensorType_FLOAT32;
-  uint32_t buffer = 0;
-  std::string name{};
-  std::unique_ptr<tflite::QuantizationParametersT> quantization{};
-  bool is_variable = false;
-  std::unique_ptr<tflite::SparsityParametersT> sparsity{};
-  std::vector<int32_t> shape_signature{};
-  bool has_rank = false;
-  std::vector<std::unique_ptr<tflite::VariantSubTypeT>> variant_tensors{};
-  TensorT() = default;
-  TensorT(const TensorT &o);
-  TensorT(TensorT&&) FLATBUFFERS_NOEXCEPT = default;
-  TensorT &operator=(TensorT o) FLATBUFFERS_NOEXCEPT;
-};
-
-struct Tensor FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef TensorT NativeTableType;
-  typedef TensorBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_SHAPE = 4,
-    VT_TYPE = 6,
-    VT_BUFFER = 8,
-    VT_NAME = 10,
-    VT_QUANTIZATION = 12,
-    VT_IS_VARIABLE = 14,
-    VT_SPARSITY = 16,
-    VT_SHAPE_SIGNATURE = 18,
-    VT_HAS_RANK = 20,
-    VT_VARIANT_TENSORS = 22
-  };
-  const flatbuffers::Vector<int32_t> *shape() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE);
-  }
-  tflite::TensorType type() const {
-    return static_cast<tflite::TensorType>(GetField<int8_t>(VT_TYPE, 0));
-  }
-  uint32_t buffer() const {
-    return GetField<uint32_t>(VT_BUFFER, 0);
-  }
-  const flatbuffers::String *name() const {
-    return GetPointer<const flatbuffers::String *>(VT_NAME);
-  }
-  const tflite::QuantizationParameters *quantization() const {
-    return GetPointer<const tflite::QuantizationParameters *>(VT_QUANTIZATION);
-  }
-  bool is_variable() const {
-    return GetField<uint8_t>(VT_IS_VARIABLE, 0) != 0;
-  }
-  const tflite::SparsityParameters *sparsity() const {
-    return GetPointer<const tflite::SparsityParameters *>(VT_SPARSITY);
-  }
-  const flatbuffers::Vector<int32_t> *shape_signature() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SHAPE_SIGNATURE);
-  }
-  bool has_rank() const {
-    return GetField<uint8_t>(VT_HAS_RANK, 0) != 0;
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<tflite::VariantSubType>> *variant_tensors() const {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::VariantSubType>> *>(VT_VARIANT_TENSORS);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_SHAPE) &&
-           verifier.VerifyVector(shape()) &&
-           VerifyField<int8_t>(verifier, VT_TYPE, 1) &&
-           VerifyField<uint32_t>(verifier, VT_BUFFER, 4) &&
-           VerifyOffset(verifier, VT_NAME) &&
-           verifier.VerifyString(name()) &&
-           VerifyOffset(verifier, VT_QUANTIZATION) &&
-           verifier.VerifyTable(quantization()) &&
-           VerifyField<uint8_t>(verifier, VT_IS_VARIABLE, 1) &&
-           VerifyOffset(verifier, VT_SPARSITY) &&
-           verifier.VerifyTable(sparsity()) &&
-           VerifyOffset(verifier, VT_SHAPE_SIGNATURE) &&
-           verifier.VerifyVector(shape_signature()) &&
-           VerifyField<uint8_t>(verifier, VT_HAS_RANK, 1) &&
-           VerifyOffset(verifier, VT_VARIANT_TENSORS) &&
-           verifier.VerifyVector(variant_tensors()) &&
-           verifier.VerifyVectorOfTables(variant_tensors()) &&
-           verifier.EndTable();
-  }
-  TensorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<Tensor> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct TensorBuilder {
-  typedef Tensor Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape) {
-    fbb_.AddOffset(Tensor::VT_SHAPE, shape);
-  }
-  void add_type(tflite::TensorType type) {
-    fbb_.AddElement<int8_t>(Tensor::VT_TYPE, static_cast<int8_t>(type), 0);
-  }
-  void add_buffer(uint32_t buffer) {
-    fbb_.AddElement<uint32_t>(Tensor::VT_BUFFER, buffer, 0);
-  }
-  void add_name(flatbuffers::Offset<flatbuffers::String> name) {
-    fbb_.AddOffset(Tensor::VT_NAME, name);
-  }
-  void add_quantization(flatbuffers::Offset<tflite::QuantizationParameters> quantization) {
-    fbb_.AddOffset(Tensor::VT_QUANTIZATION, quantization);
-  }
-  void add_is_variable(bool is_variable) {
-    fbb_.AddElement<uint8_t>(Tensor::VT_IS_VARIABLE, static_cast<uint8_t>(is_variable), 0);
-  }
-  void add_sparsity(flatbuffers::Offset<tflite::SparsityParameters> sparsity) {
-    fbb_.AddOffset(Tensor::VT_SPARSITY, sparsity);
-  }
-  void add_shape_signature(flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape_signature) {
-    fbb_.AddOffset(Tensor::VT_SHAPE_SIGNATURE, shape_signature);
-  }
-  void add_has_rank(bool has_rank) {
-    fbb_.AddElement<uint8_t>(Tensor::VT_HAS_RANK, static_cast<uint8_t>(has_rank), 0);
-  }
-  void add_variant_tensors(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::VariantSubType>>> variant_tensors) {
-    fbb_.AddOffset(Tensor::VT_VARIANT_TENSORS, variant_tensors);
-  }
-  explicit TensorBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<Tensor> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Tensor>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Tensor> CreateTensor(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape = 0,
-    tflite::TensorType type = tflite::TensorType_FLOAT32,
-    uint32_t buffer = 0,
-    flatbuffers::Offset<flatbuffers::String> name = 0,
-    flatbuffers::Offset<tflite::QuantizationParameters> quantization = 0,
-    bool is_variable = false,
-    flatbuffers::Offset<tflite::SparsityParameters> sparsity = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> shape_signature = 0,
-    bool has_rank = false,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::VariantSubType>>> variant_tensors = 0) {
-  TensorBuilder builder_(_fbb);
-  builder_.add_variant_tensors(variant_tensors);
-  builder_.add_shape_signature(shape_signature);
-  builder_.add_sparsity(sparsity);
-  builder_.add_quantization(quantization);
-  builder_.add_name(name);
-  builder_.add_buffer(buffer);
-  builder_.add_shape(shape);
-  builder_.add_has_rank(has_rank);
-  builder_.add_is_variable(is_variable);
-  builder_.add_type(type);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Tensor> CreateTensorDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<int32_t> *shape = nullptr,
-    tflite::TensorType type = tflite::TensorType_FLOAT32,
-    uint32_t buffer = 0,
-    const char *name = nullptr,
-    flatbuffers::Offset<tflite::QuantizationParameters> quantization = 0,
-    bool is_variable = false,
-    flatbuffers::Offset<tflite::SparsityParameters> sparsity = 0,
-    const std::vector<int32_t> *shape_signature = nullptr,
-    bool has_rank = false,
-    const std::vector<flatbuffers::Offset<tflite::VariantSubType>> *variant_tensors = nullptr) {
-  auto shape__ = shape ? _fbb.CreateVector<int32_t>(*shape) : 0;
-  auto name__ = name ? _fbb.CreateString(name) : 0;
-  auto shape_signature__ = shape_signature ? _fbb.CreateVector<int32_t>(*shape_signature) : 0;
-  auto variant_tensors__ = variant_tensors ? _fbb.CreateVector<flatbuffers::Offset<tflite::VariantSubType>>(*variant_tensors) : 0;
-  return tflite::CreateTensor(
-      _fbb,
-      shape__,
-      type,
-      buffer,
-      name__,
-      quantization,
-      is_variable,
-      sparsity,
-      shape_signature__,
-      has_rank,
-      variant_tensors__);
-}
-
-flatbuffers::Offset<Tensor> CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct Conv2DOptionsT : public flatbuffers::NativeTable {
-  typedef Conv2DOptions TableType;
-  tflite::Padding padding = tflite::Padding_SAME;
-  int32_t stride_w = 0;
-  int32_t stride_h = 0;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  int32_t dilation_w_factor = 1;
-  int32_t dilation_h_factor = 1;
-};
-
-struct Conv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef Conv2DOptionsT NativeTableType;
-  typedef Conv2DOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_PADDING = 4,
-    VT_STRIDE_W = 6,
-    VT_STRIDE_H = 8,
-    VT_FUSED_ACTIVATION_FUNCTION = 10,
-    VT_DILATION_W_FACTOR = 12,
-    VT_DILATION_H_FACTOR = 14
-  };
-  tflite::Padding padding() const {
-    return static_cast<tflite::Padding>(GetField<int8_t>(VT_PADDING, 0));
-  }
-  int32_t stride_w() const {
-    return GetField<int32_t>(VT_STRIDE_W, 0);
-  }
-  int32_t stride_h() const {
-    return GetField<int32_t>(VT_STRIDE_H, 0);
-  }
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  int32_t dilation_w_factor() const {
-    return GetField<int32_t>(VT_DILATION_W_FACTOR, 1);
-  }
-  int32_t dilation_h_factor() const {
-    return GetField<int32_t>(VT_DILATION_H_FACTOR, 1);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_PADDING, 1) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W, 4) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H, 4) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_W_FACTOR, 4) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR, 4) &&
-           verifier.EndTable();
-  }
-  Conv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<Conv2DOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct Conv2DOptionsBuilder {
-  typedef Conv2DOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(tflite::Padding padding) {
-    fbb_.AddElement<int8_t>(Conv2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_w(int32_t stride_w) {
-    fbb_.AddElement<int32_t>(Conv2DOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h) {
-    fbb_.AddElement<int32_t>(Conv2DOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(Conv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_dilation_w_factor(int32_t dilation_w_factor) {
-    fbb_.AddElement<int32_t>(Conv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1);
-  }
-  void add_dilation_h_factor(int32_t dilation_h_factor) {
-    fbb_.AddElement<int32_t>(Conv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1);
-  }
-  explicit Conv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<Conv2DOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Conv2DOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Conv2DOptions> CreateConv2DOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::Padding padding = tflite::Padding_SAME,
-    int32_t stride_w = 0,
-    int32_t stride_h = 0,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    int32_t dilation_w_factor = 1,
-    int32_t dilation_h_factor = 1) {
-  Conv2DOptionsBuilder builder_(_fbb);
-  builder_.add_dilation_h_factor(dilation_h_factor);
-  builder_.add_dilation_w_factor(dilation_w_factor);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<Conv2DOptions> CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct Conv3DOptionsT : public flatbuffers::NativeTable {
-  typedef Conv3DOptions TableType;
-  tflite::Padding padding = tflite::Padding_SAME;
-  int32_t stride_d = 0;
-  int32_t stride_w = 0;
-  int32_t stride_h = 0;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  int32_t dilation_d_factor = 1;
-  int32_t dilation_w_factor = 1;
-  int32_t dilation_h_factor = 1;
-};
-
-struct Conv3DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef Conv3DOptionsT NativeTableType;
-  typedef Conv3DOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_PADDING = 4,
-    VT_STRIDE_D = 6,
-    VT_STRIDE_W = 8,
-    VT_STRIDE_H = 10,
-    VT_FUSED_ACTIVATION_FUNCTION = 12,
-    VT_DILATION_D_FACTOR = 14,
-    VT_DILATION_W_FACTOR = 16,
-    VT_DILATION_H_FACTOR = 18
-  };
-  tflite::Padding padding() const {
-    return static_cast<tflite::Padding>(GetField<int8_t>(VT_PADDING, 0));
-  }
-  int32_t stride_d() const {
-    return GetField<int32_t>(VT_STRIDE_D, 0);
-  }
-  int32_t stride_w() const {
-    return GetField<int32_t>(VT_STRIDE_W, 0);
-  }
-  int32_t stride_h() const {
-    return GetField<int32_t>(VT_STRIDE_H, 0);
-  }
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  int32_t dilation_d_factor() const {
-    return GetField<int32_t>(VT_DILATION_D_FACTOR, 1);
-  }
-  int32_t dilation_w_factor() const {
-    return GetField<int32_t>(VT_DILATION_W_FACTOR, 1);
-  }
-  int32_t dilation_h_factor() const {
-    return GetField<int32_t>(VT_DILATION_H_FACTOR, 1);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_PADDING, 1) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_D, 4) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W, 4) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H, 4) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_D_FACTOR, 4) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_W_FACTOR, 4) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR, 4) &&
-           verifier.EndTable();
-  }
-  Conv3DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(Conv3DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<Conv3DOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct Conv3DOptionsBuilder {
-  typedef Conv3DOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(tflite::Padding padding) {
-    fbb_.AddElement<int8_t>(Conv3DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_d(int32_t stride_d) {
-    fbb_.AddElement<int32_t>(Conv3DOptions::VT_STRIDE_D, stride_d, 0);
-  }
-  void add_stride_w(int32_t stride_w) {
-    fbb_.AddElement<int32_t>(Conv3DOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h) {
-    fbb_.AddElement<int32_t>(Conv3DOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(Conv3DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_dilation_d_factor(int32_t dilation_d_factor) {
-    fbb_.AddElement<int32_t>(Conv3DOptions::VT_DILATION_D_FACTOR, dilation_d_factor, 1);
-  }
-  void add_dilation_w_factor(int32_t dilation_w_factor) {
-    fbb_.AddElement<int32_t>(Conv3DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1);
-  }
-  void add_dilation_h_factor(int32_t dilation_h_factor) {
-    fbb_.AddElement<int32_t>(Conv3DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1);
-  }
-  explicit Conv3DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<Conv3DOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Conv3DOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Conv3DOptions> CreateConv3DOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::Padding padding = tflite::Padding_SAME,
-    int32_t stride_d = 0,
-    int32_t stride_w = 0,
-    int32_t stride_h = 0,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    int32_t dilation_d_factor = 1,
-    int32_t dilation_w_factor = 1,
-    int32_t dilation_h_factor = 1) {
-  Conv3DOptionsBuilder builder_(_fbb);
-  builder_.add_dilation_h_factor(dilation_h_factor);
-  builder_.add_dilation_w_factor(dilation_w_factor);
-  builder_.add_dilation_d_factor(dilation_d_factor);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_stride_d(stride_d);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<Conv3DOptions> CreateConv3DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct Pool2DOptionsT : public flatbuffers::NativeTable {
-  typedef Pool2DOptions TableType;
-  tflite::Padding padding = tflite::Padding_SAME;
-  int32_t stride_w = 0;
-  int32_t stride_h = 0;
-  int32_t filter_width = 0;
-  int32_t filter_height = 0;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-};
-
-struct Pool2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef Pool2DOptionsT NativeTableType;
-  typedef Pool2DOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_PADDING = 4,
-    VT_STRIDE_W = 6,
-    VT_STRIDE_H = 8,
-    VT_FILTER_WIDTH = 10,
-    VT_FILTER_HEIGHT = 12,
-    VT_FUSED_ACTIVATION_FUNCTION = 14
-  };
-  tflite::Padding padding() const {
-    return static_cast<tflite::Padding>(GetField<int8_t>(VT_PADDING, 0));
-  }
-  int32_t stride_w() const {
-    return GetField<int32_t>(VT_STRIDE_W, 0);
-  }
-  int32_t stride_h() const {
-    return GetField<int32_t>(VT_STRIDE_H, 0);
-  }
-  int32_t filter_width() const {
-    return GetField<int32_t>(VT_FILTER_WIDTH, 0);
-  }
-  int32_t filter_height() const {
-    return GetField<int32_t>(VT_FILTER_HEIGHT, 0);
-  }
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_PADDING, 1) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W, 4) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H, 4) &&
-           VerifyField<int32_t>(verifier, VT_FILTER_WIDTH, 4) &&
-           VerifyField<int32_t>(verifier, VT_FILTER_HEIGHT, 4) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           verifier.EndTable();
-  }
-  Pool2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<Pool2DOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct Pool2DOptionsBuilder {
-  typedef Pool2DOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(tflite::Padding padding) {
-    fbb_.AddElement<int8_t>(Pool2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_w(int32_t stride_w) {
-    fbb_.AddElement<int32_t>(Pool2DOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h) {
-    fbb_.AddElement<int32_t>(Pool2DOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  void add_filter_width(int32_t filter_width) {
-    fbb_.AddElement<int32_t>(Pool2DOptions::VT_FILTER_WIDTH, filter_width, 0);
-  }
-  void add_filter_height(int32_t filter_height) {
-    fbb_.AddElement<int32_t>(Pool2DOptions::VT_FILTER_HEIGHT, filter_height, 0);
-  }
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(Pool2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit Pool2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<Pool2DOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Pool2DOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Pool2DOptions> CreatePool2DOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::Padding padding = tflite::Padding_SAME,
-    int32_t stride_w = 0,
-    int32_t stride_h = 0,
-    int32_t filter_width = 0,
-    int32_t filter_height = 0,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) {
-  Pool2DOptionsBuilder builder_(_fbb);
-  builder_.add_filter_height(filter_height);
-  builder_.add_filter_width(filter_width);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<Pool2DOptions> CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct DepthwiseConv2DOptionsT : public flatbuffers::NativeTable {
-  typedef DepthwiseConv2DOptions TableType;
-  tflite::Padding padding = tflite::Padding_SAME;
-  int32_t stride_w = 0;
-  int32_t stride_h = 0;
-  int32_t depth_multiplier = 0;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  int32_t dilation_w_factor = 1;
-  int32_t dilation_h_factor = 1;
-};
-
-struct DepthwiseConv2DOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef DepthwiseConv2DOptionsT NativeTableType;
-  typedef DepthwiseConv2DOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_PADDING = 4,
-    VT_STRIDE_W = 6,
-    VT_STRIDE_H = 8,
-    VT_DEPTH_MULTIPLIER = 10,
-    VT_FUSED_ACTIVATION_FUNCTION = 12,
-    VT_DILATION_W_FACTOR = 14,
-    VT_DILATION_H_FACTOR = 16
-  };
-  tflite::Padding padding() const {
-    return static_cast<tflite::Padding>(GetField<int8_t>(VT_PADDING, 0));
-  }
-  int32_t stride_w() const {
-    return GetField<int32_t>(VT_STRIDE_W, 0);
-  }
-  int32_t stride_h() const {
-    return GetField<int32_t>(VT_STRIDE_H, 0);
-  }
-  int32_t depth_multiplier() const {
-    return GetField<int32_t>(VT_DEPTH_MULTIPLIER, 0);
-  }
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  int32_t dilation_w_factor() const {
-    return GetField<int32_t>(VT_DILATION_W_FACTOR, 1);
-  }
-  int32_t dilation_h_factor() const {
-    return GetField<int32_t>(VT_DILATION_H_FACTOR, 1);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_PADDING, 1) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W, 4) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H, 4) &&
-           VerifyField<int32_t>(verifier, VT_DEPTH_MULTIPLIER, 4) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_W_FACTOR, 4) &&
-           VerifyField<int32_t>(verifier, VT_DILATION_H_FACTOR, 4) &&
-           verifier.EndTable();
-  }
-  DepthwiseConv2DOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<DepthwiseConv2DOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct DepthwiseConv2DOptionsBuilder {
-  typedef DepthwiseConv2DOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(tflite::Padding padding) {
-    fbb_.AddElement<int8_t>(DepthwiseConv2DOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_w(int32_t stride_w) {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h) {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  void add_depth_multiplier(int32_t depth_multiplier) {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DEPTH_MULTIPLIER, depth_multiplier, 0);
-  }
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(DepthwiseConv2DOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_dilation_w_factor(int32_t dilation_w_factor) {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DILATION_W_FACTOR, dilation_w_factor, 1);
-  }
-  void add_dilation_h_factor(int32_t dilation_h_factor) {
-    fbb_.AddElement<int32_t>(DepthwiseConv2DOptions::VT_DILATION_H_FACTOR, dilation_h_factor, 1);
-  }
-  explicit DepthwiseConv2DOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<DepthwiseConv2DOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DepthwiseConv2DOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::Padding padding = tflite::Padding_SAME,
-    int32_t stride_w = 0,
-    int32_t stride_h = 0,
-    int32_t depth_multiplier = 0,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    int32_t dilation_w_factor = 1,
-    int32_t dilation_h_factor = 1) {
-  DepthwiseConv2DOptionsBuilder builder_(_fbb);
-  builder_.add_dilation_h_factor(dilation_h_factor);
-  builder_.add_dilation_w_factor(dilation_w_factor);
-  builder_.add_depth_multiplier(depth_multiplier);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ConcatEmbeddingsOptionsT : public flatbuffers::NativeTable {
-  typedef ConcatEmbeddingsOptions TableType;
-  int32_t num_channels = 0;
-  std::vector<int32_t> num_columns_per_channel{};
-  std::vector<int32_t> embedding_dim_per_channel{};
-};
-
-struct ConcatEmbeddingsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ConcatEmbeddingsOptionsT NativeTableType;
-  typedef ConcatEmbeddingsOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_NUM_CHANNELS = 4,
-    VT_NUM_COLUMNS_PER_CHANNEL = 6,
-    VT_EMBEDDING_DIM_PER_CHANNEL = 8
-  };
-  int32_t num_channels() const {
-    return GetField<int32_t>(VT_NUM_CHANNELS, 0);
-  }
-  const flatbuffers::Vector<int32_t> *num_columns_per_channel() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_NUM_COLUMNS_PER_CHANNEL);
-  }
-  const flatbuffers::Vector<int32_t> *embedding_dim_per_channel() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_EMBEDDING_DIM_PER_CHANNEL);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_NUM_CHANNELS, 4) &&
-           VerifyOffset(verifier, VT_NUM_COLUMNS_PER_CHANNEL) &&
-           verifier.VerifyVector(num_columns_per_channel()) &&
-           VerifyOffset(verifier, VT_EMBEDDING_DIM_PER_CHANNEL) &&
-           verifier.VerifyVector(embedding_dim_per_channel()) &&
-           verifier.EndTable();
-  }
-  ConcatEmbeddingsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ConcatEmbeddingsOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ConcatEmbeddingsOptionsBuilder {
-  typedef ConcatEmbeddingsOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_num_channels(int32_t num_channels) {
-    fbb_.AddElement<int32_t>(ConcatEmbeddingsOptions::VT_NUM_CHANNELS, num_channels, 0);
-  }
-  void add_num_columns_per_channel(flatbuffers::Offset<flatbuffers::Vector<int32_t>> num_columns_per_channel) {
-    fbb_.AddOffset(ConcatEmbeddingsOptions::VT_NUM_COLUMNS_PER_CHANNEL, num_columns_per_channel);
-  }
-  void add_embedding_dim_per_channel(flatbuffers::Offset<flatbuffers::Vector<int32_t>> embedding_dim_per_channel) {
-    fbb_.AddOffset(ConcatEmbeddingsOptions::VT_EMBEDDING_DIM_PER_CHANNEL, embedding_dim_per_channel);
-  }
-  explicit ConcatEmbeddingsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ConcatEmbeddingsOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ConcatEmbeddingsOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t num_channels = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> num_columns_per_channel = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> embedding_dim_per_channel = 0) {
-  ConcatEmbeddingsOptionsBuilder builder_(_fbb);
-  builder_.add_embedding_dim_per_channel(embedding_dim_per_channel);
-  builder_.add_num_columns_per_channel(num_columns_per_channel);
-  builder_.add_num_channels(num_channels);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptionsDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t num_channels = 0,
-    const std::vector<int32_t> *num_columns_per_channel = nullptr,
-    const std::vector<int32_t> *embedding_dim_per_channel = nullptr) {
-  auto num_columns_per_channel__ = num_columns_per_channel ? _fbb.CreateVector<int32_t>(*num_columns_per_channel) : 0;
-  auto embedding_dim_per_channel__ = embedding_dim_per_channel ? _fbb.CreateVector<int32_t>(*embedding_dim_per_channel) : 0;
-  return tflite::CreateConcatEmbeddingsOptions(
-      _fbb,
-      num_channels,
-      num_columns_per_channel__,
-      embedding_dim_per_channel__);
-}
-
-flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct LSHProjectionOptionsT : public flatbuffers::NativeTable {
-  typedef LSHProjectionOptions TableType;
-  tflite::LSHProjectionType type = tflite::LSHProjectionType_UNKNOWN;
-};
-
-struct LSHProjectionOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef LSHProjectionOptionsT NativeTableType;
-  typedef LSHProjectionOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_TYPE = 4
-  };
-  tflite::LSHProjectionType type() const {
-    return static_cast<tflite::LSHProjectionType>(GetField<int8_t>(VT_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_TYPE, 1) &&
-           verifier.EndTable();
-  }
-  LSHProjectionOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<LSHProjectionOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct LSHProjectionOptionsBuilder {
-  typedef LSHProjectionOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_type(tflite::LSHProjectionType type) {
-    fbb_.AddElement<int8_t>(LSHProjectionOptions::VT_TYPE, static_cast<int8_t>(type), 0);
-  }
-  explicit LSHProjectionOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<LSHProjectionOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LSHProjectionOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LSHProjectionOptions> CreateLSHProjectionOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::LSHProjectionType type = tflite::LSHProjectionType_UNKNOWN) {
-  LSHProjectionOptionsBuilder builder_(_fbb);
-  builder_.add_type(type);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<LSHProjectionOptions> CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SVDFOptionsT : public flatbuffers::NativeTable {
-  typedef SVDFOptions TableType;
-  int32_t rank = 0;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  bool asymmetric_quantize_inputs = false;
-};
-
-struct SVDFOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SVDFOptionsT NativeTableType;
-  typedef SVDFOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_RANK = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6,
-    VT_ASYMMETRIC_QUANTIZE_INPUTS = 8
-  };
-  int32_t rank() const {
-    return GetField<int32_t>(VT_RANK, 0);
-  }
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool asymmetric_quantize_inputs() const {
-    return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_RANK, 4) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
-           verifier.EndTable();
-  }
-  SVDFOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SVDFOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SVDFOptionsBuilder {
-  typedef SVDFOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_rank(int32_t rank) {
-    fbb_.AddElement<int32_t>(SVDFOptions::VT_RANK, rank, 0);
-  }
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(SVDFOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
-    fbb_.AddElement<uint8_t>(SVDFOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
-  }
-  explicit SVDFOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SVDFOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SVDFOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SVDFOptions> CreateSVDFOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t rank = 0,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    bool asymmetric_quantize_inputs = false) {
-  SVDFOptionsBuilder builder_(_fbb);
-  builder_.add_rank(rank);
-  builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SVDFOptions> CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct RNNOptionsT : public flatbuffers::NativeTable {
-  typedef RNNOptions TableType;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  bool asymmetric_quantize_inputs = false;
-};
-
-struct RNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef RNNOptionsT NativeTableType;
-  typedef RNNOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_ASYMMETRIC_QUANTIZE_INPUTS = 6
-  };
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool asymmetric_quantize_inputs() const {
-    return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
-           verifier.EndTable();
-  }
-  RNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<RNNOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct RNNOptionsBuilder {
-  typedef RNNOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(RNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
-    fbb_.AddElement<uint8_t>(RNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
-  }
-  explicit RNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<RNNOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<RNNOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<RNNOptions> CreateRNNOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    bool asymmetric_quantize_inputs = false) {
-  RNNOptionsBuilder builder_(_fbb);
-  builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<RNNOptions> CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SequenceRNNOptionsT : public flatbuffers::NativeTable {
-  typedef SequenceRNNOptions TableType;
-  bool time_major = false;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  bool asymmetric_quantize_inputs = false;
-};
-
-struct SequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SequenceRNNOptionsT NativeTableType;
-  typedef SequenceRNNOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_TIME_MAJOR = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6,
-    VT_ASYMMETRIC_QUANTIZE_INPUTS = 8
-  };
-  bool time_major() const {
-    return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0;
-  }
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool asymmetric_quantize_inputs() const {
-    return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<uint8_t>(verifier, VT_TIME_MAJOR, 1) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
-           verifier.EndTable();
-  }
-  SequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SequenceRNNOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SequenceRNNOptionsBuilder {
-  typedef SequenceRNNOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_time_major(bool time_major) {
-    fbb_.AddElement<uint8_t>(SequenceRNNOptions::VT_TIME_MAJOR, static_cast<uint8_t>(time_major), 0);
-  }
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(SequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
-    fbb_.AddElement<uint8_t>(SequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
-  }
-  explicit SequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SequenceRNNOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SequenceRNNOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    bool time_major = false,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    bool asymmetric_quantize_inputs = false) {
-  SequenceRNNOptionsBuilder builder_(_fbb);
-  builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_time_major(time_major);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct BidirectionalSequenceRNNOptionsT : public flatbuffers::NativeTable {
-  typedef BidirectionalSequenceRNNOptions TableType;
-  bool time_major = false;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  bool merge_outputs = false;
-  bool asymmetric_quantize_inputs = false;
-};
-
-struct BidirectionalSequenceRNNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef BidirectionalSequenceRNNOptionsT NativeTableType;
-  typedef BidirectionalSequenceRNNOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_TIME_MAJOR = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6,
-    VT_MERGE_OUTPUTS = 8,
-    VT_ASYMMETRIC_QUANTIZE_INPUTS = 10
-  };
-  bool time_major() const {
-    return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0;
-  }
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool merge_outputs() const {
-    return GetField<uint8_t>(VT_MERGE_OUTPUTS, 0) != 0;
-  }
-  bool asymmetric_quantize_inputs() const {
-    return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<uint8_t>(verifier, VT_TIME_MAJOR, 1) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<uint8_t>(verifier, VT_MERGE_OUTPUTS, 1) &&
-           VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
-           verifier.EndTable();
-  }
-  BidirectionalSequenceRNNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<BidirectionalSequenceRNNOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct BidirectionalSequenceRNNOptionsBuilder {
-  typedef BidirectionalSequenceRNNOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_time_major(bool time_major) {
-    fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_TIME_MAJOR, static_cast<uint8_t>(time_major), 0);
-  }
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(BidirectionalSequenceRNNOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_merge_outputs(bool merge_outputs) {
-    fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_MERGE_OUTPUTS, static_cast<uint8_t>(merge_outputs), 0);
-  }
-  void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
-    fbb_.AddElement<uint8_t>(BidirectionalSequenceRNNOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
-  }
-  explicit BidirectionalSequenceRNNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<BidirectionalSequenceRNNOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<BidirectionalSequenceRNNOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    bool time_major = false,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    bool merge_outputs = false,
-    bool asymmetric_quantize_inputs = false) {
-  BidirectionalSequenceRNNOptionsBuilder builder_(_fbb);
-  builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs);
-  builder_.add_merge_outputs(merge_outputs);
-  builder_.add_fused_activation_function(fused_activation_function);
-  builder_.add_time_major(time_major);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct FullyConnectedOptionsT : public flatbuffers::NativeTable {
-  typedef FullyConnectedOptions TableType;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  tflite::FullyConnectedOptionsWeightsFormat weights_format = tflite::FullyConnectedOptionsWeightsFormat_DEFAULT;
-  bool keep_num_dims = false;
-  bool asymmetric_quantize_inputs = false;
-};
-
-struct FullyConnectedOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef FullyConnectedOptionsT NativeTableType;
-  typedef FullyConnectedOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_WEIGHTS_FORMAT = 6,
-    VT_KEEP_NUM_DIMS = 8,
-    VT_ASYMMETRIC_QUANTIZE_INPUTS = 10
-  };
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  tflite::FullyConnectedOptionsWeightsFormat weights_format() const {
-    return static_cast<tflite::FullyConnectedOptionsWeightsFormat>(GetField<int8_t>(VT_WEIGHTS_FORMAT, 0));
-  }
-  bool keep_num_dims() const {
-    return GetField<uint8_t>(VT_KEEP_NUM_DIMS, 0) != 0;
-  }
-  bool asymmetric_quantize_inputs() const {
-    return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<int8_t>(verifier, VT_WEIGHTS_FORMAT, 1) &&
-           VerifyField<uint8_t>(verifier, VT_KEEP_NUM_DIMS, 1) &&
-           VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
-           verifier.EndTable();
-  }
-  FullyConnectedOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<FullyConnectedOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct FullyConnectedOptionsBuilder {
-  typedef FullyConnectedOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(FullyConnectedOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_weights_format(tflite::FullyConnectedOptionsWeightsFormat weights_format) {
-    fbb_.AddElement<int8_t>(FullyConnectedOptions::VT_WEIGHTS_FORMAT, static_cast<int8_t>(weights_format), 0);
-  }
-  void add_keep_num_dims(bool keep_num_dims) {
-    fbb_.AddElement<uint8_t>(FullyConnectedOptions::VT_KEEP_NUM_DIMS, static_cast<uint8_t>(keep_num_dims), 0);
-  }
-  void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
-    fbb_.AddElement<uint8_t>(FullyConnectedOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
-  }
-  explicit FullyConnectedOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<FullyConnectedOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FullyConnectedOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    tflite::FullyConnectedOptionsWeightsFormat weights_format = tflite::FullyConnectedOptionsWeightsFormat_DEFAULT,
-    bool keep_num_dims = false,
-    bool asymmetric_quantize_inputs = false) {
-  FullyConnectedOptionsBuilder builder_(_fbb);
-  builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs);
-  builder_.add_keep_num_dims(keep_num_dims);
-  builder_.add_weights_format(weights_format);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SoftmaxOptionsT : public flatbuffers::NativeTable {
-  typedef SoftmaxOptions TableType;
-  float beta = 0.0f;
-};
-
-struct SoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SoftmaxOptionsT NativeTableType;
-  typedef SoftmaxOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_BETA = 4
-  };
-  float beta() const {
-    return GetField<float>(VT_BETA, 0.0f);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<float>(verifier, VT_BETA, 4) &&
-           verifier.EndTable();
-  }
-  SoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SoftmaxOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SoftmaxOptionsBuilder {
-  typedef SoftmaxOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_beta(float beta) {
-    fbb_.AddElement<float>(SoftmaxOptions::VT_BETA, beta, 0.0f);
-  }
-  explicit SoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SoftmaxOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SoftmaxOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SoftmaxOptions> CreateSoftmaxOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    float beta = 0.0f) {
-  SoftmaxOptionsBuilder builder_(_fbb);
-  builder_.add_beta(beta);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SoftmaxOptions> CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ConcatenationOptionsT : public flatbuffers::NativeTable {
-  typedef ConcatenationOptions TableType;
-  int32_t axis = 0;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-};
-
-struct ConcatenationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ConcatenationOptionsT NativeTableType;
-  typedef ConcatenationOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_AXIS = 4,
-    VT_FUSED_ACTIVATION_FUNCTION = 6
-  };
-  int32_t axis() const {
-    return GetField<int32_t>(VT_AXIS, 0);
-  }
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_AXIS, 4) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           verifier.EndTable();
-  }
-  ConcatenationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ConcatenationOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ConcatenationOptionsBuilder {
-  typedef ConcatenationOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_axis(int32_t axis) {
-    fbb_.AddElement<int32_t>(ConcatenationOptions::VT_AXIS, axis, 0);
-  }
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(ConcatenationOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit ConcatenationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ConcatenationOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ConcatenationOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t axis = 0,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) {
-  ConcatenationOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct AddOptionsT : public flatbuffers::NativeTable {
-  typedef AddOptions TableType;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  bool pot_scale_int16 = true;
-};
-
-struct AddOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef AddOptionsT NativeTableType;
-  typedef AddOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_POT_SCALE_INT16 = 6
-  };
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool pot_scale_int16() const {
-    return GetField<uint8_t>(VT_POT_SCALE_INT16, 1) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<uint8_t>(verifier, VT_POT_SCALE_INT16, 1) &&
-           verifier.EndTable();
-  }
-  AddOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<AddOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct AddOptionsBuilder {
-  typedef AddOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(AddOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_pot_scale_int16(bool pot_scale_int16) {
-    fbb_.AddElement<uint8_t>(AddOptions::VT_POT_SCALE_INT16, static_cast<uint8_t>(pot_scale_int16), 1);
-  }
-  explicit AddOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<AddOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<AddOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<AddOptions> CreateAddOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    bool pot_scale_int16 = true) {
-  AddOptionsBuilder builder_(_fbb);
-  builder_.add_pot_scale_int16(pot_scale_int16);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<AddOptions> CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct MulOptionsT : public flatbuffers::NativeTable {
-  typedef MulOptions TableType;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-};
-
-struct MulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef MulOptionsT NativeTableType;
-  typedef MulOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           verifier.EndTable();
-  }
-  MulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<MulOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct MulOptionsBuilder {
-  typedef MulOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(MulOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit MulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<MulOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<MulOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<MulOptions> CreateMulOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) {
-  MulOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<MulOptions> CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct L2NormOptionsT : public flatbuffers::NativeTable {
-  typedef L2NormOptions TableType;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-};
-
-struct L2NormOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef L2NormOptionsT NativeTableType;
-  typedef L2NormOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           verifier.EndTable();
-  }
-  L2NormOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<L2NormOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct L2NormOptionsBuilder {
-  typedef L2NormOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(L2NormOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit L2NormOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<L2NormOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<L2NormOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<L2NormOptions> CreateL2NormOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) {
-  L2NormOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<L2NormOptions> CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct LocalResponseNormalizationOptionsT : public flatbuffers::NativeTable {
-  typedef LocalResponseNormalizationOptions TableType;
-  int32_t radius = 0;
-  float bias = 0.0f;
-  float alpha = 0.0f;
-  float beta = 0.0f;
-};
-
-struct LocalResponseNormalizationOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef LocalResponseNormalizationOptionsT NativeTableType;
-  typedef LocalResponseNormalizationOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_RADIUS = 4,
-    VT_BIAS = 6,
-    VT_ALPHA = 8,
-    VT_BETA = 10
-  };
-  int32_t radius() const {
-    return GetField<int32_t>(VT_RADIUS, 0);
-  }
-  float bias() const {
-    return GetField<float>(VT_BIAS, 0.0f);
-  }
-  float alpha() const {
-    return GetField<float>(VT_ALPHA, 0.0f);
-  }
-  float beta() const {
-    return GetField<float>(VT_BETA, 0.0f);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_RADIUS, 4) &&
-           VerifyField<float>(verifier, VT_BIAS, 4) &&
-           VerifyField<float>(verifier, VT_ALPHA, 4) &&
-           VerifyField<float>(verifier, VT_BETA, 4) &&
-           verifier.EndTable();
-  }
-  LocalResponseNormalizationOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<LocalResponseNormalizationOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct LocalResponseNormalizationOptionsBuilder {
-  typedef LocalResponseNormalizationOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_radius(int32_t radius) {
-    fbb_.AddElement<int32_t>(LocalResponseNormalizationOptions::VT_RADIUS, radius, 0);
-  }
-  void add_bias(float bias) {
-    fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_BIAS, bias, 0.0f);
-  }
-  void add_alpha(float alpha) {
-    fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_ALPHA, alpha, 0.0f);
-  }
-  void add_beta(float beta) {
-    fbb_.AddElement<float>(LocalResponseNormalizationOptions::VT_BETA, beta, 0.0f);
-  }
-  explicit LocalResponseNormalizationOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<LocalResponseNormalizationOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LocalResponseNormalizationOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LocalResponseNormalizationOptions> CreateLocalResponseNormalizationOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t radius = 0,
-    float bias = 0.0f,
-    float alpha = 0.0f,
-    float beta = 0.0f) {
-  LocalResponseNormalizationOptionsBuilder builder_(_fbb);
-  builder_.add_beta(beta);
-  builder_.add_alpha(alpha);
-  builder_.add_bias(bias);
-  builder_.add_radius(radius);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<LocalResponseNormalizationOptions> CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct LSTMOptionsT : public flatbuffers::NativeTable {
-  typedef LSTMOptions TableType;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  float cell_clip = 0.0f;
-  float proj_clip = 0.0f;
-  tflite::LSTMKernelType kernel_type = tflite::LSTMKernelType_FULL;
-  bool asymmetric_quantize_inputs = false;
-};
-
-struct LSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef LSTMOptionsT NativeTableType;
-  typedef LSTMOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_CELL_CLIP = 6,
-    VT_PROJ_CLIP = 8,
-    VT_KERNEL_TYPE = 10,
-    VT_ASYMMETRIC_QUANTIZE_INPUTS = 12
-  };
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  float cell_clip() const {
-    return GetField<float>(VT_CELL_CLIP, 0.0f);
-  }
-  float proj_clip() const {
-    return GetField<float>(VT_PROJ_CLIP, 0.0f);
-  }
-  tflite::LSTMKernelType kernel_type() const {
-    return static_cast<tflite::LSTMKernelType>(GetField<int8_t>(VT_KERNEL_TYPE, 0));
-  }
-  bool asymmetric_quantize_inputs() const {
-    return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<float>(verifier, VT_CELL_CLIP, 4) &&
-           VerifyField<float>(verifier, VT_PROJ_CLIP, 4) &&
-           VerifyField<int8_t>(verifier, VT_KERNEL_TYPE, 1) &&
-           VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
-           verifier.EndTable();
-  }
-  LSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<LSTMOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct LSTMOptionsBuilder {
-  typedef LSTMOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(LSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_cell_clip(float cell_clip) {
-    fbb_.AddElement<float>(LSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f);
-  }
-  void add_proj_clip(float proj_clip) {
-    fbb_.AddElement<float>(LSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f);
-  }
-  void add_kernel_type(tflite::LSTMKernelType kernel_type) {
-    fbb_.AddElement<int8_t>(LSTMOptions::VT_KERNEL_TYPE, static_cast<int8_t>(kernel_type), 0);
-  }
-  void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
-    fbb_.AddElement<uint8_t>(LSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
-  }
-  explicit LSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<LSTMOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LSTMOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LSTMOptions> CreateLSTMOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    float cell_clip = 0.0f,
-    float proj_clip = 0.0f,
-    tflite::LSTMKernelType kernel_type = tflite::LSTMKernelType_FULL,
-    bool asymmetric_quantize_inputs = false) {
-  LSTMOptionsBuilder builder_(_fbb);
-  builder_.add_proj_clip(proj_clip);
-  builder_.add_cell_clip(cell_clip);
-  builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs);
-  builder_.add_kernel_type(kernel_type);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<LSTMOptions> CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct UnidirectionalSequenceLSTMOptionsT : public flatbuffers::NativeTable {
-  typedef UnidirectionalSequenceLSTMOptions TableType;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  float cell_clip = 0.0f;
-  float proj_clip = 0.0f;
-  bool time_major = false;
-  bool asymmetric_quantize_inputs = false;
-};
-
-struct UnidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef UnidirectionalSequenceLSTMOptionsT NativeTableType;
-  typedef UnidirectionalSequenceLSTMOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_CELL_CLIP = 6,
-    VT_PROJ_CLIP = 8,
-    VT_TIME_MAJOR = 10,
-    VT_ASYMMETRIC_QUANTIZE_INPUTS = 12
-  };
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  float cell_clip() const {
-    return GetField<float>(VT_CELL_CLIP, 0.0f);
-  }
-  float proj_clip() const {
-    return GetField<float>(VT_PROJ_CLIP, 0.0f);
-  }
-  bool time_major() const {
-    return GetField<uint8_t>(VT_TIME_MAJOR, 0) != 0;
-  }
-  bool asymmetric_quantize_inputs() const {
-    return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<float>(verifier, VT_CELL_CLIP, 4) &&
-           VerifyField<float>(verifier, VT_PROJ_CLIP, 4) &&
-           VerifyField<uint8_t>(verifier, VT_TIME_MAJOR, 1) &&
-           VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
-           verifier.EndTable();
-  }
-  UnidirectionalSequenceLSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct UnidirectionalSequenceLSTMOptionsBuilder {
-  typedef UnidirectionalSequenceLSTMOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(UnidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_cell_clip(float cell_clip) {
-    fbb_.AddElement<float>(UnidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f);
-  }
-  void add_proj_clip(float proj_clip) {
-    fbb_.AddElement<float>(UnidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f);
-  }
-  void add_time_major(bool time_major) {
-    fbb_.AddElement<uint8_t>(UnidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, static_cast<uint8_t>(time_major), 0);
-  }
-  void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
-    fbb_.AddElement<uint8_t>(UnidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
-  }
-  explicit UnidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<UnidirectionalSequenceLSTMOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> CreateUnidirectionalSequenceLSTMOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    float cell_clip = 0.0f,
-    float proj_clip = 0.0f,
-    bool time_major = false,
-    bool asymmetric_quantize_inputs = false) {
-  UnidirectionalSequenceLSTMOptionsBuilder builder_(_fbb);
-  builder_.add_proj_clip(proj_clip);
-  builder_.add_cell_clip(cell_clip);
-  builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs);
-  builder_.add_time_major(time_major);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> CreateUnidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct BidirectionalSequenceLSTMOptionsT : public flatbuffers::NativeTable {
-  typedef BidirectionalSequenceLSTMOptions TableType;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  float cell_clip = 0.0f;
-  float proj_clip = 0.0f;
-  bool merge_outputs = false;
-  bool time_major = true;
-  bool asymmetric_quantize_inputs = false;
-};
-
-struct BidirectionalSequenceLSTMOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef BidirectionalSequenceLSTMOptionsT NativeTableType;
-  typedef BidirectionalSequenceLSTMOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_CELL_CLIP = 6,
-    VT_PROJ_CLIP = 8,
-    VT_MERGE_OUTPUTS = 10,
-    VT_TIME_MAJOR = 12,
-    VT_ASYMMETRIC_QUANTIZE_INPUTS = 14
-  };
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  float cell_clip() const {
-    return GetField<float>(VT_CELL_CLIP, 0.0f);
-  }
-  float proj_clip() const {
-    return GetField<float>(VT_PROJ_CLIP, 0.0f);
-  }
-  bool merge_outputs() const {
-    return GetField<uint8_t>(VT_MERGE_OUTPUTS, 0) != 0;
-  }
-  bool time_major() const {
-    return GetField<uint8_t>(VT_TIME_MAJOR, 1) != 0;
-  }
-  bool asymmetric_quantize_inputs() const {
-    return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<float>(verifier, VT_CELL_CLIP, 4) &&
-           VerifyField<float>(verifier, VT_PROJ_CLIP, 4) &&
-           VerifyField<uint8_t>(verifier, VT_MERGE_OUTPUTS, 1) &&
-           VerifyField<uint8_t>(verifier, VT_TIME_MAJOR, 1) &&
-           VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
-           verifier.EndTable();
-  }
-  BidirectionalSequenceLSTMOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<BidirectionalSequenceLSTMOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct BidirectionalSequenceLSTMOptionsBuilder {
-  typedef BidirectionalSequenceLSTMOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(BidirectionalSequenceLSTMOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_cell_clip(float cell_clip) {
-    fbb_.AddElement<float>(BidirectionalSequenceLSTMOptions::VT_CELL_CLIP, cell_clip, 0.0f);
-  }
-  void add_proj_clip(float proj_clip) {
-    fbb_.AddElement<float>(BidirectionalSequenceLSTMOptions::VT_PROJ_CLIP, proj_clip, 0.0f);
-  }
-  void add_merge_outputs(bool merge_outputs) {
-    fbb_.AddElement<uint8_t>(BidirectionalSequenceLSTMOptions::VT_MERGE_OUTPUTS, static_cast<uint8_t>(merge_outputs), 0);
-  }
-  void add_time_major(bool time_major) {
-    fbb_.AddElement<uint8_t>(BidirectionalSequenceLSTMOptions::VT_TIME_MAJOR, static_cast<uint8_t>(time_major), 1);
-  }
-  void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
-    fbb_.AddElement<uint8_t>(BidirectionalSequenceLSTMOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
-  }
-  explicit BidirectionalSequenceLSTMOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<BidirectionalSequenceLSTMOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<BidirectionalSequenceLSTMOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    float cell_clip = 0.0f,
-    float proj_clip = 0.0f,
-    bool merge_outputs = false,
-    bool time_major = true,
-    bool asymmetric_quantize_inputs = false) {
-  BidirectionalSequenceLSTMOptionsBuilder builder_(_fbb);
-  builder_.add_proj_clip(proj_clip);
-  builder_.add_cell_clip(cell_clip);
-  builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs);
-  builder_.add_time_major(time_major);
-  builder_.add_merge_outputs(merge_outputs);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ResizeBilinearOptionsT : public flatbuffers::NativeTable {
-  typedef ResizeBilinearOptions TableType;
-  bool align_corners = false;
-  bool half_pixel_centers = false;
-};
-
-struct ResizeBilinearOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ResizeBilinearOptionsT NativeTableType;
-  typedef ResizeBilinearOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_ALIGN_CORNERS = 8,
-    VT_HALF_PIXEL_CENTERS = 10
-  };
-  bool align_corners() const {
-    return GetField<uint8_t>(VT_ALIGN_CORNERS, 0) != 0;
-  }
-  bool half_pixel_centers() const {
-    return GetField<uint8_t>(VT_HALF_PIXEL_CENTERS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<uint8_t>(verifier, VT_ALIGN_CORNERS, 1) &&
-           VerifyField<uint8_t>(verifier, VT_HALF_PIXEL_CENTERS, 1) &&
-           verifier.EndTable();
-  }
-  ResizeBilinearOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ResizeBilinearOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ResizeBilinearOptionsBuilder {
-  typedef ResizeBilinearOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_align_corners(bool align_corners) {
-    fbb_.AddElement<uint8_t>(ResizeBilinearOptions::VT_ALIGN_CORNERS, static_cast<uint8_t>(align_corners), 0);
-  }
-  void add_half_pixel_centers(bool half_pixel_centers) {
-    fbb_.AddElement<uint8_t>(ResizeBilinearOptions::VT_HALF_PIXEL_CENTERS, static_cast<uint8_t>(half_pixel_centers), 0);
-  }
-  explicit ResizeBilinearOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ResizeBilinearOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ResizeBilinearOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ResizeBilinearOptions> CreateResizeBilinearOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    bool align_corners = false,
-    bool half_pixel_centers = false) {
-  ResizeBilinearOptionsBuilder builder_(_fbb);
-  builder_.add_half_pixel_centers(half_pixel_centers);
-  builder_.add_align_corners(align_corners);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ResizeBilinearOptions> CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ResizeNearestNeighborOptionsT : public flatbuffers::NativeTable {
-  typedef ResizeNearestNeighborOptions TableType;
-  bool align_corners = false;
-  bool half_pixel_centers = false;
-};
-
-struct ResizeNearestNeighborOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ResizeNearestNeighborOptionsT NativeTableType;
-  typedef ResizeNearestNeighborOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_ALIGN_CORNERS = 4,
-    VT_HALF_PIXEL_CENTERS = 6
-  };
-  bool align_corners() const {
-    return GetField<uint8_t>(VT_ALIGN_CORNERS, 0) != 0;
-  }
-  bool half_pixel_centers() const {
-    return GetField<uint8_t>(VT_HALF_PIXEL_CENTERS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<uint8_t>(verifier, VT_ALIGN_CORNERS, 1) &&
-           VerifyField<uint8_t>(verifier, VT_HALF_PIXEL_CENTERS, 1) &&
-           verifier.EndTable();
-  }
-  ResizeNearestNeighborOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ResizeNearestNeighborOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ResizeNearestNeighborOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ResizeNearestNeighborOptionsBuilder {
-  typedef ResizeNearestNeighborOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_align_corners(bool align_corners) {
-    fbb_.AddElement<uint8_t>(ResizeNearestNeighborOptions::VT_ALIGN_CORNERS, static_cast<uint8_t>(align_corners), 0);
-  }
-  void add_half_pixel_centers(bool half_pixel_centers) {
-    fbb_.AddElement<uint8_t>(ResizeNearestNeighborOptions::VT_HALF_PIXEL_CENTERS, static_cast<uint8_t>(half_pixel_centers), 0);
-  }
-  explicit ResizeNearestNeighborOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ResizeNearestNeighborOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ResizeNearestNeighborOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ResizeNearestNeighborOptions> CreateResizeNearestNeighborOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    bool align_corners = false,
-    bool half_pixel_centers = false) {
-  ResizeNearestNeighborOptionsBuilder builder_(_fbb);
-  builder_.add_half_pixel_centers(half_pixel_centers);
-  builder_.add_align_corners(align_corners);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ResizeNearestNeighborOptions> CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct CallOptionsT : public flatbuffers::NativeTable {
-  typedef CallOptions TableType;
-  uint32_t subgraph = 0;
-};
-
-struct CallOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef CallOptionsT NativeTableType;
-  typedef CallOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_SUBGRAPH = 4
-  };
-  uint32_t subgraph() const {
-    return GetField<uint32_t>(VT_SUBGRAPH, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<uint32_t>(verifier, VT_SUBGRAPH, 4) &&
-           verifier.EndTable();
-  }
-  CallOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<CallOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct CallOptionsBuilder {
-  typedef CallOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_subgraph(uint32_t subgraph) {
-    fbb_.AddElement<uint32_t>(CallOptions::VT_SUBGRAPH, subgraph, 0);
-  }
-  explicit CallOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<CallOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<CallOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<CallOptions> CreateCallOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    uint32_t subgraph = 0) {
-  CallOptionsBuilder builder_(_fbb);
-  builder_.add_subgraph(subgraph);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<CallOptions> CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct PadOptionsT : public flatbuffers::NativeTable {
-  typedef PadOptions TableType;
-};
-
-struct PadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef PadOptionsT NativeTableType;
-  typedef PadOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  PadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<PadOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct PadOptionsBuilder {
-  typedef PadOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit PadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<PadOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<PadOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<PadOptions> CreatePadOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  PadOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<PadOptions> CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct PadV2OptionsT : public flatbuffers::NativeTable {
-  typedef PadV2Options TableType;
-};
-
-struct PadV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef PadV2OptionsT NativeTableType;
-  typedef PadV2OptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  PadV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(PadV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<PadV2Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct PadV2OptionsBuilder {
-  typedef PadV2Options Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit PadV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<PadV2Options> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<PadV2Options>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<PadV2Options> CreatePadV2Options(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  PadV2OptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<PadV2Options> CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ReshapeOptionsT : public flatbuffers::NativeTable {
-  typedef ReshapeOptions TableType;
-  std::vector<int32_t> new_shape{};
-};
-
-struct ReshapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ReshapeOptionsT NativeTableType;
-  typedef ReshapeOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_NEW_SHAPE = 4
-  };
-  const flatbuffers::Vector<int32_t> *new_shape() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_NEW_SHAPE);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_NEW_SHAPE) &&
-           verifier.VerifyVector(new_shape()) &&
-           verifier.EndTable();
-  }
-  ReshapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ReshapeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ReshapeOptionsBuilder {
-  typedef ReshapeOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_new_shape(flatbuffers::Offset<flatbuffers::Vector<int32_t>> new_shape) {
-    fbb_.AddOffset(ReshapeOptions::VT_NEW_SHAPE, new_shape);
-  }
-  explicit ReshapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ReshapeOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ReshapeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ReshapeOptions> CreateReshapeOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> new_shape = 0) {
-  ReshapeOptionsBuilder builder_(_fbb);
-  builder_.add_new_shape(new_shape);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<ReshapeOptions> CreateReshapeOptionsDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<int32_t> *new_shape = nullptr) {
-  auto new_shape__ = new_shape ? _fbb.CreateVector<int32_t>(*new_shape) : 0;
-  return tflite::CreateReshapeOptions(
-      _fbb,
-      new_shape__);
-}
-
-flatbuffers::Offset<ReshapeOptions> CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SpaceToBatchNDOptionsT : public flatbuffers::NativeTable {
-  typedef SpaceToBatchNDOptions TableType;
-};
-
-struct SpaceToBatchNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SpaceToBatchNDOptionsT NativeTableType;
-  typedef SpaceToBatchNDOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  SpaceToBatchNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SpaceToBatchNDOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SpaceToBatchNDOptionsBuilder {
-  typedef SpaceToBatchNDOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SpaceToBatchNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SpaceToBatchNDOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SpaceToBatchNDOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SpaceToBatchNDOptions> CreateSpaceToBatchNDOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  SpaceToBatchNDOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SpaceToBatchNDOptions> CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct BatchToSpaceNDOptionsT : public flatbuffers::NativeTable {
-  typedef BatchToSpaceNDOptions TableType;
-};
-
-struct BatchToSpaceNDOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef BatchToSpaceNDOptionsT NativeTableType;
-  typedef BatchToSpaceNDOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  BatchToSpaceNDOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<BatchToSpaceNDOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct BatchToSpaceNDOptionsBuilder {
-  typedef BatchToSpaceNDOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit BatchToSpaceNDOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<BatchToSpaceNDOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<BatchToSpaceNDOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<BatchToSpaceNDOptions> CreateBatchToSpaceNDOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  BatchToSpaceNDOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<BatchToSpaceNDOptions> CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SkipGramOptionsT : public flatbuffers::NativeTable {
-  typedef SkipGramOptions TableType;
-  int32_t ngram_size = 0;
-  int32_t max_skip_size = 0;
-  bool include_all_ngrams = false;
-};
-
-struct SkipGramOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SkipGramOptionsT NativeTableType;
-  typedef SkipGramOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_NGRAM_SIZE = 4,
-    VT_MAX_SKIP_SIZE = 6,
-    VT_INCLUDE_ALL_NGRAMS = 8
-  };
-  int32_t ngram_size() const {
-    return GetField<int32_t>(VT_NGRAM_SIZE, 0);
-  }
-  int32_t max_skip_size() const {
-    return GetField<int32_t>(VT_MAX_SKIP_SIZE, 0);
-  }
-  bool include_all_ngrams() const {
-    return GetField<uint8_t>(VT_INCLUDE_ALL_NGRAMS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_NGRAM_SIZE, 4) &&
-           VerifyField<int32_t>(verifier, VT_MAX_SKIP_SIZE, 4) &&
-           VerifyField<uint8_t>(verifier, VT_INCLUDE_ALL_NGRAMS, 1) &&
-           verifier.EndTable();
-  }
-  SkipGramOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SkipGramOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SkipGramOptionsBuilder {
-  typedef SkipGramOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_ngram_size(int32_t ngram_size) {
-    fbb_.AddElement<int32_t>(SkipGramOptions::VT_NGRAM_SIZE, ngram_size, 0);
-  }
-  void add_max_skip_size(int32_t max_skip_size) {
-    fbb_.AddElement<int32_t>(SkipGramOptions::VT_MAX_SKIP_SIZE, max_skip_size, 0);
-  }
-  void add_include_all_ngrams(bool include_all_ngrams) {
-    fbb_.AddElement<uint8_t>(SkipGramOptions::VT_INCLUDE_ALL_NGRAMS, static_cast<uint8_t>(include_all_ngrams), 0);
-  }
-  explicit SkipGramOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SkipGramOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SkipGramOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SkipGramOptions> CreateSkipGramOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t ngram_size = 0,
-    int32_t max_skip_size = 0,
-    bool include_all_ngrams = false) {
-  SkipGramOptionsBuilder builder_(_fbb);
-  builder_.add_max_skip_size(max_skip_size);
-  builder_.add_ngram_size(ngram_size);
-  builder_.add_include_all_ngrams(include_all_ngrams);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SkipGramOptions> CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SpaceToDepthOptionsT : public flatbuffers::NativeTable {
-  typedef SpaceToDepthOptions TableType;
-  int32_t block_size = 0;
-};
-
-struct SpaceToDepthOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SpaceToDepthOptionsT NativeTableType;
-  typedef SpaceToDepthOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_BLOCK_SIZE = 4
-  };
-  int32_t block_size() const {
-    return GetField<int32_t>(VT_BLOCK_SIZE, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_BLOCK_SIZE, 4) &&
-           verifier.EndTable();
-  }
-  SpaceToDepthOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SpaceToDepthOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SpaceToDepthOptionsBuilder {
-  typedef SpaceToDepthOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_block_size(int32_t block_size) {
-    fbb_.AddElement<int32_t>(SpaceToDepthOptions::VT_BLOCK_SIZE, block_size, 0);
-  }
-  explicit SpaceToDepthOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SpaceToDepthOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SpaceToDepthOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SpaceToDepthOptions> CreateSpaceToDepthOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t block_size = 0) {
-  SpaceToDepthOptionsBuilder builder_(_fbb);
-  builder_.add_block_size(block_size);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SpaceToDepthOptions> CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct DepthToSpaceOptionsT : public flatbuffers::NativeTable {
-  typedef DepthToSpaceOptions TableType;
-  int32_t block_size = 0;
-};
-
-struct DepthToSpaceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef DepthToSpaceOptionsT NativeTableType;
-  typedef DepthToSpaceOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_BLOCK_SIZE = 4
-  };
-  int32_t block_size() const {
-    return GetField<int32_t>(VT_BLOCK_SIZE, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_BLOCK_SIZE, 4) &&
-           verifier.EndTable();
-  }
-  DepthToSpaceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(DepthToSpaceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<DepthToSpaceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct DepthToSpaceOptionsBuilder {
-  typedef DepthToSpaceOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_block_size(int32_t block_size) {
-    fbb_.AddElement<int32_t>(DepthToSpaceOptions::VT_BLOCK_SIZE, block_size, 0);
-  }
-  explicit DepthToSpaceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<DepthToSpaceOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DepthToSpaceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DepthToSpaceOptions> CreateDepthToSpaceOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t block_size = 0) {
-  DepthToSpaceOptionsBuilder builder_(_fbb);
-  builder_.add_block_size(block_size);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<DepthToSpaceOptions> CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SubOptionsT : public flatbuffers::NativeTable {
-  typedef SubOptions TableType;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-  bool pot_scale_int16 = true;
-};
-
-struct SubOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SubOptionsT NativeTableType;
-  typedef SubOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_FUSED_ACTIVATION_FUNCTION = 4,
-    VT_POT_SCALE_INT16 = 6
-  };
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool pot_scale_int16() const {
-    return GetField<uint8_t>(VT_POT_SCALE_INT16, 1) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           VerifyField<uint8_t>(verifier, VT_POT_SCALE_INT16, 1) &&
-           verifier.EndTable();
-  }
-  SubOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SubOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SubOptionsBuilder {
-  typedef SubOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(SubOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  void add_pot_scale_int16(bool pot_scale_int16) {
-    fbb_.AddElement<uint8_t>(SubOptions::VT_POT_SCALE_INT16, static_cast<uint8_t>(pot_scale_int16), 1);
-  }
-  explicit SubOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SubOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SubOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SubOptions> CreateSubOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE,
-    bool pot_scale_int16 = true) {
-  SubOptionsBuilder builder_(_fbb);
-  builder_.add_pot_scale_int16(pot_scale_int16);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SubOptions> CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct DivOptionsT : public flatbuffers::NativeTable {
-  typedef DivOptions TableType;
-  tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE;
-};
-
-struct DivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef DivOptionsT NativeTableType;
-  typedef DivOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_FUSED_ACTIVATION_FUNCTION = 4
-  };
-  tflite::ActivationFunctionType fused_activation_function() const {
-    return static_cast<tflite::ActivationFunctionType>(GetField<int8_t>(VT_FUSED_ACTIVATION_FUNCTION, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_FUSED_ACTIVATION_FUNCTION, 1) &&
-           verifier.EndTable();
-  }
-  DivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<DivOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct DivOptionsBuilder {
-  typedef DivOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_fused_activation_function(tflite::ActivationFunctionType fused_activation_function) {
-    fbb_.AddElement<int8_t>(DivOptions::VT_FUSED_ACTIVATION_FUNCTION, static_cast<int8_t>(fused_activation_function), 0);
-  }
-  explicit DivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<DivOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DivOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DivOptions> CreateDivOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::ActivationFunctionType fused_activation_function = tflite::ActivationFunctionType_NONE) {
-  DivOptionsBuilder builder_(_fbb);
-  builder_.add_fused_activation_function(fused_activation_function);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<DivOptions> CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct TopKV2OptionsT : public flatbuffers::NativeTable {
-  typedef TopKV2Options TableType;
-};
-
-struct TopKV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef TopKV2OptionsT NativeTableType;
-  typedef TopKV2OptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  TopKV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<TopKV2Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct TopKV2OptionsBuilder {
-  typedef TopKV2Options Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit TopKV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<TopKV2Options> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TopKV2Options>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  TopKV2OptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct EmbeddingLookupSparseOptionsT : public flatbuffers::NativeTable {
-  typedef EmbeddingLookupSparseOptions TableType;
-  tflite::CombinerType combiner = tflite::CombinerType_SUM;
-};
-
-struct EmbeddingLookupSparseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef EmbeddingLookupSparseOptionsT NativeTableType;
-  typedef EmbeddingLookupSparseOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_COMBINER = 4
-  };
-  tflite::CombinerType combiner() const {
-    return static_cast<tflite::CombinerType>(GetField<int8_t>(VT_COMBINER, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_COMBINER, 1) &&
-           verifier.EndTable();
-  }
-  EmbeddingLookupSparseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<EmbeddingLookupSparseOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct EmbeddingLookupSparseOptionsBuilder {
-  typedef EmbeddingLookupSparseOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_combiner(tflite::CombinerType combiner) {
-    fbb_.AddElement<int8_t>(EmbeddingLookupSparseOptions::VT_COMBINER, static_cast<int8_t>(combiner), 0);
-  }
-  explicit EmbeddingLookupSparseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<EmbeddingLookupSparseOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<EmbeddingLookupSparseOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<EmbeddingLookupSparseOptions> CreateEmbeddingLookupSparseOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::CombinerType combiner = tflite::CombinerType_SUM) {
-  EmbeddingLookupSparseOptionsBuilder builder_(_fbb);
-  builder_.add_combiner(combiner);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<EmbeddingLookupSparseOptions> CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct GatherOptionsT : public flatbuffers::NativeTable {
-  typedef GatherOptions TableType;
-  int32_t axis = 0;
-  int32_t batch_dims = 0;
-};
-
-struct GatherOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef GatherOptionsT NativeTableType;
-  typedef GatherOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_AXIS = 4,
-    VT_BATCH_DIMS = 6
-  };
-  int32_t axis() const {
-    return GetField<int32_t>(VT_AXIS, 0);
-  }
-  int32_t batch_dims() const {
-    return GetField<int32_t>(VT_BATCH_DIMS, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_AXIS, 4) &&
-           VerifyField<int32_t>(verifier, VT_BATCH_DIMS, 4) &&
-           verifier.EndTable();
-  }
-  GatherOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<GatherOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct GatherOptionsBuilder {
-  typedef GatherOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_axis(int32_t axis) {
-    fbb_.AddElement<int32_t>(GatherOptions::VT_AXIS, axis, 0);
-  }
-  void add_batch_dims(int32_t batch_dims) {
-    fbb_.AddElement<int32_t>(GatherOptions::VT_BATCH_DIMS, batch_dims, 0);
-  }
-  explicit GatherOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<GatherOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<GatherOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<GatherOptions> CreateGatherOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t axis = 0,
-    int32_t batch_dims = 0) {
-  GatherOptionsBuilder builder_(_fbb);
-  builder_.add_batch_dims(batch_dims);
-  builder_.add_axis(axis);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<GatherOptions> CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct TransposeOptionsT : public flatbuffers::NativeTable {
-  typedef TransposeOptions TableType;
-};
-
-struct TransposeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef TransposeOptionsT NativeTableType;
-  typedef TransposeOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  TransposeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<TransposeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct TransposeOptionsBuilder {
-  typedef TransposeOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit TransposeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<TransposeOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TransposeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TransposeOptions> CreateTransposeOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  TransposeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<TransposeOptions> CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ExpOptionsT : public flatbuffers::NativeTable {
-  typedef ExpOptions TableType;
-};
-
-struct ExpOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ExpOptionsT NativeTableType;
-  typedef ExpOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  ExpOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ExpOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ExpOptionsBuilder {
-  typedef ExpOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ExpOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ExpOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ExpOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ExpOptions> CreateExpOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  ExpOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ExpOptions> CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct CosOptionsT : public flatbuffers::NativeTable {
-  typedef CosOptions TableType;
-};
-
-struct CosOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef CosOptionsT NativeTableType;
-  typedef CosOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  CosOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(CosOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<CosOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct CosOptionsBuilder {
-  typedef CosOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit CosOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<CosOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<CosOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<CosOptions> CreateCosOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  CosOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<CosOptions> CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ReducerOptionsT : public flatbuffers::NativeTable {
-  typedef ReducerOptions TableType;
-  bool keep_dims = false;
-};
-
-struct ReducerOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ReducerOptionsT NativeTableType;
-  typedef ReducerOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_KEEP_DIMS = 4
-  };
-  bool keep_dims() const {
-    return GetField<uint8_t>(VT_KEEP_DIMS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<uint8_t>(verifier, VT_KEEP_DIMS, 1) &&
-           verifier.EndTable();
-  }
-  ReducerOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ReducerOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ReducerOptionsBuilder {
-  typedef ReducerOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_keep_dims(bool keep_dims) {
-    fbb_.AddElement<uint8_t>(ReducerOptions::VT_KEEP_DIMS, static_cast<uint8_t>(keep_dims), 0);
-  }
-  explicit ReducerOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ReducerOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ReducerOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ReducerOptions> CreateReducerOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    bool keep_dims = false) {
-  ReducerOptionsBuilder builder_(_fbb);
-  builder_.add_keep_dims(keep_dims);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ReducerOptions> CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SqueezeOptionsT : public flatbuffers::NativeTable {
-  typedef SqueezeOptions TableType;
-  std::vector<int32_t> squeeze_dims{};
-};
-
-struct SqueezeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SqueezeOptionsT NativeTableType;
-  typedef SqueezeOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_SQUEEZE_DIMS = 4
-  };
-  const flatbuffers::Vector<int32_t> *squeeze_dims() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_SQUEEZE_DIMS);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_SQUEEZE_DIMS) &&
-           verifier.VerifyVector(squeeze_dims()) &&
-           verifier.EndTable();
-  }
-  SqueezeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SqueezeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SqueezeOptionsBuilder {
-  typedef SqueezeOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_squeeze_dims(flatbuffers::Offset<flatbuffers::Vector<int32_t>> squeeze_dims) {
-    fbb_.AddOffset(SqueezeOptions::VT_SQUEEZE_DIMS, squeeze_dims);
-  }
-  explicit SqueezeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SqueezeOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SqueezeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SqueezeOptions> CreateSqueezeOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> squeeze_dims = 0) {
-  SqueezeOptionsBuilder builder_(_fbb);
-  builder_.add_squeeze_dims(squeeze_dims);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<SqueezeOptions> CreateSqueezeOptionsDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<int32_t> *squeeze_dims = nullptr) {
-  auto squeeze_dims__ = squeeze_dims ? _fbb.CreateVector<int32_t>(*squeeze_dims) : 0;
-  return tflite::CreateSqueezeOptions(
-      _fbb,
-      squeeze_dims__);
-}
-
-flatbuffers::Offset<SqueezeOptions> CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SplitOptionsT : public flatbuffers::NativeTable {
-  typedef SplitOptions TableType;
-  int32_t num_splits = 0;
-};
-
-struct SplitOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SplitOptionsT NativeTableType;
-  typedef SplitOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_NUM_SPLITS = 4
-  };
-  int32_t num_splits() const {
-    return GetField<int32_t>(VT_NUM_SPLITS, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_NUM_SPLITS, 4) &&
-           verifier.EndTable();
-  }
-  SplitOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SplitOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SplitOptionsBuilder {
-  typedef SplitOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_num_splits(int32_t num_splits) {
-    fbb_.AddElement<int32_t>(SplitOptions::VT_NUM_SPLITS, num_splits, 0);
-  }
-  explicit SplitOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SplitOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SplitOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SplitOptions> CreateSplitOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t num_splits = 0) {
-  SplitOptionsBuilder builder_(_fbb);
-  builder_.add_num_splits(num_splits);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SplitOptions> CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SplitVOptionsT : public flatbuffers::NativeTable {
-  typedef SplitVOptions TableType;
-  int32_t num_splits = 0;
-};
-
-struct SplitVOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SplitVOptionsT NativeTableType;
-  typedef SplitVOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_NUM_SPLITS = 4
-  };
-  int32_t num_splits() const {
-    return GetField<int32_t>(VT_NUM_SPLITS, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_NUM_SPLITS, 4) &&
-           verifier.EndTable();
-  }
-  SplitVOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SplitVOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SplitVOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SplitVOptionsBuilder {
-  typedef SplitVOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_num_splits(int32_t num_splits) {
-    fbb_.AddElement<int32_t>(SplitVOptions::VT_NUM_SPLITS, num_splits, 0);
-  }
-  explicit SplitVOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SplitVOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SplitVOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t num_splits = 0) {
-  SplitVOptionsBuilder builder_(_fbb);
-  builder_.add_num_splits(num_splits);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct StridedSliceOptionsT : public flatbuffers::NativeTable {
-  typedef StridedSliceOptions TableType;
-  int32_t begin_mask = 0;
-  int32_t end_mask = 0;
-  int32_t ellipsis_mask = 0;
-  int32_t new_axis_mask = 0;
-  int32_t shrink_axis_mask = 0;
-};
-
-struct StridedSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef StridedSliceOptionsT NativeTableType;
-  typedef StridedSliceOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_BEGIN_MASK = 4,
-    VT_END_MASK = 6,
-    VT_ELLIPSIS_MASK = 8,
-    VT_NEW_AXIS_MASK = 10,
-    VT_SHRINK_AXIS_MASK = 12
-  };
-  int32_t begin_mask() const {
-    return GetField<int32_t>(VT_BEGIN_MASK, 0);
-  }
-  int32_t end_mask() const {
-    return GetField<int32_t>(VT_END_MASK, 0);
-  }
-  int32_t ellipsis_mask() const {
-    return GetField<int32_t>(VT_ELLIPSIS_MASK, 0);
-  }
-  int32_t new_axis_mask() const {
-    return GetField<int32_t>(VT_NEW_AXIS_MASK, 0);
-  }
-  int32_t shrink_axis_mask() const {
-    return GetField<int32_t>(VT_SHRINK_AXIS_MASK, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_BEGIN_MASK, 4) &&
-           VerifyField<int32_t>(verifier, VT_END_MASK, 4) &&
-           VerifyField<int32_t>(verifier, VT_ELLIPSIS_MASK, 4) &&
-           VerifyField<int32_t>(verifier, VT_NEW_AXIS_MASK, 4) &&
-           VerifyField<int32_t>(verifier, VT_SHRINK_AXIS_MASK, 4) &&
-           verifier.EndTable();
-  }
-  StridedSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<StridedSliceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct StridedSliceOptionsBuilder {
-  typedef StridedSliceOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_begin_mask(int32_t begin_mask) {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_BEGIN_MASK, begin_mask, 0);
-  }
-  void add_end_mask(int32_t end_mask) {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_END_MASK, end_mask, 0);
-  }
-  void add_ellipsis_mask(int32_t ellipsis_mask) {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_ELLIPSIS_MASK, ellipsis_mask, 0);
-  }
-  void add_new_axis_mask(int32_t new_axis_mask) {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_NEW_AXIS_MASK, new_axis_mask, 0);
-  }
-  void add_shrink_axis_mask(int32_t shrink_axis_mask) {
-    fbb_.AddElement<int32_t>(StridedSliceOptions::VT_SHRINK_AXIS_MASK, shrink_axis_mask, 0);
-  }
-  explicit StridedSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<StridedSliceOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<StridedSliceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<StridedSliceOptions> CreateStridedSliceOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t begin_mask = 0,
-    int32_t end_mask = 0,
-    int32_t ellipsis_mask = 0,
-    int32_t new_axis_mask = 0,
-    int32_t shrink_axis_mask = 0) {
-  StridedSliceOptionsBuilder builder_(_fbb);
-  builder_.add_shrink_axis_mask(shrink_axis_mask);
-  builder_.add_new_axis_mask(new_axis_mask);
-  builder_.add_ellipsis_mask(ellipsis_mask);
-  builder_.add_end_mask(end_mask);
-  builder_.add_begin_mask(begin_mask);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<StridedSliceOptions> CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct LogSoftmaxOptionsT : public flatbuffers::NativeTable {
-  typedef LogSoftmaxOptions TableType;
-};
-
-struct LogSoftmaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef LogSoftmaxOptionsT NativeTableType;
-  typedef LogSoftmaxOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  LogSoftmaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<LogSoftmaxOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct LogSoftmaxOptionsBuilder {
-  typedef LogSoftmaxOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LogSoftmaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<LogSoftmaxOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LogSoftmaxOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LogSoftmaxOptions> CreateLogSoftmaxOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  LogSoftmaxOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<LogSoftmaxOptions> CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct CastOptionsT : public flatbuffers::NativeTable {
-  typedef CastOptions TableType;
-  tflite::TensorType in_data_type = tflite::TensorType_FLOAT32;
-  tflite::TensorType out_data_type = tflite::TensorType_FLOAT32;
-};
-
-struct CastOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef CastOptionsT NativeTableType;
-  typedef CastOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_IN_DATA_TYPE = 4,
-    VT_OUT_DATA_TYPE = 6
-  };
-  tflite::TensorType in_data_type() const {
-    return static_cast<tflite::TensorType>(GetField<int8_t>(VT_IN_DATA_TYPE, 0));
-  }
-  tflite::TensorType out_data_type() const {
-    return static_cast<tflite::TensorType>(GetField<int8_t>(VT_OUT_DATA_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_IN_DATA_TYPE, 1) &&
-           VerifyField<int8_t>(verifier, VT_OUT_DATA_TYPE, 1) &&
-           verifier.EndTable();
-  }
-  CastOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<CastOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct CastOptionsBuilder {
-  typedef CastOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_in_data_type(tflite::TensorType in_data_type) {
-    fbb_.AddElement<int8_t>(CastOptions::VT_IN_DATA_TYPE, static_cast<int8_t>(in_data_type), 0);
-  }
-  void add_out_data_type(tflite::TensorType out_data_type) {
-    fbb_.AddElement<int8_t>(CastOptions::VT_OUT_DATA_TYPE, static_cast<int8_t>(out_data_type), 0);
-  }
-  explicit CastOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<CastOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<CastOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<CastOptions> CreateCastOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::TensorType in_data_type = tflite::TensorType_FLOAT32,
-    tflite::TensorType out_data_type = tflite::TensorType_FLOAT32) {
-  CastOptionsBuilder builder_(_fbb);
-  builder_.add_out_data_type(out_data_type);
-  builder_.add_in_data_type(in_data_type);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<CastOptions> CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct DequantizeOptionsT : public flatbuffers::NativeTable {
-  typedef DequantizeOptions TableType;
-};
-
-struct DequantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef DequantizeOptionsT NativeTableType;
-  typedef DequantizeOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  DequantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<DequantizeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct DequantizeOptionsBuilder {
-  typedef DequantizeOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit DequantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<DequantizeOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DequantizeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DequantizeOptions> CreateDequantizeOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  DequantizeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<DequantizeOptions> CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct MaximumMinimumOptionsT : public flatbuffers::NativeTable {
-  typedef MaximumMinimumOptions TableType;
-};
-
-struct MaximumMinimumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef MaximumMinimumOptionsT NativeTableType;
-  typedef MaximumMinimumOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  MaximumMinimumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(MaximumMinimumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<MaximumMinimumOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct MaximumMinimumOptionsBuilder {
-  typedef MaximumMinimumOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit MaximumMinimumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<MaximumMinimumOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<MaximumMinimumOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<MaximumMinimumOptions> CreateMaximumMinimumOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  MaximumMinimumOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<MaximumMinimumOptions> CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct TileOptionsT : public flatbuffers::NativeTable {
-  typedef TileOptions TableType;
-};
-
-struct TileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef TileOptionsT NativeTableType;
-  typedef TileOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  TileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(TileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<TileOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct TileOptionsBuilder {
-  typedef TileOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit TileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<TileOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TileOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TileOptions> CreateTileOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  TileOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<TileOptions> CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ArgMaxOptionsT : public flatbuffers::NativeTable {
-  typedef ArgMaxOptions TableType;
-  tflite::TensorType output_type = tflite::TensorType_FLOAT32;
-};
-
-struct ArgMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ArgMaxOptionsT NativeTableType;
-  typedef ArgMaxOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_OUTPUT_TYPE = 4
-  };
-  tflite::TensorType output_type() const {
-    return static_cast<tflite::TensorType>(GetField<int8_t>(VT_OUTPUT_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_OUTPUT_TYPE, 1) &&
-           verifier.EndTable();
-  }
-  ArgMaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ArgMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ArgMaxOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ArgMaxOptionsBuilder {
-  typedef ArgMaxOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_output_type(tflite::TensorType output_type) {
-    fbb_.AddElement<int8_t>(ArgMaxOptions::VT_OUTPUT_TYPE, static_cast<int8_t>(output_type), 0);
-  }
-  explicit ArgMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ArgMaxOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ArgMaxOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ArgMaxOptions> CreateArgMaxOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::TensorType output_type = tflite::TensorType_FLOAT32) {
-  ArgMaxOptionsBuilder builder_(_fbb);
-  builder_.add_output_type(output_type);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ArgMaxOptions> CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ArgMinOptionsT : public flatbuffers::NativeTable {
-  typedef ArgMinOptions TableType;
-  tflite::TensorType output_type = tflite::TensorType_FLOAT32;
-};
-
-struct ArgMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ArgMinOptionsT NativeTableType;
-  typedef ArgMinOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_OUTPUT_TYPE = 4
-  };
-  tflite::TensorType output_type() const {
-    return static_cast<tflite::TensorType>(GetField<int8_t>(VT_OUTPUT_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_OUTPUT_TYPE, 1) &&
-           verifier.EndTable();
-  }
-  ArgMinOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ArgMinOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ArgMinOptionsBuilder {
-  typedef ArgMinOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_output_type(tflite::TensorType output_type) {
-    fbb_.AddElement<int8_t>(ArgMinOptions::VT_OUTPUT_TYPE, static_cast<int8_t>(output_type), 0);
-  }
-  explicit ArgMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ArgMinOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ArgMinOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ArgMinOptions> CreateArgMinOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::TensorType output_type = tflite::TensorType_FLOAT32) {
-  ArgMinOptionsBuilder builder_(_fbb);
-  builder_.add_output_type(output_type);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ArgMinOptions> CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct GreaterOptionsT : public flatbuffers::NativeTable {
-  typedef GreaterOptions TableType;
-};
-
-struct GreaterOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef GreaterOptionsT NativeTableType;
-  typedef GreaterOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  GreaterOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<GreaterOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct GreaterOptionsBuilder {
-  typedef GreaterOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit GreaterOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<GreaterOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<GreaterOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<GreaterOptions> CreateGreaterOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  GreaterOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<GreaterOptions> CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct GreaterEqualOptionsT : public flatbuffers::NativeTable {
-  typedef GreaterEqualOptions TableType;
-};
-
-struct GreaterEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef GreaterEqualOptionsT NativeTableType;
-  typedef GreaterEqualOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  GreaterEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<GreaterEqualOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct GreaterEqualOptionsBuilder {
-  typedef GreaterEqualOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit GreaterEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<GreaterEqualOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<GreaterEqualOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<GreaterEqualOptions> CreateGreaterEqualOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  GreaterEqualOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<GreaterEqualOptions> CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct LessOptionsT : public flatbuffers::NativeTable {
-  typedef LessOptions TableType;
-};
-
-struct LessOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef LessOptionsT NativeTableType;
-  typedef LessOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  LessOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(LessOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<LessOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct LessOptionsBuilder {
-  typedef LessOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LessOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<LessOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LessOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LessOptions> CreateLessOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  LessOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<LessOptions> CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct LessEqualOptionsT : public flatbuffers::NativeTable {
-  typedef LessEqualOptions TableType;
-};
-
-struct LessEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef LessEqualOptionsT NativeTableType;
-  typedef LessEqualOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  LessEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<LessEqualOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct LessEqualOptionsBuilder {
-  typedef LessEqualOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LessEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<LessEqualOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LessEqualOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LessEqualOptions> CreateLessEqualOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  LessEqualOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<LessEqualOptions> CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct NegOptionsT : public flatbuffers::NativeTable {
-  typedef NegOptions TableType;
-};
-
-struct NegOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef NegOptionsT NativeTableType;
-  typedef NegOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  NegOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<NegOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct NegOptionsBuilder {
-  typedef NegOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit NegOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<NegOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<NegOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<NegOptions> CreateNegOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  NegOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<NegOptions> CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SelectOptionsT : public flatbuffers::NativeTable {
-  typedef SelectOptions TableType;
-};
-
-struct SelectOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SelectOptionsT NativeTableType;
-  typedef SelectOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  SelectOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SelectOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SelectOptionsBuilder {
-  typedef SelectOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SelectOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SelectOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SelectOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SelectOptions> CreateSelectOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  SelectOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SelectOptions> CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SliceOptionsT : public flatbuffers::NativeTable {
-  typedef SliceOptions TableType;
-};
-
-struct SliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SliceOptionsT NativeTableType;
-  typedef SliceOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  SliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SliceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SliceOptionsBuilder {
-  typedef SliceOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SliceOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SliceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SliceOptions> CreateSliceOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  SliceOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SliceOptions> CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct TransposeConvOptionsT : public flatbuffers::NativeTable {
-  typedef TransposeConvOptions TableType;
-  tflite::Padding padding = tflite::Padding_SAME;
-  int32_t stride_w = 0;
-  int32_t stride_h = 0;
-};
-
-struct TransposeConvOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef TransposeConvOptionsT NativeTableType;
-  typedef TransposeConvOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_PADDING = 4,
-    VT_STRIDE_W = 6,
-    VT_STRIDE_H = 8
-  };
-  tflite::Padding padding() const {
-    return static_cast<tflite::Padding>(GetField<int8_t>(VT_PADDING, 0));
-  }
-  int32_t stride_w() const {
-    return GetField<int32_t>(VT_STRIDE_W, 0);
-  }
-  int32_t stride_h() const {
-    return GetField<int32_t>(VT_STRIDE_H, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_PADDING, 1) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_W, 4) &&
-           VerifyField<int32_t>(verifier, VT_STRIDE_H, 4) &&
-           verifier.EndTable();
-  }
-  TransposeConvOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(TransposeConvOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<TransposeConvOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct TransposeConvOptionsBuilder {
-  typedef TransposeConvOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_padding(tflite::Padding padding) {
-    fbb_.AddElement<int8_t>(TransposeConvOptions::VT_PADDING, static_cast<int8_t>(padding), 0);
-  }
-  void add_stride_w(int32_t stride_w) {
-    fbb_.AddElement<int32_t>(TransposeConvOptions::VT_STRIDE_W, stride_w, 0);
-  }
-  void add_stride_h(int32_t stride_h) {
-    fbb_.AddElement<int32_t>(TransposeConvOptions::VT_STRIDE_H, stride_h, 0);
-  }
-  explicit TransposeConvOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<TransposeConvOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TransposeConvOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TransposeConvOptions> CreateTransposeConvOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::Padding padding = tflite::Padding_SAME,
-    int32_t stride_w = 0,
-    int32_t stride_h = 0) {
-  TransposeConvOptionsBuilder builder_(_fbb);
-  builder_.add_stride_h(stride_h);
-  builder_.add_stride_w(stride_w);
-  builder_.add_padding(padding);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<TransposeConvOptions> CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ExpandDimsOptionsT : public flatbuffers::NativeTable {
-  typedef ExpandDimsOptions TableType;
-};
-
-struct ExpandDimsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ExpandDimsOptionsT NativeTableType;
-  typedef ExpandDimsOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  ExpandDimsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ExpandDimsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ExpandDimsOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ExpandDimsOptionsBuilder {
-  typedef ExpandDimsOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ExpandDimsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ExpandDimsOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ExpandDimsOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ExpandDimsOptions> CreateExpandDimsOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  ExpandDimsOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ExpandDimsOptions> CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SparseToDenseOptionsT : public flatbuffers::NativeTable {
-  typedef SparseToDenseOptions TableType;
-  bool validate_indices = false;
-};
-
-struct SparseToDenseOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SparseToDenseOptionsT NativeTableType;
-  typedef SparseToDenseOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_VALIDATE_INDICES = 4
-  };
-  bool validate_indices() const {
-    return GetField<uint8_t>(VT_VALIDATE_INDICES, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<uint8_t>(verifier, VT_VALIDATE_INDICES, 1) &&
-           verifier.EndTable();
-  }
-  SparseToDenseOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SparseToDenseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SparseToDenseOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SparseToDenseOptionsBuilder {
-  typedef SparseToDenseOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_validate_indices(bool validate_indices) {
-    fbb_.AddElement<uint8_t>(SparseToDenseOptions::VT_VALIDATE_INDICES, static_cast<uint8_t>(validate_indices), 0);
-  }
-  explicit SparseToDenseOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SparseToDenseOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SparseToDenseOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SparseToDenseOptions> CreateSparseToDenseOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    bool validate_indices = false) {
-  SparseToDenseOptionsBuilder builder_(_fbb);
-  builder_.add_validate_indices(validate_indices);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SparseToDenseOptions> CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct EqualOptionsT : public flatbuffers::NativeTable {
-  typedef EqualOptions TableType;
-};
-
-struct EqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef EqualOptionsT NativeTableType;
-  typedef EqualOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  EqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(EqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<EqualOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct EqualOptionsBuilder {
-  typedef EqualOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit EqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<EqualOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<EqualOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<EqualOptions> CreateEqualOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  EqualOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<EqualOptions> CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct NotEqualOptionsT : public flatbuffers::NativeTable {
-  typedef NotEqualOptions TableType;
-};
-
-struct NotEqualOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef NotEqualOptionsT NativeTableType;
-  typedef NotEqualOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  NotEqualOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(NotEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<NotEqualOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct NotEqualOptionsBuilder {
-  typedef NotEqualOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit NotEqualOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<NotEqualOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<NotEqualOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<NotEqualOptions> CreateNotEqualOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  NotEqualOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<NotEqualOptions> CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ShapeOptionsT : public flatbuffers::NativeTable {
-  typedef ShapeOptions TableType;
-  tflite::TensorType out_type = tflite::TensorType_FLOAT32;
-};
-
-struct ShapeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ShapeOptionsT NativeTableType;
-  typedef ShapeOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_OUT_TYPE = 4
-  };
-  tflite::TensorType out_type() const {
-    return static_cast<tflite::TensorType>(GetField<int8_t>(VT_OUT_TYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_OUT_TYPE, 1) &&
-           verifier.EndTable();
-  }
-  ShapeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ShapeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ShapeOptionsBuilder {
-  typedef ShapeOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_out_type(tflite::TensorType out_type) {
-    fbb_.AddElement<int8_t>(ShapeOptions::VT_OUT_TYPE, static_cast<int8_t>(out_type), 0);
-  }
-  explicit ShapeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ShapeOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ShapeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ShapeOptions> CreateShapeOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::TensorType out_type = tflite::TensorType_FLOAT32) {
-  ShapeOptionsBuilder builder_(_fbb);
-  builder_.add_out_type(out_type);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ShapeOptions> CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct RankOptionsT : public flatbuffers::NativeTable {
-  typedef RankOptions TableType;
-};
-
-struct RankOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef RankOptionsT NativeTableType;
-  typedef RankOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  RankOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(RankOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<RankOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct RankOptionsBuilder {
-  typedef RankOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit RankOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<RankOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<RankOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<RankOptions> CreateRankOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  RankOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<RankOptions> CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct PowOptionsT : public flatbuffers::NativeTable {
-  typedef PowOptions TableType;
-};
-
-struct PowOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef PowOptionsT NativeTableType;
-  typedef PowOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  PowOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<PowOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct PowOptionsBuilder {
-  typedef PowOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit PowOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<PowOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<PowOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<PowOptions> CreatePowOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  PowOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<PowOptions> CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct FakeQuantOptionsT : public flatbuffers::NativeTable {
-  typedef FakeQuantOptions TableType;
-  float min = 0.0f;
-  float max = 0.0f;
-  int32_t num_bits = 0;
-  bool narrow_range = false;
-};
-
-struct FakeQuantOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef FakeQuantOptionsT NativeTableType;
-  typedef FakeQuantOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_MIN = 4,
-    VT_MAX = 6,
-    VT_NUM_BITS = 8,
-    VT_NARROW_RANGE = 10
-  };
-  float min() const {
-    return GetField<float>(VT_MIN, 0.0f);
-  }
-  float max() const {
-    return GetField<float>(VT_MAX, 0.0f);
-  }
-  int32_t num_bits() const {
-    return GetField<int32_t>(VT_NUM_BITS, 0);
-  }
-  bool narrow_range() const {
-    return GetField<uint8_t>(VT_NARROW_RANGE, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<float>(verifier, VT_MIN, 4) &&
-           VerifyField<float>(verifier, VT_MAX, 4) &&
-           VerifyField<int32_t>(verifier, VT_NUM_BITS, 4) &&
-           VerifyField<uint8_t>(verifier, VT_NARROW_RANGE, 1) &&
-           verifier.EndTable();
-  }
-  FakeQuantOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<FakeQuantOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct FakeQuantOptionsBuilder {
-  typedef FakeQuantOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_min(float min) {
-    fbb_.AddElement<float>(FakeQuantOptions::VT_MIN, min, 0.0f);
-  }
-  void add_max(float max) {
-    fbb_.AddElement<float>(FakeQuantOptions::VT_MAX, max, 0.0f);
-  }
-  void add_num_bits(int32_t num_bits) {
-    fbb_.AddElement<int32_t>(FakeQuantOptions::VT_NUM_BITS, num_bits, 0);
-  }
-  void add_narrow_range(bool narrow_range) {
-    fbb_.AddElement<uint8_t>(FakeQuantOptions::VT_NARROW_RANGE, static_cast<uint8_t>(narrow_range), 0);
-  }
-  explicit FakeQuantOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<FakeQuantOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FakeQuantOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FakeQuantOptions> CreateFakeQuantOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    float min = 0.0f,
-    float max = 0.0f,
-    int32_t num_bits = 0,
-    bool narrow_range = false) {
-  FakeQuantOptionsBuilder builder_(_fbb);
-  builder_.add_num_bits(num_bits);
-  builder_.add_max(max);
-  builder_.add_min(min);
-  builder_.add_narrow_range(narrow_range);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<FakeQuantOptions> CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct PackOptionsT : public flatbuffers::NativeTable {
-  typedef PackOptions TableType;
-  int32_t values_count = 0;
-  int32_t axis = 0;
-};
-
-struct PackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef PackOptionsT NativeTableType;
-  typedef PackOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_VALUES_COUNT = 4,
-    VT_AXIS = 6
-  };
-  int32_t values_count() const {
-    return GetField<int32_t>(VT_VALUES_COUNT, 0);
-  }
-  int32_t axis() const {
-    return GetField<int32_t>(VT_AXIS, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_VALUES_COUNT, 4) &&
-           VerifyField<int32_t>(verifier, VT_AXIS, 4) &&
-           verifier.EndTable();
-  }
-  PackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<PackOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct PackOptionsBuilder {
-  typedef PackOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_values_count(int32_t values_count) {
-    fbb_.AddElement<int32_t>(PackOptions::VT_VALUES_COUNT, values_count, 0);
-  }
-  void add_axis(int32_t axis) {
-    fbb_.AddElement<int32_t>(PackOptions::VT_AXIS, axis, 0);
-  }
-  explicit PackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<PackOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<PackOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<PackOptions> CreatePackOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t values_count = 0,
-    int32_t axis = 0) {
-  PackOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  builder_.add_values_count(values_count);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<PackOptions> CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct LogicalOrOptionsT : public flatbuffers::NativeTable {
-  typedef LogicalOrOptions TableType;
-};
-
-struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef LogicalOrOptionsT NativeTableType;
-  typedef LogicalOrOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  LogicalOrOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<LogicalOrOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct LogicalOrOptionsBuilder {
-  typedef LogicalOrOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LogicalOrOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<LogicalOrOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LogicalOrOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LogicalOrOptions> CreateLogicalOrOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  LogicalOrOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<LogicalOrOptions> CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct OneHotOptionsT : public flatbuffers::NativeTable {
-  typedef OneHotOptions TableType;
-  int32_t axis = 0;
-};
-
-struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef OneHotOptionsT NativeTableType;
-  typedef OneHotOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_AXIS = 4
-  };
-  int32_t axis() const {
-    return GetField<int32_t>(VT_AXIS, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_AXIS, 4) &&
-           verifier.EndTable();
-  }
-  OneHotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<OneHotOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct OneHotOptionsBuilder {
-  typedef OneHotOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_axis(int32_t axis) {
-    fbb_.AddElement<int32_t>(OneHotOptions::VT_AXIS, axis, 0);
-  }
-  explicit OneHotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<OneHotOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<OneHotOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t axis = 0) {
-  OneHotOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct AbsOptionsT : public flatbuffers::NativeTable {
-  typedef AbsOptions TableType;
-};
-
-struct AbsOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef AbsOptionsT NativeTableType;
-  typedef AbsOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  AbsOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(AbsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<AbsOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct AbsOptionsBuilder {
-  typedef AbsOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit AbsOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<AbsOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<AbsOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<AbsOptions> CreateAbsOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  AbsOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<AbsOptions> CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct HardSwishOptionsT : public flatbuffers::NativeTable {
-  typedef HardSwishOptions TableType;
-};
-
-struct HardSwishOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef HardSwishOptionsT NativeTableType;
-  typedef HardSwishOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  HardSwishOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(HardSwishOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<HardSwishOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct HardSwishOptionsBuilder {
-  typedef HardSwishOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit HardSwishOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<HardSwishOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<HardSwishOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<HardSwishOptions> CreateHardSwishOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  HardSwishOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<HardSwishOptions> CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct LogicalAndOptionsT : public flatbuffers::NativeTable {
-  typedef LogicalAndOptions TableType;
-};
-
-struct LogicalAndOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef LogicalAndOptionsT NativeTableType;
-  typedef LogicalAndOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  LogicalAndOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(LogicalAndOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<LogicalAndOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct LogicalAndOptionsBuilder {
-  typedef LogicalAndOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LogicalAndOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<LogicalAndOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LogicalAndOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LogicalAndOptions> CreateLogicalAndOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  LogicalAndOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<LogicalAndOptions> CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct LogicalNotOptionsT : public flatbuffers::NativeTable {
-  typedef LogicalNotOptions TableType;
-};
-
-struct LogicalNotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef LogicalNotOptionsT NativeTableType;
-  typedef LogicalNotOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  LogicalNotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(LogicalNotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<LogicalNotOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct LogicalNotOptionsBuilder {
-  typedef LogicalNotOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit LogicalNotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<LogicalNotOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LogicalNotOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LogicalNotOptions> CreateLogicalNotOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  LogicalNotOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<LogicalNotOptions> CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct UnpackOptionsT : public flatbuffers::NativeTable {
-  typedef UnpackOptions TableType;
-  int32_t num = 0;
-  int32_t axis = 0;
-};
-
-struct UnpackOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef UnpackOptionsT NativeTableType;
-  typedef UnpackOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_NUM = 4,
-    VT_AXIS = 6
-  };
-  int32_t num() const {
-    return GetField<int32_t>(VT_NUM, 0);
-  }
-  int32_t axis() const {
-    return GetField<int32_t>(VT_AXIS, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_NUM, 4) &&
-           VerifyField<int32_t>(verifier, VT_AXIS, 4) &&
-           verifier.EndTable();
-  }
-  UnpackOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(UnpackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<UnpackOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct UnpackOptionsBuilder {
-  typedef UnpackOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_num(int32_t num) {
-    fbb_.AddElement<int32_t>(UnpackOptions::VT_NUM, num, 0);
-  }
-  void add_axis(int32_t axis) {
-    fbb_.AddElement<int32_t>(UnpackOptions::VT_AXIS, axis, 0);
-  }
-  explicit UnpackOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<UnpackOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<UnpackOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t num = 0,
-    int32_t axis = 0) {
-  UnpackOptionsBuilder builder_(_fbb);
-  builder_.add_axis(axis);
-  builder_.add_num(num);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct FloorDivOptionsT : public flatbuffers::NativeTable {
-  typedef FloorDivOptions TableType;
-};
-
-struct FloorDivOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef FloorDivOptionsT NativeTableType;
-  typedef FloorDivOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  FloorDivOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(FloorDivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<FloorDivOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct FloorDivOptionsBuilder {
-  typedef FloorDivOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit FloorDivOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<FloorDivOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FloorDivOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FloorDivOptions> CreateFloorDivOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  FloorDivOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<FloorDivOptions> CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SquareOptionsT : public flatbuffers::NativeTable {
-  typedef SquareOptions TableType;
-};
-
-struct SquareOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SquareOptionsT NativeTableType;
-  typedef SquareOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  SquareOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SquareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SquareOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SquareOptionsBuilder {
-  typedef SquareOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SquareOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SquareOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SquareOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SquareOptions> CreateSquareOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  SquareOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SquareOptions> CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ZerosLikeOptionsT : public flatbuffers::NativeTable {
-  typedef ZerosLikeOptions TableType;
-};
-
-struct ZerosLikeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ZerosLikeOptionsT NativeTableType;
-  typedef ZerosLikeOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  ZerosLikeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ZerosLikeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ZerosLikeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ZerosLikeOptionsBuilder {
-  typedef ZerosLikeOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ZerosLikeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ZerosLikeOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ZerosLikeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ZerosLikeOptions> CreateZerosLikeOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  ZerosLikeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ZerosLikeOptions> CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct FillOptionsT : public flatbuffers::NativeTable {
-  typedef FillOptions TableType;
-};
-
-struct FillOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef FillOptionsT NativeTableType;
-  typedef FillOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  FillOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(FillOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<FillOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct FillOptionsBuilder {
-  typedef FillOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit FillOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<FillOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FillOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FillOptions> CreateFillOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  FillOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<FillOptions> CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct FloorModOptionsT : public flatbuffers::NativeTable {
-  typedef FloorModOptions TableType;
-};
-
-struct FloorModOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef FloorModOptionsT NativeTableType;
-  typedef FloorModOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  FloorModOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(FloorModOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<FloorModOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct FloorModOptionsBuilder {
-  typedef FloorModOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit FloorModOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<FloorModOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<FloorModOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<FloorModOptions> CreateFloorModOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  FloorModOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<FloorModOptions> CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct RangeOptionsT : public flatbuffers::NativeTable {
-  typedef RangeOptions TableType;
-};
-
-struct RangeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef RangeOptionsT NativeTableType;
-  typedef RangeOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  RangeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(RangeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<RangeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct RangeOptionsBuilder {
-  typedef RangeOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit RangeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<RangeOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<RangeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<RangeOptions> CreateRangeOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  RangeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<RangeOptions> CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct LeakyReluOptionsT : public flatbuffers::NativeTable {
-  typedef LeakyReluOptions TableType;
-  float alpha = 0.0f;
-};
-
-struct LeakyReluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef LeakyReluOptionsT NativeTableType;
-  typedef LeakyReluOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_ALPHA = 4
-  };
-  float alpha() const {
-    return GetField<float>(VT_ALPHA, 0.0f);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<float>(verifier, VT_ALPHA, 4) &&
-           verifier.EndTable();
-  }
-  LeakyReluOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(LeakyReluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<LeakyReluOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct LeakyReluOptionsBuilder {
-  typedef LeakyReluOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_alpha(float alpha) {
-    fbb_.AddElement<float>(LeakyReluOptions::VT_ALPHA, alpha, 0.0f);
-  }
-  explicit LeakyReluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<LeakyReluOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<LeakyReluOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<LeakyReluOptions> CreateLeakyReluOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    float alpha = 0.0f) {
-  LeakyReluOptionsBuilder builder_(_fbb);
-  builder_.add_alpha(alpha);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<LeakyReluOptions> CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SquaredDifferenceOptionsT : public flatbuffers::NativeTable {
-  typedef SquaredDifferenceOptions TableType;
-};
-
-struct SquaredDifferenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SquaredDifferenceOptionsT NativeTableType;
-  typedef SquaredDifferenceOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  SquaredDifferenceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SquaredDifferenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SquaredDifferenceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SquaredDifferenceOptionsBuilder {
-  typedef SquaredDifferenceOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SquaredDifferenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SquaredDifferenceOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SquaredDifferenceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SquaredDifferenceOptions> CreateSquaredDifferenceOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  SquaredDifferenceOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SquaredDifferenceOptions> CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct MirrorPadOptionsT : public flatbuffers::NativeTable {
-  typedef MirrorPadOptions TableType;
-  tflite::MirrorPadMode mode = tflite::MirrorPadMode_REFLECT;
-};
-
-struct MirrorPadOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef MirrorPadOptionsT NativeTableType;
-  typedef MirrorPadOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_MODE = 4
-  };
-  tflite::MirrorPadMode mode() const {
-    return static_cast<tflite::MirrorPadMode>(GetField<int8_t>(VT_MODE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_MODE, 1) &&
-           verifier.EndTable();
-  }
-  MirrorPadOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(MirrorPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<MirrorPadOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct MirrorPadOptionsBuilder {
-  typedef MirrorPadOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_mode(tflite::MirrorPadMode mode) {
-    fbb_.AddElement<int8_t>(MirrorPadOptions::VT_MODE, static_cast<int8_t>(mode), 0);
-  }
-  explicit MirrorPadOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<MirrorPadOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<MirrorPadOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<MirrorPadOptions> CreateMirrorPadOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::MirrorPadMode mode = tflite::MirrorPadMode_REFLECT) {
-  MirrorPadOptionsBuilder builder_(_fbb);
-  builder_.add_mode(mode);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<MirrorPadOptions> CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct UniqueOptionsT : public flatbuffers::NativeTable {
-  typedef UniqueOptions TableType;
-  tflite::TensorType idx_out_type = tflite::TensorType_INT32;
-};
-
-struct UniqueOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef UniqueOptionsT NativeTableType;
-  typedef UniqueOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_IDX_OUT_TYPE = 4
-  };
-  tflite::TensorType idx_out_type() const {
-    return static_cast<tflite::TensorType>(GetField<int8_t>(VT_IDX_OUT_TYPE, 2));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_IDX_OUT_TYPE, 1) &&
-           verifier.EndTable();
-  }
-  UniqueOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(UniqueOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<UniqueOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct UniqueOptionsBuilder {
-  typedef UniqueOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_idx_out_type(tflite::TensorType idx_out_type) {
-    fbb_.AddElement<int8_t>(UniqueOptions::VT_IDX_OUT_TYPE, static_cast<int8_t>(idx_out_type), 2);
-  }
-  explicit UniqueOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<UniqueOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<UniqueOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<UniqueOptions> CreateUniqueOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    tflite::TensorType idx_out_type = tflite::TensorType_INT32) {
-  UniqueOptionsBuilder builder_(_fbb);
-  builder_.add_idx_out_type(idx_out_type);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<UniqueOptions> CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ReverseV2OptionsT : public flatbuffers::NativeTable {
-  typedef ReverseV2Options TableType;
-};
-
-struct ReverseV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ReverseV2OptionsT NativeTableType;
-  typedef ReverseV2OptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  ReverseV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ReverseV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ReverseV2Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ReverseV2OptionsBuilder {
-  typedef ReverseV2Options Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ReverseV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ReverseV2Options> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ReverseV2Options>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ReverseV2Options> CreateReverseV2Options(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  ReverseV2OptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ReverseV2Options> CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct AddNOptionsT : public flatbuffers::NativeTable {
-  typedef AddNOptions TableType;
-};
-
-struct AddNOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef AddNOptionsT NativeTableType;
-  typedef AddNOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  AddNOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(AddNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<AddNOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct AddNOptionsBuilder {
-  typedef AddNOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit AddNOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<AddNOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<AddNOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<AddNOptions> CreateAddNOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  AddNOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<AddNOptions> CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct GatherNdOptionsT : public flatbuffers::NativeTable {
-  typedef GatherNdOptions TableType;
-};
-
-struct GatherNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef GatherNdOptionsT NativeTableType;
-  typedef GatherNdOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  GatherNdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(GatherNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<GatherNdOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct GatherNdOptionsBuilder {
-  typedef GatherNdOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit GatherNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<GatherNdOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<GatherNdOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<GatherNdOptions> CreateGatherNdOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  GatherNdOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<GatherNdOptions> CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct WhereOptionsT : public flatbuffers::NativeTable {
-  typedef WhereOptions TableType;
-};
-
-struct WhereOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef WhereOptionsT NativeTableType;
-  typedef WhereOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  WhereOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(WhereOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<WhereOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct WhereOptionsBuilder {
-  typedef WhereOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit WhereOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<WhereOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<WhereOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<WhereOptions> CreateWhereOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  WhereOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<WhereOptions> CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ReverseSequenceOptionsT : public flatbuffers::NativeTable {
-  typedef ReverseSequenceOptions TableType;
-  int32_t seq_dim = 0;
-  int32_t batch_dim = 0;
-};
-
-struct ReverseSequenceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ReverseSequenceOptionsT NativeTableType;
-  typedef ReverseSequenceOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_SEQ_DIM = 4,
-    VT_BATCH_DIM = 6
-  };
-  int32_t seq_dim() const {
-    return GetField<int32_t>(VT_SEQ_DIM, 0);
-  }
-  int32_t batch_dim() const {
-    return GetField<int32_t>(VT_BATCH_DIM, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_SEQ_DIM, 4) &&
-           VerifyField<int32_t>(verifier, VT_BATCH_DIM, 4) &&
-           verifier.EndTable();
-  }
-  ReverseSequenceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ReverseSequenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ReverseSequenceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ReverseSequenceOptionsBuilder {
-  typedef ReverseSequenceOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_seq_dim(int32_t seq_dim) {
-    fbb_.AddElement<int32_t>(ReverseSequenceOptions::VT_SEQ_DIM, seq_dim, 0);
-  }
-  void add_batch_dim(int32_t batch_dim) {
-    fbb_.AddElement<int32_t>(ReverseSequenceOptions::VT_BATCH_DIM, batch_dim, 0);
-  }
-  explicit ReverseSequenceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ReverseSequenceOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ReverseSequenceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ReverseSequenceOptions> CreateReverseSequenceOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t seq_dim = 0,
-    int32_t batch_dim = 0) {
-  ReverseSequenceOptionsBuilder builder_(_fbb);
-  builder_.add_batch_dim(batch_dim);
-  builder_.add_seq_dim(seq_dim);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ReverseSequenceOptions> CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct MatrixDiagOptionsT : public flatbuffers::NativeTable {
-  typedef MatrixDiagOptions TableType;
-};
-
-struct MatrixDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef MatrixDiagOptionsT NativeTableType;
-  typedef MatrixDiagOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  MatrixDiagOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(MatrixDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<MatrixDiagOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct MatrixDiagOptionsBuilder {
-  typedef MatrixDiagOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit MatrixDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<MatrixDiagOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<MatrixDiagOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<MatrixDiagOptions> CreateMatrixDiagOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  MatrixDiagOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<MatrixDiagOptions> CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct QuantizeOptionsT : public flatbuffers::NativeTable {
-  typedef QuantizeOptions TableType;
-};
-
-struct QuantizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef QuantizeOptionsT NativeTableType;
-  typedef QuantizeOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  QuantizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(QuantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<QuantizeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct QuantizeOptionsBuilder {
-  typedef QuantizeOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit QuantizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<QuantizeOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<QuantizeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<QuantizeOptions> CreateQuantizeOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  QuantizeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<QuantizeOptions> CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct MatrixSetDiagOptionsT : public flatbuffers::NativeTable {
-  typedef MatrixSetDiagOptions TableType;
-};
-
-struct MatrixSetDiagOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef MatrixSetDiagOptionsT NativeTableType;
-  typedef MatrixSetDiagOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  MatrixSetDiagOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(MatrixSetDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<MatrixSetDiagOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct MatrixSetDiagOptionsBuilder {
-  typedef MatrixSetDiagOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit MatrixSetDiagOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<MatrixSetDiagOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<MatrixSetDiagOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<MatrixSetDiagOptions> CreateMatrixSetDiagOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  MatrixSetDiagOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<MatrixSetDiagOptions> CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct IfOptionsT : public flatbuffers::NativeTable {
-  typedef IfOptions TableType;
-  int32_t then_subgraph_index = 0;
-  int32_t else_subgraph_index = 0;
-};
-
-struct IfOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef IfOptionsT NativeTableType;
-  typedef IfOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_THEN_SUBGRAPH_INDEX = 4,
-    VT_ELSE_SUBGRAPH_INDEX = 6
-  };
-  int32_t then_subgraph_index() const {
-    return GetField<int32_t>(VT_THEN_SUBGRAPH_INDEX, 0);
-  }
-  int32_t else_subgraph_index() const {
-    return GetField<int32_t>(VT_ELSE_SUBGRAPH_INDEX, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_THEN_SUBGRAPH_INDEX, 4) &&
-           VerifyField<int32_t>(verifier, VT_ELSE_SUBGRAPH_INDEX, 4) &&
-           verifier.EndTable();
-  }
-  IfOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(IfOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<IfOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct IfOptionsBuilder {
-  typedef IfOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_then_subgraph_index(int32_t then_subgraph_index) {
-    fbb_.AddElement<int32_t>(IfOptions::VT_THEN_SUBGRAPH_INDEX, then_subgraph_index, 0);
-  }
-  void add_else_subgraph_index(int32_t else_subgraph_index) {
-    fbb_.AddElement<int32_t>(IfOptions::VT_ELSE_SUBGRAPH_INDEX, else_subgraph_index, 0);
-  }
-  explicit IfOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<IfOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<IfOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<IfOptions> CreateIfOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t then_subgraph_index = 0,
-    int32_t else_subgraph_index = 0) {
-  IfOptionsBuilder builder_(_fbb);
-  builder_.add_else_subgraph_index(else_subgraph_index);
-  builder_.add_then_subgraph_index(then_subgraph_index);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<IfOptions> CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct CallOnceOptionsT : public flatbuffers::NativeTable {
-  typedef CallOnceOptions TableType;
-  int32_t init_subgraph_index = 0;
-};
-
-struct CallOnceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef CallOnceOptionsT NativeTableType;
-  typedef CallOnceOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_INIT_SUBGRAPH_INDEX = 4
-  };
-  int32_t init_subgraph_index() const {
-    return GetField<int32_t>(VT_INIT_SUBGRAPH_INDEX, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_INIT_SUBGRAPH_INDEX, 4) &&
-           verifier.EndTable();
-  }
-  CallOnceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(CallOnceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<CallOnceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct CallOnceOptionsBuilder {
-  typedef CallOnceOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_init_subgraph_index(int32_t init_subgraph_index) {
-    fbb_.AddElement<int32_t>(CallOnceOptions::VT_INIT_SUBGRAPH_INDEX, init_subgraph_index, 0);
-  }
-  explicit CallOnceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<CallOnceOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<CallOnceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<CallOnceOptions> CreateCallOnceOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t init_subgraph_index = 0) {
-  CallOnceOptionsBuilder builder_(_fbb);
-  builder_.add_init_subgraph_index(init_subgraph_index);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<CallOnceOptions> CreateCallOnceOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct WhileOptionsT : public flatbuffers::NativeTable {
-  typedef WhileOptions TableType;
-  int32_t cond_subgraph_index = 0;
-  int32_t body_subgraph_index = 0;
-};
-
-struct WhileOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef WhileOptionsT NativeTableType;
-  typedef WhileOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_COND_SUBGRAPH_INDEX = 4,
-    VT_BODY_SUBGRAPH_INDEX = 6
-  };
-  int32_t cond_subgraph_index() const {
-    return GetField<int32_t>(VT_COND_SUBGRAPH_INDEX, 0);
-  }
-  int32_t body_subgraph_index() const {
-    return GetField<int32_t>(VT_BODY_SUBGRAPH_INDEX, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_COND_SUBGRAPH_INDEX, 4) &&
-           VerifyField<int32_t>(verifier, VT_BODY_SUBGRAPH_INDEX, 4) &&
-           verifier.EndTable();
-  }
-  WhileOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(WhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<WhileOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct WhileOptionsBuilder {
-  typedef WhileOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_cond_subgraph_index(int32_t cond_subgraph_index) {
-    fbb_.AddElement<int32_t>(WhileOptions::VT_COND_SUBGRAPH_INDEX, cond_subgraph_index, 0);
-  }
-  void add_body_subgraph_index(int32_t body_subgraph_index) {
-    fbb_.AddElement<int32_t>(WhileOptions::VT_BODY_SUBGRAPH_INDEX, body_subgraph_index, 0);
-  }
-  explicit WhileOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<WhileOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<WhileOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<WhileOptions> CreateWhileOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t cond_subgraph_index = 0,
-    int32_t body_subgraph_index = 0) {
-  WhileOptionsBuilder builder_(_fbb);
-  builder_.add_body_subgraph_index(body_subgraph_index);
-  builder_.add_cond_subgraph_index(cond_subgraph_index);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<WhileOptions> CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct NonMaxSuppressionV4OptionsT : public flatbuffers::NativeTable {
-  typedef NonMaxSuppressionV4Options TableType;
-};
-
-struct NonMaxSuppressionV4Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef NonMaxSuppressionV4OptionsT NativeTableType;
-  typedef NonMaxSuppressionV4OptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  NonMaxSuppressionV4OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(NonMaxSuppressionV4OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<NonMaxSuppressionV4Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct NonMaxSuppressionV4OptionsBuilder {
-  typedef NonMaxSuppressionV4Options Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit NonMaxSuppressionV4OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<NonMaxSuppressionV4Options> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<NonMaxSuppressionV4Options>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<NonMaxSuppressionV4Options> CreateNonMaxSuppressionV4Options(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  NonMaxSuppressionV4OptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<NonMaxSuppressionV4Options> CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct NonMaxSuppressionV5OptionsT : public flatbuffers::NativeTable {
-  typedef NonMaxSuppressionV5Options TableType;
-};
-
-struct NonMaxSuppressionV5Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef NonMaxSuppressionV5OptionsT NativeTableType;
-  typedef NonMaxSuppressionV5OptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  NonMaxSuppressionV5OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(NonMaxSuppressionV5OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<NonMaxSuppressionV5Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct NonMaxSuppressionV5OptionsBuilder {
-  typedef NonMaxSuppressionV5Options Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit NonMaxSuppressionV5OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<NonMaxSuppressionV5Options> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<NonMaxSuppressionV5Options>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<NonMaxSuppressionV5Options> CreateNonMaxSuppressionV5Options(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  NonMaxSuppressionV5OptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<NonMaxSuppressionV5Options> CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ScatterNdOptionsT : public flatbuffers::NativeTable {
-  typedef ScatterNdOptions TableType;
-};
-
-struct ScatterNdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ScatterNdOptionsT NativeTableType;
-  typedef ScatterNdOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  ScatterNdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ScatterNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ScatterNdOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ScatterNdOptionsBuilder {
-  typedef ScatterNdOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ScatterNdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ScatterNdOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ScatterNdOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ScatterNdOptions> CreateScatterNdOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  ScatterNdOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ScatterNdOptions> CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SelectV2OptionsT : public flatbuffers::NativeTable {
-  typedef SelectV2Options TableType;
-};
-
-struct SelectV2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SelectV2OptionsT NativeTableType;
-  typedef SelectV2OptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  SelectV2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SelectV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SelectV2Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SelectV2OptionsBuilder {
-  typedef SelectV2Options Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SelectV2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SelectV2Options> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SelectV2Options>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SelectV2Options> CreateSelectV2Options(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  SelectV2OptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SelectV2Options> CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct DensifyOptionsT : public flatbuffers::NativeTable {
-  typedef DensifyOptions TableType;
-};
-
-struct DensifyOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef DensifyOptionsT NativeTableType;
-  typedef DensifyOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  DensifyOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(DensifyOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<DensifyOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct DensifyOptionsBuilder {
-  typedef DensifyOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit DensifyOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<DensifyOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DensifyOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DensifyOptions> CreateDensifyOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  DensifyOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<DensifyOptions> CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SegmentSumOptionsT : public flatbuffers::NativeTable {
-  typedef SegmentSumOptions TableType;
-};
-
-struct SegmentSumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SegmentSumOptionsT NativeTableType;
-  typedef SegmentSumOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  SegmentSumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SegmentSumOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SegmentSumOptionsBuilder {
-  typedef SegmentSumOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SegmentSumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SegmentSumOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SegmentSumOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SegmentSumOptions> CreateSegmentSumOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  SegmentSumOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SegmentSumOptions> CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct BatchMatMulOptionsT : public flatbuffers::NativeTable {
-  typedef BatchMatMulOptions TableType;
-  bool adj_x = false;
-  bool adj_y = false;
-  bool asymmetric_quantize_inputs = false;
-};
-
-struct BatchMatMulOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef BatchMatMulOptionsT NativeTableType;
-  typedef BatchMatMulOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_ADJ_X = 4,
-    VT_ADJ_Y = 6,
-    VT_ASYMMETRIC_QUANTIZE_INPUTS = 8
-  };
-  bool adj_x() const {
-    return GetField<uint8_t>(VT_ADJ_X, 0) != 0;
-  }
-  bool adj_y() const {
-    return GetField<uint8_t>(VT_ADJ_Y, 0) != 0;
-  }
-  bool asymmetric_quantize_inputs() const {
-    return GetField<uint8_t>(VT_ASYMMETRIC_QUANTIZE_INPUTS, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<uint8_t>(verifier, VT_ADJ_X, 1) &&
-           VerifyField<uint8_t>(verifier, VT_ADJ_Y, 1) &&
-           VerifyField<uint8_t>(verifier, VT_ASYMMETRIC_QUANTIZE_INPUTS, 1) &&
-           verifier.EndTable();
-  }
-  BatchMatMulOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(BatchMatMulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<BatchMatMulOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct BatchMatMulOptionsBuilder {
-  typedef BatchMatMulOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_adj_x(bool adj_x) {
-    fbb_.AddElement<uint8_t>(BatchMatMulOptions::VT_ADJ_X, static_cast<uint8_t>(adj_x), 0);
-  }
-  void add_adj_y(bool adj_y) {
-    fbb_.AddElement<uint8_t>(BatchMatMulOptions::VT_ADJ_Y, static_cast<uint8_t>(adj_y), 0);
-  }
-  void add_asymmetric_quantize_inputs(bool asymmetric_quantize_inputs) {
-    fbb_.AddElement<uint8_t>(BatchMatMulOptions::VT_ASYMMETRIC_QUANTIZE_INPUTS, static_cast<uint8_t>(asymmetric_quantize_inputs), 0);
-  }
-  explicit BatchMatMulOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<BatchMatMulOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<BatchMatMulOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<BatchMatMulOptions> CreateBatchMatMulOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    bool adj_x = false,
-    bool adj_y = false,
-    bool asymmetric_quantize_inputs = false) {
-  BatchMatMulOptionsBuilder builder_(_fbb);
-  builder_.add_asymmetric_quantize_inputs(asymmetric_quantize_inputs);
-  builder_.add_adj_y(adj_y);
-  builder_.add_adj_x(adj_x);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<BatchMatMulOptions> CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct CumsumOptionsT : public flatbuffers::NativeTable {
-  typedef CumsumOptions TableType;
-  bool exclusive = false;
-  bool reverse = false;
-};
-
-struct CumsumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef CumsumOptionsT NativeTableType;
-  typedef CumsumOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_EXCLUSIVE = 4,
-    VT_REVERSE = 6
-  };
-  bool exclusive() const {
-    return GetField<uint8_t>(VT_EXCLUSIVE, 0) != 0;
-  }
-  bool reverse() const {
-    return GetField<uint8_t>(VT_REVERSE, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<uint8_t>(verifier, VT_EXCLUSIVE, 1) &&
-           VerifyField<uint8_t>(verifier, VT_REVERSE, 1) &&
-           verifier.EndTable();
-  }
-  CumsumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(CumsumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<CumsumOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct CumsumOptionsBuilder {
-  typedef CumsumOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_exclusive(bool exclusive) {
-    fbb_.AddElement<uint8_t>(CumsumOptions::VT_EXCLUSIVE, static_cast<uint8_t>(exclusive), 0);
-  }
-  void add_reverse(bool reverse) {
-    fbb_.AddElement<uint8_t>(CumsumOptions::VT_REVERSE, static_cast<uint8_t>(reverse), 0);
-  }
-  explicit CumsumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<CumsumOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<CumsumOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<CumsumOptions> CreateCumsumOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    bool exclusive = false,
-    bool reverse = false) {
-  CumsumOptionsBuilder builder_(_fbb);
-  builder_.add_reverse(reverse);
-  builder_.add_exclusive(exclusive);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<CumsumOptions> CreateCumsumOptions(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct BroadcastToOptionsT : public flatbuffers::NativeTable {
-  typedef BroadcastToOptions TableType;
-};
-
-struct BroadcastToOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef BroadcastToOptionsT NativeTableType;
-  typedef BroadcastToOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  BroadcastToOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(BroadcastToOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<BroadcastToOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct BroadcastToOptionsBuilder {
-  typedef BroadcastToOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit BroadcastToOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<BroadcastToOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<BroadcastToOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<BroadcastToOptions> CreateBroadcastToOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  BroadcastToOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<BroadcastToOptions> CreateBroadcastToOptions(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct Rfft2dOptionsT : public flatbuffers::NativeTable {
-  typedef Rfft2dOptions TableType;
-};
-
-struct Rfft2dOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef Rfft2dOptionsT NativeTableType;
-  typedef Rfft2dOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  Rfft2dOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(Rfft2dOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<Rfft2dOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct Rfft2dOptionsBuilder {
-  typedef Rfft2dOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit Rfft2dOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<Rfft2dOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Rfft2dOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Rfft2dOptions> CreateRfft2dOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  Rfft2dOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<Rfft2dOptions> CreateRfft2dOptions(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct HashtableOptionsT : public flatbuffers::NativeTable {
-  typedef HashtableOptions TableType;
-  int32_t table_id = 0;
-  tflite::TensorType key_dtype = tflite::TensorType_FLOAT32;
-  tflite::TensorType value_dtype = tflite::TensorType_FLOAT32;
-};
-
-struct HashtableOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef HashtableOptionsT NativeTableType;
-  typedef HashtableOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_TABLE_ID = 4,
-    VT_KEY_DTYPE = 6,
-    VT_VALUE_DTYPE = 8
-  };
-  int32_t table_id() const {
-    return GetField<int32_t>(VT_TABLE_ID, 0);
-  }
-  tflite::TensorType key_dtype() const {
-    return static_cast<tflite::TensorType>(GetField<int8_t>(VT_KEY_DTYPE, 0));
-  }
-  tflite::TensorType value_dtype() const {
-    return static_cast<tflite::TensorType>(GetField<int8_t>(VT_VALUE_DTYPE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int32_t>(verifier, VT_TABLE_ID, 4) &&
-           VerifyField<int8_t>(verifier, VT_KEY_DTYPE, 1) &&
-           VerifyField<int8_t>(verifier, VT_VALUE_DTYPE, 1) &&
-           verifier.EndTable();
-  }
-  HashtableOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(HashtableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<HashtableOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct HashtableOptionsBuilder {
-  typedef HashtableOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_table_id(int32_t table_id) {
-    fbb_.AddElement<int32_t>(HashtableOptions::VT_TABLE_ID, table_id, 0);
-  }
-  void add_key_dtype(tflite::TensorType key_dtype) {
-    fbb_.AddElement<int8_t>(HashtableOptions::VT_KEY_DTYPE, static_cast<int8_t>(key_dtype), 0);
-  }
-  void add_value_dtype(tflite::TensorType value_dtype) {
-    fbb_.AddElement<int8_t>(HashtableOptions::VT_VALUE_DTYPE, static_cast<int8_t>(value_dtype), 0);
-  }
-  explicit HashtableOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<HashtableOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<HashtableOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<HashtableOptions> CreateHashtableOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int32_t table_id = 0,
-    tflite::TensorType key_dtype = tflite::TensorType_FLOAT32,
-    tflite::TensorType value_dtype = tflite::TensorType_FLOAT32) {
-  HashtableOptionsBuilder builder_(_fbb);
-  builder_.add_table_id(table_id);
-  builder_.add_value_dtype(value_dtype);
-  builder_.add_key_dtype(key_dtype);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<HashtableOptions> CreateHashtableOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct HashtableFindOptionsT : public flatbuffers::NativeTable {
-  typedef HashtableFindOptions TableType;
-};
-
-struct HashtableFindOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef HashtableFindOptionsT NativeTableType;
-  typedef HashtableFindOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  HashtableFindOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(HashtableFindOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<HashtableFindOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct HashtableFindOptionsBuilder {
-  typedef HashtableFindOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit HashtableFindOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<HashtableFindOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<HashtableFindOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<HashtableFindOptions> CreateHashtableFindOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  HashtableFindOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<HashtableFindOptions> CreateHashtableFindOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct HashtableImportOptionsT : public flatbuffers::NativeTable {
-  typedef HashtableImportOptions TableType;
-};
-
-struct HashtableImportOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef HashtableImportOptionsT NativeTableType;
-  typedef HashtableImportOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  HashtableImportOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(HashtableImportOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<HashtableImportOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct HashtableImportOptionsBuilder {
-  typedef HashtableImportOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit HashtableImportOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<HashtableImportOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<HashtableImportOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<HashtableImportOptions> CreateHashtableImportOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  HashtableImportOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<HashtableImportOptions> CreateHashtableImportOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct HashtableSizeOptionsT : public flatbuffers::NativeTable {
-  typedef HashtableSizeOptions TableType;
-};
-
-struct HashtableSizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef HashtableSizeOptionsT NativeTableType;
-  typedef HashtableSizeOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  HashtableSizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(HashtableSizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<HashtableSizeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct HashtableSizeOptionsBuilder {
-  typedef HashtableSizeOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit HashtableSizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<HashtableSizeOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<HashtableSizeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<HashtableSizeOptions> CreateHashtableSizeOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  HashtableSizeOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<HashtableSizeOptions> CreateHashtableSizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct VarHandleOptionsT : public flatbuffers::NativeTable {
-  typedef VarHandleOptions TableType;
-  std::string container{};
-  std::string shared_name{};
-};
-
-struct VarHandleOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef VarHandleOptionsT NativeTableType;
-  typedef VarHandleOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_CONTAINER = 4,
-    VT_SHARED_NAME = 6
-  };
-  const flatbuffers::String *container() const {
-    return GetPointer<const flatbuffers::String *>(VT_CONTAINER);
-  }
-  const flatbuffers::String *shared_name() const {
-    return GetPointer<const flatbuffers::String *>(VT_SHARED_NAME);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_CONTAINER) &&
-           verifier.VerifyString(container()) &&
-           VerifyOffset(verifier, VT_SHARED_NAME) &&
-           verifier.VerifyString(shared_name()) &&
-           verifier.EndTable();
-  }
-  VarHandleOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(VarHandleOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<VarHandleOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct VarHandleOptionsBuilder {
-  typedef VarHandleOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_container(flatbuffers::Offset<flatbuffers::String> container) {
-    fbb_.AddOffset(VarHandleOptions::VT_CONTAINER, container);
-  }
-  void add_shared_name(flatbuffers::Offset<flatbuffers::String> shared_name) {
-    fbb_.AddOffset(VarHandleOptions::VT_SHARED_NAME, shared_name);
-  }
-  explicit VarHandleOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<VarHandleOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<VarHandleOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<VarHandleOptions> CreateVarHandleOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::String> container = 0,
-    flatbuffers::Offset<flatbuffers::String> shared_name = 0) {
-  VarHandleOptionsBuilder builder_(_fbb);
-  builder_.add_shared_name(shared_name);
-  builder_.add_container(container);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<VarHandleOptions> CreateVarHandleOptionsDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const char *container = nullptr,
-    const char *shared_name = nullptr) {
-  auto container__ = container ? _fbb.CreateString(container) : 0;
-  auto shared_name__ = shared_name ? _fbb.CreateString(shared_name) : 0;
-  return tflite::CreateVarHandleOptions(
-      _fbb,
-      container__,
-      shared_name__);
-}
-
-flatbuffers::Offset<VarHandleOptions> CreateVarHandleOptions(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ReadVariableOptionsT : public flatbuffers::NativeTable {
-  typedef ReadVariableOptions TableType;
-};
-
-struct ReadVariableOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ReadVariableOptionsT NativeTableType;
-  typedef ReadVariableOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  ReadVariableOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ReadVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ReadVariableOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ReadVariableOptionsBuilder {
-  typedef ReadVariableOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ReadVariableOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ReadVariableOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ReadVariableOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ReadVariableOptions> CreateReadVariableOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  ReadVariableOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ReadVariableOptions> CreateReadVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct AssignVariableOptionsT : public flatbuffers::NativeTable {
-  typedef AssignVariableOptions TableType;
-};
-
-struct AssignVariableOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef AssignVariableOptionsT NativeTableType;
-  typedef AssignVariableOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  AssignVariableOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(AssignVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<AssignVariableOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct AssignVariableOptionsBuilder {
-  typedef AssignVariableOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit AssignVariableOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<AssignVariableOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<AssignVariableOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<AssignVariableOptions> CreateAssignVariableOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  AssignVariableOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<AssignVariableOptions> CreateAssignVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct RandomOptionsT : public flatbuffers::NativeTable {
-  typedef RandomOptions TableType;
-  int64_t seed = 0;
-  int64_t seed2 = 0;
-};
-
-struct RandomOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef RandomOptionsT NativeTableType;
-  typedef RandomOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_SEED = 4,
-    VT_SEED2 = 6
-  };
-  int64_t seed() const {
-    return GetField<int64_t>(VT_SEED, 0);
-  }
-  int64_t seed2() const {
-    return GetField<int64_t>(VT_SEED2, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int64_t>(verifier, VT_SEED, 8) &&
-           VerifyField<int64_t>(verifier, VT_SEED2, 8) &&
-           verifier.EndTable();
-  }
-  RandomOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(RandomOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<RandomOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct RandomOptionsBuilder {
-  typedef RandomOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_seed(int64_t seed) {
-    fbb_.AddElement<int64_t>(RandomOptions::VT_SEED, seed, 0);
-  }
-  void add_seed2(int64_t seed2) {
-    fbb_.AddElement<int64_t>(RandomOptions::VT_SEED2, seed2, 0);
-  }
-  explicit RandomOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<RandomOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<RandomOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<RandomOptions> CreateRandomOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int64_t seed = 0,
-    int64_t seed2 = 0) {
-  RandomOptionsBuilder builder_(_fbb);
-  builder_.add_seed2(seed2);
-  builder_.add_seed(seed);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<RandomOptions> CreateRandomOptions(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct BucketizeOptionsT : public flatbuffers::NativeTable {
-  typedef BucketizeOptions TableType;
-  std::vector<float> boundaries{};
-};
-
-struct BucketizeOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef BucketizeOptionsT NativeTableType;
-  typedef BucketizeOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_BOUNDARIES = 4
-  };
-  const flatbuffers::Vector<float> *boundaries() const {
-    return GetPointer<const flatbuffers::Vector<float> *>(VT_BOUNDARIES);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_BOUNDARIES) &&
-           verifier.VerifyVector(boundaries()) &&
-           verifier.EndTable();
-  }
-  BucketizeOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(BucketizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<BucketizeOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct BucketizeOptionsBuilder {
-  typedef BucketizeOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_boundaries(flatbuffers::Offset<flatbuffers::Vector<float>> boundaries) {
-    fbb_.AddOffset(BucketizeOptions::VT_BOUNDARIES, boundaries);
-  }
-  explicit BucketizeOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<BucketizeOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<BucketizeOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<BucketizeOptions> CreateBucketizeOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<float>> boundaries = 0) {
-  BucketizeOptionsBuilder builder_(_fbb);
-  builder_.add_boundaries(boundaries);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<BucketizeOptions> CreateBucketizeOptionsDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<float> *boundaries = nullptr) {
-  auto boundaries__ = boundaries ? _fbb.CreateVector<float>(*boundaries) : 0;
-  return tflite::CreateBucketizeOptions(
-      _fbb,
-      boundaries__);
-}
-
-flatbuffers::Offset<BucketizeOptions> CreateBucketizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct GeluOptionsT : public flatbuffers::NativeTable {
-  typedef GeluOptions TableType;
-  bool approximate = false;
-};
-
-struct GeluOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef GeluOptionsT NativeTableType;
-  typedef GeluOptionsBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_APPROXIMATE = 4
-  };
-  bool approximate() const {
-    return GetField<uint8_t>(VT_APPROXIMATE, 0) != 0;
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<uint8_t>(verifier, VT_APPROXIMATE, 1) &&
-           verifier.EndTable();
-  }
-  GeluOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(GeluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<GeluOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct GeluOptionsBuilder {
-  typedef GeluOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_approximate(bool approximate) {
-    fbb_.AddElement<uint8_t>(GeluOptions::VT_APPROXIMATE, static_cast<uint8_t>(approximate), 0);
-  }
-  explicit GeluOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<GeluOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<GeluOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<GeluOptions> CreateGeluOptions(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    bool approximate = false) {
-  GeluOptionsBuilder builder_(_fbb);
-  builder_.add_approximate(approximate);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<GeluOptions> CreateGeluOptions(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct DynamicUpdateSliceOptionsT : public flatbuffers::NativeTable {
-  typedef DynamicUpdateSliceOptions TableType;
-};
-
-struct DynamicUpdateSliceOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef DynamicUpdateSliceOptionsT NativeTableType;
-  typedef DynamicUpdateSliceOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  DynamicUpdateSliceOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(DynamicUpdateSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<DynamicUpdateSliceOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct DynamicUpdateSliceOptionsBuilder {
-  typedef DynamicUpdateSliceOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit DynamicUpdateSliceOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<DynamicUpdateSliceOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<DynamicUpdateSliceOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<DynamicUpdateSliceOptions> CreateDynamicUpdateSliceOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  DynamicUpdateSliceOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<DynamicUpdateSliceOptions> CreateDynamicUpdateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct UnsortedSegmentProdOptionsT : public flatbuffers::NativeTable {
-  typedef UnsortedSegmentProdOptions TableType;
-};
-
-struct UnsortedSegmentProdOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef UnsortedSegmentProdOptionsT NativeTableType;
-  typedef UnsortedSegmentProdOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  UnsortedSegmentProdOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(UnsortedSegmentProdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<UnsortedSegmentProdOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct UnsortedSegmentProdOptionsBuilder {
-  typedef UnsortedSegmentProdOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit UnsortedSegmentProdOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<UnsortedSegmentProdOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<UnsortedSegmentProdOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<UnsortedSegmentProdOptions> CreateUnsortedSegmentProdOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  UnsortedSegmentProdOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<UnsortedSegmentProdOptions> CreateUnsortedSegmentProdOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct UnsortedSegmentMaxOptionsT : public flatbuffers::NativeTable {
-  typedef UnsortedSegmentMaxOptions TableType;
-};
-
-struct UnsortedSegmentMaxOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef UnsortedSegmentMaxOptionsT NativeTableType;
-  typedef UnsortedSegmentMaxOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  UnsortedSegmentMaxOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(UnsortedSegmentMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<UnsortedSegmentMaxOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct UnsortedSegmentMaxOptionsBuilder {
-  typedef UnsortedSegmentMaxOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit UnsortedSegmentMaxOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<UnsortedSegmentMaxOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<UnsortedSegmentMaxOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<UnsortedSegmentMaxOptions> CreateUnsortedSegmentMaxOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  UnsortedSegmentMaxOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<UnsortedSegmentMaxOptions> CreateUnsortedSegmentMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct UnsortedSegmentSumOptionsT : public flatbuffers::NativeTable {
-  typedef UnsortedSegmentSumOptions TableType;
-};
-
-struct UnsortedSegmentSumOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef UnsortedSegmentSumOptionsT NativeTableType;
-  typedef UnsortedSegmentSumOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  UnsortedSegmentSumOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(UnsortedSegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<UnsortedSegmentSumOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct UnsortedSegmentSumOptionsBuilder {
-  typedef UnsortedSegmentSumOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit UnsortedSegmentSumOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<UnsortedSegmentSumOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<UnsortedSegmentSumOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<UnsortedSegmentSumOptions> CreateUnsortedSegmentSumOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  UnsortedSegmentSumOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<UnsortedSegmentSumOptions> CreateUnsortedSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ATan2OptionsT : public flatbuffers::NativeTable {
-  typedef ATan2Options TableType;
-};
-
-struct ATan2Options FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ATan2OptionsT NativeTableType;
-  typedef ATan2OptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  ATan2OptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ATan2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<ATan2Options> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ATan2OptionsBuilder {
-  typedef ATan2Options Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit ATan2OptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<ATan2Options> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<ATan2Options>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<ATan2Options> CreateATan2Options(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  ATan2OptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<ATan2Options> CreateATan2Options(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct UnsortedSegmentMinOptionsT : public flatbuffers::NativeTable {
-  typedef UnsortedSegmentMinOptions TableType;
-};
-
-struct UnsortedSegmentMinOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef UnsortedSegmentMinOptionsT NativeTableType;
-  typedef UnsortedSegmentMinOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  UnsortedSegmentMinOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(UnsortedSegmentMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<UnsortedSegmentMinOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct UnsortedSegmentMinOptionsBuilder {
-  typedef UnsortedSegmentMinOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit UnsortedSegmentMinOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<UnsortedSegmentMinOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<UnsortedSegmentMinOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<UnsortedSegmentMinOptions> CreateUnsortedSegmentMinOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  UnsortedSegmentMinOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<UnsortedSegmentMinOptions> CreateUnsortedSegmentMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SignOptionsT : public flatbuffers::NativeTable {
-  typedef SignOptions TableType;
-};
-
-struct SignOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SignOptionsT NativeTableType;
-  typedef SignOptionsBuilder Builder;
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           verifier.EndTable();
-  }
-  SignOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SignOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SignOptions> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SignOptionsBuilder {
-  typedef SignOptions Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  explicit SignOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SignOptions> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SignOptions>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SignOptions> CreateSignOptions(
-    flatbuffers::FlatBufferBuilder &_fbb) {
-  SignOptionsBuilder builder_(_fbb);
-  return builder_.Finish();
-}
-
-flatbuffers::Offset<SignOptions> CreateSignOptions(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct OperatorCodeT : public flatbuffers::NativeTable {
-  typedef OperatorCode TableType;
-  int8_t deprecated_builtin_code = 0;
-  std::string custom_code{};
-  int32_t version = 1;
-  tflite::BuiltinOperator builtin_code = tflite::BuiltinOperator_ADD;
-};
-
-struct OperatorCode FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef OperatorCodeT NativeTableType;
-  typedef OperatorCodeBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_DEPRECATED_BUILTIN_CODE = 4,
-    VT_CUSTOM_CODE = 6,
-    VT_VERSION = 8,
-    VT_BUILTIN_CODE = 10
-  };
-  int8_t deprecated_builtin_code() const {
-    return GetField<int8_t>(VT_DEPRECATED_BUILTIN_CODE, 0);
-  }
-  const flatbuffers::String *custom_code() const {
-    return GetPointer<const flatbuffers::String *>(VT_CUSTOM_CODE);
-  }
-  int32_t version() const {
-    return GetField<int32_t>(VT_VERSION, 1);
-  }
-  tflite::BuiltinOperator builtin_code() const {
-    return static_cast<tflite::BuiltinOperator>(GetField<int32_t>(VT_BUILTIN_CODE, 0));
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<int8_t>(verifier, VT_DEPRECATED_BUILTIN_CODE, 1) &&
-           VerifyOffset(verifier, VT_CUSTOM_CODE) &&
-           verifier.VerifyString(custom_code()) &&
-           VerifyField<int32_t>(verifier, VT_VERSION, 4) &&
-           VerifyField<int32_t>(verifier, VT_BUILTIN_CODE, 4) &&
-           verifier.EndTable();
-  }
-  OperatorCodeT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<OperatorCode> Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct OperatorCodeBuilder {
-  typedef OperatorCode Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_deprecated_builtin_code(int8_t deprecated_builtin_code) {
-    fbb_.AddElement<int8_t>(OperatorCode::VT_DEPRECATED_BUILTIN_CODE, deprecated_builtin_code, 0);
-  }
-  void add_custom_code(flatbuffers::Offset<flatbuffers::String> custom_code) {
-    fbb_.AddOffset(OperatorCode::VT_CUSTOM_CODE, custom_code);
-  }
-  void add_version(int32_t version) {
-    fbb_.AddElement<int32_t>(OperatorCode::VT_VERSION, version, 1);
-  }
-  void add_builtin_code(tflite::BuiltinOperator builtin_code) {
-    fbb_.AddElement<int32_t>(OperatorCode::VT_BUILTIN_CODE, static_cast<int32_t>(builtin_code), 0);
-  }
-  explicit OperatorCodeBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<OperatorCode> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<OperatorCode>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<OperatorCode> CreateOperatorCode(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int8_t deprecated_builtin_code = 0,
-    flatbuffers::Offset<flatbuffers::String> custom_code = 0,
-    int32_t version = 1,
-    tflite::BuiltinOperator builtin_code = tflite::BuiltinOperator_ADD) {
-  OperatorCodeBuilder builder_(_fbb);
-  builder_.add_builtin_code(builtin_code);
-  builder_.add_version(version);
-  builder_.add_custom_code(custom_code);
-  builder_.add_deprecated_builtin_code(deprecated_builtin_code);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<OperatorCode> CreateOperatorCodeDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    int8_t deprecated_builtin_code = 0,
-    const char *custom_code = nullptr,
-    int32_t version = 1,
-    tflite::BuiltinOperator builtin_code = tflite::BuiltinOperator_ADD) {
-  auto custom_code__ = custom_code ? _fbb.CreateString(custom_code) : 0;
-  return tflite::CreateOperatorCode(
-      _fbb,
-      deprecated_builtin_code,
-      custom_code__,
-      version,
-      builtin_code);
-}
-
-flatbuffers::Offset<OperatorCode> CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct OperatorT : public flatbuffers::NativeTable {
-  typedef Operator TableType;
-  uint32_t opcode_index = 0;
-  std::vector<int32_t> inputs{};
-  std::vector<int32_t> outputs{};
-  tflite::BuiltinOptionsUnion builtin_options{};
-  std::vector<uint8_t> custom_options{};
-  tflite::CustomOptionsFormat custom_options_format = tflite::CustomOptionsFormat_FLEXBUFFERS;
-  std::vector<bool> mutating_variable_inputs{};
-  std::vector<int32_t> intermediates{};
-};
-
-struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef OperatorT NativeTableType;
-  typedef OperatorBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_OPCODE_INDEX = 4,
-    VT_INPUTS = 6,
-    VT_OUTPUTS = 8,
-    VT_BUILTIN_OPTIONS_TYPE = 10,
-    VT_BUILTIN_OPTIONS = 12,
-    VT_CUSTOM_OPTIONS = 14,
-    VT_CUSTOM_OPTIONS_FORMAT = 16,
-    VT_MUTATING_VARIABLE_INPUTS = 18,
-    VT_INTERMEDIATES = 20
-  };
-  uint32_t opcode_index() const {
-    return GetField<uint32_t>(VT_OPCODE_INDEX, 0);
-  }
-  const flatbuffers::Vector<int32_t> *inputs() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INPUTS);
-  }
-  const flatbuffers::Vector<int32_t> *outputs() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUTS);
-  }
-  tflite::BuiltinOptions builtin_options_type() const {
-    return static_cast<tflite::BuiltinOptions>(GetField<uint8_t>(VT_BUILTIN_OPTIONS_TYPE, 0));
-  }
-  const void *builtin_options() const {
-    return GetPointer<const void *>(VT_BUILTIN_OPTIONS);
-  }
-  template<typename T> const T *builtin_options_as() const;
-  const tflite::Conv2DOptions *builtin_options_as_Conv2DOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_Conv2DOptions ? static_cast<const tflite::Conv2DOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::DepthwiseConv2DOptions *builtin_options_as_DepthwiseConv2DOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_DepthwiseConv2DOptions ? static_cast<const tflite::DepthwiseConv2DOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ConcatEmbeddingsOptions *builtin_options_as_ConcatEmbeddingsOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ConcatEmbeddingsOptions ? static_cast<const tflite::ConcatEmbeddingsOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::LSHProjectionOptions *builtin_options_as_LSHProjectionOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_LSHProjectionOptions ? static_cast<const tflite::LSHProjectionOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::Pool2DOptions *builtin_options_as_Pool2DOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_Pool2DOptions ? static_cast<const tflite::Pool2DOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SVDFOptions *builtin_options_as_SVDFOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SVDFOptions ? static_cast<const tflite::SVDFOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::RNNOptions *builtin_options_as_RNNOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_RNNOptions ? static_cast<const tflite::RNNOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::FullyConnectedOptions *builtin_options_as_FullyConnectedOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_FullyConnectedOptions ? static_cast<const tflite::FullyConnectedOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SoftmaxOptions *builtin_options_as_SoftmaxOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SoftmaxOptions ? static_cast<const tflite::SoftmaxOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ConcatenationOptions *builtin_options_as_ConcatenationOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ConcatenationOptions ? static_cast<const tflite::ConcatenationOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::AddOptions *builtin_options_as_AddOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_AddOptions ? static_cast<const tflite::AddOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::L2NormOptions *builtin_options_as_L2NormOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_L2NormOptions ? static_cast<const tflite::L2NormOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::LocalResponseNormalizationOptions *builtin_options_as_LocalResponseNormalizationOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_LocalResponseNormalizationOptions ? static_cast<const tflite::LocalResponseNormalizationOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::LSTMOptions *builtin_options_as_LSTMOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_LSTMOptions ? static_cast<const tflite::LSTMOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ResizeBilinearOptions *builtin_options_as_ResizeBilinearOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ResizeBilinearOptions ? static_cast<const tflite::ResizeBilinearOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::CallOptions *builtin_options_as_CallOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_CallOptions ? static_cast<const tflite::CallOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ReshapeOptions *builtin_options_as_ReshapeOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ReshapeOptions ? static_cast<const tflite::ReshapeOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SkipGramOptions *builtin_options_as_SkipGramOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SkipGramOptions ? static_cast<const tflite::SkipGramOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SpaceToDepthOptions *builtin_options_as_SpaceToDepthOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SpaceToDepthOptions ? static_cast<const tflite::SpaceToDepthOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::EmbeddingLookupSparseOptions *builtin_options_as_EmbeddingLookupSparseOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_EmbeddingLookupSparseOptions ? static_cast<const tflite::EmbeddingLookupSparseOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::MulOptions *builtin_options_as_MulOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_MulOptions ? static_cast<const tflite::MulOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::PadOptions *builtin_options_as_PadOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_PadOptions ? static_cast<const tflite::PadOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::GatherOptions *builtin_options_as_GatherOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_GatherOptions ? static_cast<const tflite::GatherOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::BatchToSpaceNDOptions *builtin_options_as_BatchToSpaceNDOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_BatchToSpaceNDOptions ? static_cast<const tflite::BatchToSpaceNDOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SpaceToBatchNDOptions *builtin_options_as_SpaceToBatchNDOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SpaceToBatchNDOptions ? static_cast<const tflite::SpaceToBatchNDOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::TransposeOptions *builtin_options_as_TransposeOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_TransposeOptions ? static_cast<const tflite::TransposeOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ReducerOptions *builtin_options_as_ReducerOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ReducerOptions ? static_cast<const tflite::ReducerOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SubOptions *builtin_options_as_SubOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SubOptions ? static_cast<const tflite::SubOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::DivOptions *builtin_options_as_DivOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_DivOptions ? static_cast<const tflite::DivOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SqueezeOptions *builtin_options_as_SqueezeOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SqueezeOptions ? static_cast<const tflite::SqueezeOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SequenceRNNOptions *builtin_options_as_SequenceRNNOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SequenceRNNOptions ? static_cast<const tflite::SequenceRNNOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::StridedSliceOptions *builtin_options_as_StridedSliceOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_StridedSliceOptions ? static_cast<const tflite::StridedSliceOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ExpOptions *builtin_options_as_ExpOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ExpOptions ? static_cast<const tflite::ExpOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::TopKV2Options *builtin_options_as_TopKV2Options() const {
-    return builtin_options_type() == tflite::BuiltinOptions_TopKV2Options ? static_cast<const tflite::TopKV2Options *>(builtin_options()) : nullptr;
-  }
-  const tflite::SplitOptions *builtin_options_as_SplitOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SplitOptions ? static_cast<const tflite::SplitOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::LogSoftmaxOptions *builtin_options_as_LogSoftmaxOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_LogSoftmaxOptions ? static_cast<const tflite::LogSoftmaxOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::CastOptions *builtin_options_as_CastOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_CastOptions ? static_cast<const tflite::CastOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::DequantizeOptions *builtin_options_as_DequantizeOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_DequantizeOptions ? static_cast<const tflite::DequantizeOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::MaximumMinimumOptions *builtin_options_as_MaximumMinimumOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_MaximumMinimumOptions ? static_cast<const tflite::MaximumMinimumOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ArgMaxOptions *builtin_options_as_ArgMaxOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ArgMaxOptions ? static_cast<const tflite::ArgMaxOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::LessOptions *builtin_options_as_LessOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_LessOptions ? static_cast<const tflite::LessOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::NegOptions *builtin_options_as_NegOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_NegOptions ? static_cast<const tflite::NegOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::PadV2Options *builtin_options_as_PadV2Options() const {
-    return builtin_options_type() == tflite::BuiltinOptions_PadV2Options ? static_cast<const tflite::PadV2Options *>(builtin_options()) : nullptr;
-  }
-  const tflite::GreaterOptions *builtin_options_as_GreaterOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_GreaterOptions ? static_cast<const tflite::GreaterOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::GreaterEqualOptions *builtin_options_as_GreaterEqualOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_GreaterEqualOptions ? static_cast<const tflite::GreaterEqualOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::LessEqualOptions *builtin_options_as_LessEqualOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_LessEqualOptions ? static_cast<const tflite::LessEqualOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SelectOptions *builtin_options_as_SelectOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SelectOptions ? static_cast<const tflite::SelectOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SliceOptions *builtin_options_as_SliceOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SliceOptions ? static_cast<const tflite::SliceOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::TransposeConvOptions *builtin_options_as_TransposeConvOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_TransposeConvOptions ? static_cast<const tflite::TransposeConvOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SparseToDenseOptions *builtin_options_as_SparseToDenseOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SparseToDenseOptions ? static_cast<const tflite::SparseToDenseOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::TileOptions *builtin_options_as_TileOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_TileOptions ? static_cast<const tflite::TileOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ExpandDimsOptions *builtin_options_as_ExpandDimsOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ExpandDimsOptions ? static_cast<const tflite::ExpandDimsOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::EqualOptions *builtin_options_as_EqualOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_EqualOptions ? static_cast<const tflite::EqualOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::NotEqualOptions *builtin_options_as_NotEqualOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_NotEqualOptions ? static_cast<const tflite::NotEqualOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ShapeOptions *builtin_options_as_ShapeOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ShapeOptions ? static_cast<const tflite::ShapeOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::PowOptions *builtin_options_as_PowOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_PowOptions ? static_cast<const tflite::PowOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ArgMinOptions *builtin_options_as_ArgMinOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ArgMinOptions ? static_cast<const tflite::ArgMinOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::FakeQuantOptions *builtin_options_as_FakeQuantOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_FakeQuantOptions ? static_cast<const tflite::FakeQuantOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::PackOptions *builtin_options_as_PackOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_PackOptions ? static_cast<const tflite::PackOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::LogicalOrOptions *builtin_options_as_LogicalOrOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_LogicalOrOptions ? static_cast<const tflite::LogicalOrOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::OneHotOptions *builtin_options_as_OneHotOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_OneHotOptions ? static_cast<const tflite::OneHotOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::LogicalAndOptions *builtin_options_as_LogicalAndOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_LogicalAndOptions ? static_cast<const tflite::LogicalAndOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::LogicalNotOptions *builtin_options_as_LogicalNotOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_LogicalNotOptions ? static_cast<const tflite::LogicalNotOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::UnpackOptions *builtin_options_as_UnpackOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_UnpackOptions ? static_cast<const tflite::UnpackOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::FloorDivOptions *builtin_options_as_FloorDivOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_FloorDivOptions ? static_cast<const tflite::FloorDivOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SquareOptions *builtin_options_as_SquareOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SquareOptions ? static_cast<const tflite::SquareOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ZerosLikeOptions *builtin_options_as_ZerosLikeOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ZerosLikeOptions ? static_cast<const tflite::ZerosLikeOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::FillOptions *builtin_options_as_FillOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_FillOptions ? static_cast<const tflite::FillOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::BidirectionalSequenceLSTMOptions *builtin_options_as_BidirectionalSequenceLSTMOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_BidirectionalSequenceLSTMOptions ? static_cast<const tflite::BidirectionalSequenceLSTMOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::BidirectionalSequenceRNNOptions *builtin_options_as_BidirectionalSequenceRNNOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_BidirectionalSequenceRNNOptions ? static_cast<const tflite::BidirectionalSequenceRNNOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::UnidirectionalSequenceLSTMOptions *builtin_options_as_UnidirectionalSequenceLSTMOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_UnidirectionalSequenceLSTMOptions ? static_cast<const tflite::UnidirectionalSequenceLSTMOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::FloorModOptions *builtin_options_as_FloorModOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_FloorModOptions ? static_cast<const tflite::FloorModOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::RangeOptions *builtin_options_as_RangeOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_RangeOptions ? static_cast<const tflite::RangeOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ResizeNearestNeighborOptions *builtin_options_as_ResizeNearestNeighborOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ResizeNearestNeighborOptions ? static_cast<const tflite::ResizeNearestNeighborOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::LeakyReluOptions *builtin_options_as_LeakyReluOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_LeakyReluOptions ? static_cast<const tflite::LeakyReluOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SquaredDifferenceOptions *builtin_options_as_SquaredDifferenceOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SquaredDifferenceOptions ? static_cast<const tflite::SquaredDifferenceOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::MirrorPadOptions *builtin_options_as_MirrorPadOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_MirrorPadOptions ? static_cast<const tflite::MirrorPadOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::AbsOptions *builtin_options_as_AbsOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_AbsOptions ? static_cast<const tflite::AbsOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SplitVOptions *builtin_options_as_SplitVOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SplitVOptions ? static_cast<const tflite::SplitVOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::UniqueOptions *builtin_options_as_UniqueOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_UniqueOptions ? static_cast<const tflite::UniqueOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ReverseV2Options *builtin_options_as_ReverseV2Options() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ReverseV2Options ? static_cast<const tflite::ReverseV2Options *>(builtin_options()) : nullptr;
-  }
-  const tflite::AddNOptions *builtin_options_as_AddNOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_AddNOptions ? static_cast<const tflite::AddNOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::GatherNdOptions *builtin_options_as_GatherNdOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_GatherNdOptions ? static_cast<const tflite::GatherNdOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::CosOptions *builtin_options_as_CosOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_CosOptions ? static_cast<const tflite::CosOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::WhereOptions *builtin_options_as_WhereOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_WhereOptions ? static_cast<const tflite::WhereOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::RankOptions *builtin_options_as_RankOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_RankOptions ? static_cast<const tflite::RankOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ReverseSequenceOptions *builtin_options_as_ReverseSequenceOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ReverseSequenceOptions ? static_cast<const tflite::ReverseSequenceOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::MatrixDiagOptions *builtin_options_as_MatrixDiagOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_MatrixDiagOptions ? static_cast<const tflite::MatrixDiagOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::QuantizeOptions *builtin_options_as_QuantizeOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_QuantizeOptions ? static_cast<const tflite::QuantizeOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::MatrixSetDiagOptions *builtin_options_as_MatrixSetDiagOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_MatrixSetDiagOptions ? static_cast<const tflite::MatrixSetDiagOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::HardSwishOptions *builtin_options_as_HardSwishOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_HardSwishOptions ? static_cast<const tflite::HardSwishOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::IfOptions *builtin_options_as_IfOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_IfOptions ? static_cast<const tflite::IfOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::WhileOptions *builtin_options_as_WhileOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_WhileOptions ? static_cast<const tflite::WhileOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::DepthToSpaceOptions *builtin_options_as_DepthToSpaceOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_DepthToSpaceOptions ? static_cast<const tflite::DepthToSpaceOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::NonMaxSuppressionV4Options *builtin_options_as_NonMaxSuppressionV4Options() const {
-    return builtin_options_type() == tflite::BuiltinOptions_NonMaxSuppressionV4Options ? static_cast<const tflite::NonMaxSuppressionV4Options *>(builtin_options()) : nullptr;
-  }
-  const tflite::NonMaxSuppressionV5Options *builtin_options_as_NonMaxSuppressionV5Options() const {
-    return builtin_options_type() == tflite::BuiltinOptions_NonMaxSuppressionV5Options ? static_cast<const tflite::NonMaxSuppressionV5Options *>(builtin_options()) : nullptr;
-  }
-  const tflite::ScatterNdOptions *builtin_options_as_ScatterNdOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ScatterNdOptions ? static_cast<const tflite::ScatterNdOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SelectV2Options *builtin_options_as_SelectV2Options() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SelectV2Options ? static_cast<const tflite::SelectV2Options *>(builtin_options()) : nullptr;
-  }
-  const tflite::DensifyOptions *builtin_options_as_DensifyOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_DensifyOptions ? static_cast<const tflite::DensifyOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::SegmentSumOptions *builtin_options_as_SegmentSumOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SegmentSumOptions ? static_cast<const tflite::SegmentSumOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::BatchMatMulOptions *builtin_options_as_BatchMatMulOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_BatchMatMulOptions ? static_cast<const tflite::BatchMatMulOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::CumsumOptions *builtin_options_as_CumsumOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_CumsumOptions ? static_cast<const tflite::CumsumOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::CallOnceOptions *builtin_options_as_CallOnceOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_CallOnceOptions ? static_cast<const tflite::CallOnceOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::BroadcastToOptions *builtin_options_as_BroadcastToOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_BroadcastToOptions ? static_cast<const tflite::BroadcastToOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::Rfft2dOptions *builtin_options_as_Rfft2dOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_Rfft2dOptions ? static_cast<const tflite::Rfft2dOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::Conv3DOptions *builtin_options_as_Conv3DOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_Conv3DOptions ? static_cast<const tflite::Conv3DOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::HashtableOptions *builtin_options_as_HashtableOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_HashtableOptions ? static_cast<const tflite::HashtableOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::HashtableFindOptions *builtin_options_as_HashtableFindOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_HashtableFindOptions ? static_cast<const tflite::HashtableFindOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::HashtableImportOptions *builtin_options_as_HashtableImportOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_HashtableImportOptions ? static_cast<const tflite::HashtableImportOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::HashtableSizeOptions *builtin_options_as_HashtableSizeOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_HashtableSizeOptions ? static_cast<const tflite::HashtableSizeOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::VarHandleOptions *builtin_options_as_VarHandleOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_VarHandleOptions ? static_cast<const tflite::VarHandleOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ReadVariableOptions *builtin_options_as_ReadVariableOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ReadVariableOptions ? static_cast<const tflite::ReadVariableOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::AssignVariableOptions *builtin_options_as_AssignVariableOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_AssignVariableOptions ? static_cast<const tflite::AssignVariableOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::RandomOptions *builtin_options_as_RandomOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_RandomOptions ? static_cast<const tflite::RandomOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::BucketizeOptions *builtin_options_as_BucketizeOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_BucketizeOptions ? static_cast<const tflite::BucketizeOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::GeluOptions *builtin_options_as_GeluOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_GeluOptions ? static_cast<const tflite::GeluOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::DynamicUpdateSliceOptions *builtin_options_as_DynamicUpdateSliceOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_DynamicUpdateSliceOptions ? static_cast<const tflite::DynamicUpdateSliceOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::UnsortedSegmentProdOptions *builtin_options_as_UnsortedSegmentProdOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_UnsortedSegmentProdOptions ? static_cast<const tflite::UnsortedSegmentProdOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::UnsortedSegmentMaxOptions *builtin_options_as_UnsortedSegmentMaxOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_UnsortedSegmentMaxOptions ? static_cast<const tflite::UnsortedSegmentMaxOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::UnsortedSegmentMinOptions *builtin_options_as_UnsortedSegmentMinOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_UnsortedSegmentMinOptions ? static_cast<const tflite::UnsortedSegmentMinOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::UnsortedSegmentSumOptions *builtin_options_as_UnsortedSegmentSumOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_UnsortedSegmentSumOptions ? static_cast<const tflite::UnsortedSegmentSumOptions *>(builtin_options()) : nullptr;
-  }
-  const tflite::ATan2Options *builtin_options_as_ATan2Options() const {
-    return builtin_options_type() == tflite::BuiltinOptions_ATan2Options ? static_cast<const tflite::ATan2Options *>(builtin_options()) : nullptr;
-  }
-  const tflite::SignOptions *builtin_options_as_SignOptions() const {
-    return builtin_options_type() == tflite::BuiltinOptions_SignOptions ? static_cast<const tflite::SignOptions *>(builtin_options()) : nullptr;
-  }
-  const flatbuffers::Vector<uint8_t> *custom_options() const {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_CUSTOM_OPTIONS);
-  }
-  tflite::CustomOptionsFormat custom_options_format() const {
-    return static_cast<tflite::CustomOptionsFormat>(GetField<int8_t>(VT_CUSTOM_OPTIONS_FORMAT, 0));
-  }
-  const flatbuffers::Vector<uint8_t> *mutating_variable_inputs() const {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_MUTATING_VARIABLE_INPUTS);
-  }
-  const flatbuffers::Vector<int32_t> *intermediates() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INTERMEDIATES);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<uint32_t>(verifier, VT_OPCODE_INDEX, 4) &&
-           VerifyOffset(verifier, VT_INPUTS) &&
-           verifier.VerifyVector(inputs()) &&
-           VerifyOffset(verifier, VT_OUTPUTS) &&
-           verifier.VerifyVector(outputs()) &&
-           VerifyField<uint8_t>(verifier, VT_BUILTIN_OPTIONS_TYPE, 1) &&
-           VerifyOffset(verifier, VT_BUILTIN_OPTIONS) &&
-           VerifyBuiltinOptions(verifier, builtin_options(), builtin_options_type()) &&
-           VerifyOffset(verifier, VT_CUSTOM_OPTIONS) &&
-           verifier.VerifyVector(custom_options()) &&
-           VerifyField<int8_t>(verifier, VT_CUSTOM_OPTIONS_FORMAT, 1) &&
-           VerifyOffset(verifier, VT_MUTATING_VARIABLE_INPUTS) &&
-           verifier.VerifyVector(mutating_variable_inputs()) &&
-           VerifyOffset(verifier, VT_INTERMEDIATES) &&
-           verifier.VerifyVector(intermediates()) &&
-           verifier.EndTable();
-  }
-  OperatorT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<Operator> Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-template<> inline const tflite::Conv2DOptions *Operator::builtin_options_as<tflite::Conv2DOptions>() const {
-  return builtin_options_as_Conv2DOptions();
-}
-
-template<> inline const tflite::DepthwiseConv2DOptions *Operator::builtin_options_as<tflite::DepthwiseConv2DOptions>() const {
-  return builtin_options_as_DepthwiseConv2DOptions();
-}
-
-template<> inline const tflite::ConcatEmbeddingsOptions *Operator::builtin_options_as<tflite::ConcatEmbeddingsOptions>() const {
-  return builtin_options_as_ConcatEmbeddingsOptions();
-}
-
-template<> inline const tflite::LSHProjectionOptions *Operator::builtin_options_as<tflite::LSHProjectionOptions>() const {
-  return builtin_options_as_LSHProjectionOptions();
-}
-
-template<> inline const tflite::Pool2DOptions *Operator::builtin_options_as<tflite::Pool2DOptions>() const {
-  return builtin_options_as_Pool2DOptions();
-}
-
-template<> inline const tflite::SVDFOptions *Operator::builtin_options_as<tflite::SVDFOptions>() const {
-  return builtin_options_as_SVDFOptions();
-}
-
-template<> inline const tflite::RNNOptions *Operator::builtin_options_as<tflite::RNNOptions>() const {
-  return builtin_options_as_RNNOptions();
-}
-
-template<> inline const tflite::FullyConnectedOptions *Operator::builtin_options_as<tflite::FullyConnectedOptions>() const {
-  return builtin_options_as_FullyConnectedOptions();
-}
-
-template<> inline const tflite::SoftmaxOptions *Operator::builtin_options_as<tflite::SoftmaxOptions>() const {
-  return builtin_options_as_SoftmaxOptions();
-}
-
-template<> inline const tflite::ConcatenationOptions *Operator::builtin_options_as<tflite::ConcatenationOptions>() const {
-  return builtin_options_as_ConcatenationOptions();
-}
-
-template<> inline const tflite::AddOptions *Operator::builtin_options_as<tflite::AddOptions>() const {
-  return builtin_options_as_AddOptions();
-}
-
-template<> inline const tflite::L2NormOptions *Operator::builtin_options_as<tflite::L2NormOptions>() const {
-  return builtin_options_as_L2NormOptions();
-}
-
-template<> inline const tflite::LocalResponseNormalizationOptions *Operator::builtin_options_as<tflite::LocalResponseNormalizationOptions>() const {
-  return builtin_options_as_LocalResponseNormalizationOptions();
-}
-
-template<> inline const tflite::LSTMOptions *Operator::builtin_options_as<tflite::LSTMOptions>() const {
-  return builtin_options_as_LSTMOptions();
-}
-
-template<> inline const tflite::ResizeBilinearOptions *Operator::builtin_options_as<tflite::ResizeBilinearOptions>() const {
-  return builtin_options_as_ResizeBilinearOptions();
-}
-
-template<> inline const tflite::CallOptions *Operator::builtin_options_as<tflite::CallOptions>() const {
-  return builtin_options_as_CallOptions();
-}
-
-template<> inline const tflite::ReshapeOptions *Operator::builtin_options_as<tflite::ReshapeOptions>() const {
-  return builtin_options_as_ReshapeOptions();
-}
-
-template<> inline const tflite::SkipGramOptions *Operator::builtin_options_as<tflite::SkipGramOptions>() const {
-  return builtin_options_as_SkipGramOptions();
-}
-
-template<> inline const tflite::SpaceToDepthOptions *Operator::builtin_options_as<tflite::SpaceToDepthOptions>() const {
-  return builtin_options_as_SpaceToDepthOptions();
-}
-
-template<> inline const tflite::EmbeddingLookupSparseOptions *Operator::builtin_options_as<tflite::EmbeddingLookupSparseOptions>() const {
-  return builtin_options_as_EmbeddingLookupSparseOptions();
-}
-
-template<> inline const tflite::MulOptions *Operator::builtin_options_as<tflite::MulOptions>() const {
-  return builtin_options_as_MulOptions();
-}
-
-template<> inline const tflite::PadOptions *Operator::builtin_options_as<tflite::PadOptions>() const {
-  return builtin_options_as_PadOptions();
-}
-
-template<> inline const tflite::GatherOptions *Operator::builtin_options_as<tflite::GatherOptions>() const {
-  return builtin_options_as_GatherOptions();
-}
-
-template<> inline const tflite::BatchToSpaceNDOptions *Operator::builtin_options_as<tflite::BatchToSpaceNDOptions>() const {
-  return builtin_options_as_BatchToSpaceNDOptions();
-}
-
-template<> inline const tflite::SpaceToBatchNDOptions *Operator::builtin_options_as<tflite::SpaceToBatchNDOptions>() const {
-  return builtin_options_as_SpaceToBatchNDOptions();
-}
-
-template<> inline const tflite::TransposeOptions *Operator::builtin_options_as<tflite::TransposeOptions>() const {
-  return builtin_options_as_TransposeOptions();
-}
-
-template<> inline const tflite::ReducerOptions *Operator::builtin_options_as<tflite::ReducerOptions>() const {
-  return builtin_options_as_ReducerOptions();
-}
-
-template<> inline const tflite::SubOptions *Operator::builtin_options_as<tflite::SubOptions>() const {
-  return builtin_options_as_SubOptions();
-}
-
-template<> inline const tflite::DivOptions *Operator::builtin_options_as<tflite::DivOptions>() const {
-  return builtin_options_as_DivOptions();
-}
-
-template<> inline const tflite::SqueezeOptions *Operator::builtin_options_as<tflite::SqueezeOptions>() const {
-  return builtin_options_as_SqueezeOptions();
-}
-
-template<> inline const tflite::SequenceRNNOptions *Operator::builtin_options_as<tflite::SequenceRNNOptions>() const {
-  return builtin_options_as_SequenceRNNOptions();
-}
-
-template<> inline const tflite::StridedSliceOptions *Operator::builtin_options_as<tflite::StridedSliceOptions>() const {
-  return builtin_options_as_StridedSliceOptions();
-}
-
-template<> inline const tflite::ExpOptions *Operator::builtin_options_as<tflite::ExpOptions>() const {
-  return builtin_options_as_ExpOptions();
-}
-
-template<> inline const tflite::TopKV2Options *Operator::builtin_options_as<tflite::TopKV2Options>() const {
-  return builtin_options_as_TopKV2Options();
-}
-
-template<> inline const tflite::SplitOptions *Operator::builtin_options_as<tflite::SplitOptions>() const {
-  return builtin_options_as_SplitOptions();
-}
-
-template<> inline const tflite::LogSoftmaxOptions *Operator::builtin_options_as<tflite::LogSoftmaxOptions>() const {
-  return builtin_options_as_LogSoftmaxOptions();
-}
-
-template<> inline const tflite::CastOptions *Operator::builtin_options_as<tflite::CastOptions>() const {
-  return builtin_options_as_CastOptions();
-}
-
-template<> inline const tflite::DequantizeOptions *Operator::builtin_options_as<tflite::DequantizeOptions>() const {
-  return builtin_options_as_DequantizeOptions();
-}
-
-template<> inline const tflite::MaximumMinimumOptions *Operator::builtin_options_as<tflite::MaximumMinimumOptions>() const {
-  return builtin_options_as_MaximumMinimumOptions();
-}
-
-template<> inline const tflite::ArgMaxOptions *Operator::builtin_options_as<tflite::ArgMaxOptions>() const {
-  return builtin_options_as_ArgMaxOptions();
-}
-
-template<> inline const tflite::LessOptions *Operator::builtin_options_as<tflite::LessOptions>() const {
-  return builtin_options_as_LessOptions();
-}
-
-template<> inline const tflite::NegOptions *Operator::builtin_options_as<tflite::NegOptions>() const {
-  return builtin_options_as_NegOptions();
-}
-
-template<> inline const tflite::PadV2Options *Operator::builtin_options_as<tflite::PadV2Options>() const {
-  return builtin_options_as_PadV2Options();
-}
-
-template<> inline const tflite::GreaterOptions *Operator::builtin_options_as<tflite::GreaterOptions>() const {
-  return builtin_options_as_GreaterOptions();
-}
-
-template<> inline const tflite::GreaterEqualOptions *Operator::builtin_options_as<tflite::GreaterEqualOptions>() const {
-  return builtin_options_as_GreaterEqualOptions();
-}
-
-template<> inline const tflite::LessEqualOptions *Operator::builtin_options_as<tflite::LessEqualOptions>() const {
-  return builtin_options_as_LessEqualOptions();
-}
-
-template<> inline const tflite::SelectOptions *Operator::builtin_options_as<tflite::SelectOptions>() const {
-  return builtin_options_as_SelectOptions();
-}
-
-template<> inline const tflite::SliceOptions *Operator::builtin_options_as<tflite::SliceOptions>() const {
-  return builtin_options_as_SliceOptions();
-}
-
-template<> inline const tflite::TransposeConvOptions *Operator::builtin_options_as<tflite::TransposeConvOptions>() const {
-  return builtin_options_as_TransposeConvOptions();
-}
-
-template<> inline const tflite::SparseToDenseOptions *Operator::builtin_options_as<tflite::SparseToDenseOptions>() const {
-  return builtin_options_as_SparseToDenseOptions();
-}
-
-template<> inline const tflite::TileOptions *Operator::builtin_options_as<tflite::TileOptions>() const {
-  return builtin_options_as_TileOptions();
-}
-
-template<> inline const tflite::ExpandDimsOptions *Operator::builtin_options_as<tflite::ExpandDimsOptions>() const {
-  return builtin_options_as_ExpandDimsOptions();
-}
-
-template<> inline const tflite::EqualOptions *Operator::builtin_options_as<tflite::EqualOptions>() const {
-  return builtin_options_as_EqualOptions();
-}
-
-template<> inline const tflite::NotEqualOptions *Operator::builtin_options_as<tflite::NotEqualOptions>() const {
-  return builtin_options_as_NotEqualOptions();
-}
-
-template<> inline const tflite::ShapeOptions *Operator::builtin_options_as<tflite::ShapeOptions>() const {
-  return builtin_options_as_ShapeOptions();
-}
-
-template<> inline const tflite::PowOptions *Operator::builtin_options_as<tflite::PowOptions>() const {
-  return builtin_options_as_PowOptions();
-}
-
-template<> inline const tflite::ArgMinOptions *Operator::builtin_options_as<tflite::ArgMinOptions>() const {
-  return builtin_options_as_ArgMinOptions();
-}
-
-template<> inline const tflite::FakeQuantOptions *Operator::builtin_options_as<tflite::FakeQuantOptions>() const {
-  return builtin_options_as_FakeQuantOptions();
-}
-
-template<> inline const tflite::PackOptions *Operator::builtin_options_as<tflite::PackOptions>() const {
-  return builtin_options_as_PackOptions();
-}
-
-template<> inline const tflite::LogicalOrOptions *Operator::builtin_options_as<tflite::LogicalOrOptions>() const {
-  return builtin_options_as_LogicalOrOptions();
-}
-
-template<> inline const tflite::OneHotOptions *Operator::builtin_options_as<tflite::OneHotOptions>() const {
-  return builtin_options_as_OneHotOptions();
-}
-
-template<> inline const tflite::LogicalAndOptions *Operator::builtin_options_as<tflite::LogicalAndOptions>() const {
-  return builtin_options_as_LogicalAndOptions();
-}
-
-template<> inline const tflite::LogicalNotOptions *Operator::builtin_options_as<tflite::LogicalNotOptions>() const {
-  return builtin_options_as_LogicalNotOptions();
-}
-
-template<> inline const tflite::UnpackOptions *Operator::builtin_options_as<tflite::UnpackOptions>() const {
-  return builtin_options_as_UnpackOptions();
-}
-
-template<> inline const tflite::FloorDivOptions *Operator::builtin_options_as<tflite::FloorDivOptions>() const {
-  return builtin_options_as_FloorDivOptions();
-}
-
-template<> inline const tflite::SquareOptions *Operator::builtin_options_as<tflite::SquareOptions>() const {
-  return builtin_options_as_SquareOptions();
-}
-
-template<> inline const tflite::ZerosLikeOptions *Operator::builtin_options_as<tflite::ZerosLikeOptions>() const {
-  return builtin_options_as_ZerosLikeOptions();
-}
-
-template<> inline const tflite::FillOptions *Operator::builtin_options_as<tflite::FillOptions>() const {
-  return builtin_options_as_FillOptions();
-}
-
-template<> inline const tflite::BidirectionalSequenceLSTMOptions *Operator::builtin_options_as<tflite::BidirectionalSequenceLSTMOptions>() const {
-  return builtin_options_as_BidirectionalSequenceLSTMOptions();
-}
-
-template<> inline const tflite::BidirectionalSequenceRNNOptions *Operator::builtin_options_as<tflite::BidirectionalSequenceRNNOptions>() const {
-  return builtin_options_as_BidirectionalSequenceRNNOptions();
-}
-
-template<> inline const tflite::UnidirectionalSequenceLSTMOptions *Operator::builtin_options_as<tflite::UnidirectionalSequenceLSTMOptions>() const {
-  return builtin_options_as_UnidirectionalSequenceLSTMOptions();
-}
-
-template<> inline const tflite::FloorModOptions *Operator::builtin_options_as<tflite::FloorModOptions>() const {
-  return builtin_options_as_FloorModOptions();
-}
-
-template<> inline const tflite::RangeOptions *Operator::builtin_options_as<tflite::RangeOptions>() const {
-  return builtin_options_as_RangeOptions();
-}
-
-template<> inline const tflite::ResizeNearestNeighborOptions *Operator::builtin_options_as<tflite::ResizeNearestNeighborOptions>() const {
-  return builtin_options_as_ResizeNearestNeighborOptions();
-}
-
-template<> inline const tflite::LeakyReluOptions *Operator::builtin_options_as<tflite::LeakyReluOptions>() const {
-  return builtin_options_as_LeakyReluOptions();
-}
-
-template<> inline const tflite::SquaredDifferenceOptions *Operator::builtin_options_as<tflite::SquaredDifferenceOptions>() const {
-  return builtin_options_as_SquaredDifferenceOptions();
-}
-
-template<> inline const tflite::MirrorPadOptions *Operator::builtin_options_as<tflite::MirrorPadOptions>() const {
-  return builtin_options_as_MirrorPadOptions();
-}
-
-template<> inline const tflite::AbsOptions *Operator::builtin_options_as<tflite::AbsOptions>() const {
-  return builtin_options_as_AbsOptions();
-}
-
-template<> inline const tflite::SplitVOptions *Operator::builtin_options_as<tflite::SplitVOptions>() const {
-  return builtin_options_as_SplitVOptions();
-}
-
-template<> inline const tflite::UniqueOptions *Operator::builtin_options_as<tflite::UniqueOptions>() const {
-  return builtin_options_as_UniqueOptions();
-}
-
-template<> inline const tflite::ReverseV2Options *Operator::builtin_options_as<tflite::ReverseV2Options>() const {
-  return builtin_options_as_ReverseV2Options();
-}
-
-template<> inline const tflite::AddNOptions *Operator::builtin_options_as<tflite::AddNOptions>() const {
-  return builtin_options_as_AddNOptions();
-}
-
-template<> inline const tflite::GatherNdOptions *Operator::builtin_options_as<tflite::GatherNdOptions>() const {
-  return builtin_options_as_GatherNdOptions();
-}
-
-template<> inline const tflite::CosOptions *Operator::builtin_options_as<tflite::CosOptions>() const {
-  return builtin_options_as_CosOptions();
-}
-
-template<> inline const tflite::WhereOptions *Operator::builtin_options_as<tflite::WhereOptions>() const {
-  return builtin_options_as_WhereOptions();
-}
-
-template<> inline const tflite::RankOptions *Operator::builtin_options_as<tflite::RankOptions>() const {
-  return builtin_options_as_RankOptions();
-}
-
-template<> inline const tflite::ReverseSequenceOptions *Operator::builtin_options_as<tflite::ReverseSequenceOptions>() const {
-  return builtin_options_as_ReverseSequenceOptions();
-}
-
-template<> inline const tflite::MatrixDiagOptions *Operator::builtin_options_as<tflite::MatrixDiagOptions>() const {
-  return builtin_options_as_MatrixDiagOptions();
-}
-
-template<> inline const tflite::QuantizeOptions *Operator::builtin_options_as<tflite::QuantizeOptions>() const {
-  return builtin_options_as_QuantizeOptions();
-}
-
-template<> inline const tflite::MatrixSetDiagOptions *Operator::builtin_options_as<tflite::MatrixSetDiagOptions>() const {
-  return builtin_options_as_MatrixSetDiagOptions();
-}
-
-template<> inline const tflite::HardSwishOptions *Operator::builtin_options_as<tflite::HardSwishOptions>() const {
-  return builtin_options_as_HardSwishOptions();
-}
-
-template<> inline const tflite::IfOptions *Operator::builtin_options_as<tflite::IfOptions>() const {
-  return builtin_options_as_IfOptions();
-}
-
-template<> inline const tflite::WhileOptions *Operator::builtin_options_as<tflite::WhileOptions>() const {
-  return builtin_options_as_WhileOptions();
-}
-
-template<> inline const tflite::DepthToSpaceOptions *Operator::builtin_options_as<tflite::DepthToSpaceOptions>() const {
-  return builtin_options_as_DepthToSpaceOptions();
-}
-
-template<> inline const tflite::NonMaxSuppressionV4Options *Operator::builtin_options_as<tflite::NonMaxSuppressionV4Options>() const {
-  return builtin_options_as_NonMaxSuppressionV4Options();
-}
-
-template<> inline const tflite::NonMaxSuppressionV5Options *Operator::builtin_options_as<tflite::NonMaxSuppressionV5Options>() const {
-  return builtin_options_as_NonMaxSuppressionV5Options();
-}
-
-template<> inline const tflite::ScatterNdOptions *Operator::builtin_options_as<tflite::ScatterNdOptions>() const {
-  return builtin_options_as_ScatterNdOptions();
-}
-
-template<> inline const tflite::SelectV2Options *Operator::builtin_options_as<tflite::SelectV2Options>() const {
-  return builtin_options_as_SelectV2Options();
-}
-
-template<> inline const tflite::DensifyOptions *Operator::builtin_options_as<tflite::DensifyOptions>() const {
-  return builtin_options_as_DensifyOptions();
-}
-
-template<> inline const tflite::SegmentSumOptions *Operator::builtin_options_as<tflite::SegmentSumOptions>() const {
-  return builtin_options_as_SegmentSumOptions();
-}
-
-template<> inline const tflite::BatchMatMulOptions *Operator::builtin_options_as<tflite::BatchMatMulOptions>() const {
-  return builtin_options_as_BatchMatMulOptions();
-}
-
-template<> inline const tflite::CumsumOptions *Operator::builtin_options_as<tflite::CumsumOptions>() const {
-  return builtin_options_as_CumsumOptions();
-}
-
-template<> inline const tflite::CallOnceOptions *Operator::builtin_options_as<tflite::CallOnceOptions>() const {
-  return builtin_options_as_CallOnceOptions();
-}
-
-template<> inline const tflite::BroadcastToOptions *Operator::builtin_options_as<tflite::BroadcastToOptions>() const {
-  return builtin_options_as_BroadcastToOptions();
-}
-
-template<> inline const tflite::Rfft2dOptions *Operator::builtin_options_as<tflite::Rfft2dOptions>() const {
-  return builtin_options_as_Rfft2dOptions();
-}
-
-template<> inline const tflite::Conv3DOptions *Operator::builtin_options_as<tflite::Conv3DOptions>() const {
-  return builtin_options_as_Conv3DOptions();
-}
-
-template<> inline const tflite::HashtableOptions *Operator::builtin_options_as<tflite::HashtableOptions>() const {
-  return builtin_options_as_HashtableOptions();
-}
-
-template<> inline const tflite::HashtableFindOptions *Operator::builtin_options_as<tflite::HashtableFindOptions>() const {
-  return builtin_options_as_HashtableFindOptions();
-}
-
-template<> inline const tflite::HashtableImportOptions *Operator::builtin_options_as<tflite::HashtableImportOptions>() const {
-  return builtin_options_as_HashtableImportOptions();
-}
-
-template<> inline const tflite::HashtableSizeOptions *Operator::builtin_options_as<tflite::HashtableSizeOptions>() const {
-  return builtin_options_as_HashtableSizeOptions();
-}
-
-template<> inline const tflite::VarHandleOptions *Operator::builtin_options_as<tflite::VarHandleOptions>() const {
-  return builtin_options_as_VarHandleOptions();
-}
-
-template<> inline const tflite::ReadVariableOptions *Operator::builtin_options_as<tflite::ReadVariableOptions>() const {
-  return builtin_options_as_ReadVariableOptions();
-}
-
-template<> inline const tflite::AssignVariableOptions *Operator::builtin_options_as<tflite::AssignVariableOptions>() const {
-  return builtin_options_as_AssignVariableOptions();
-}
-
-template<> inline const tflite::RandomOptions *Operator::builtin_options_as<tflite::RandomOptions>() const {
-  return builtin_options_as_RandomOptions();
-}
-
-template<> inline const tflite::BucketizeOptions *Operator::builtin_options_as<tflite::BucketizeOptions>() const {
-  return builtin_options_as_BucketizeOptions();
-}
-
-template<> inline const tflite::GeluOptions *Operator::builtin_options_as<tflite::GeluOptions>() const {
-  return builtin_options_as_GeluOptions();
-}
-
-template<> inline const tflite::DynamicUpdateSliceOptions *Operator::builtin_options_as<tflite::DynamicUpdateSliceOptions>() const {
-  return builtin_options_as_DynamicUpdateSliceOptions();
-}
-
-template<> inline const tflite::UnsortedSegmentProdOptions *Operator::builtin_options_as<tflite::UnsortedSegmentProdOptions>() const {
-  return builtin_options_as_UnsortedSegmentProdOptions();
-}
-
-template<> inline const tflite::UnsortedSegmentMaxOptions *Operator::builtin_options_as<tflite::UnsortedSegmentMaxOptions>() const {
-  return builtin_options_as_UnsortedSegmentMaxOptions();
-}
-
-template<> inline const tflite::UnsortedSegmentMinOptions *Operator::builtin_options_as<tflite::UnsortedSegmentMinOptions>() const {
-  return builtin_options_as_UnsortedSegmentMinOptions();
-}
-
-template<> inline const tflite::UnsortedSegmentSumOptions *Operator::builtin_options_as<tflite::UnsortedSegmentSumOptions>() const {
-  return builtin_options_as_UnsortedSegmentSumOptions();
-}
-
-template<> inline const tflite::ATan2Options *Operator::builtin_options_as<tflite::ATan2Options>() const {
-  return builtin_options_as_ATan2Options();
-}
-
-template<> inline const tflite::SignOptions *Operator::builtin_options_as<tflite::SignOptions>() const {
-  return builtin_options_as_SignOptions();
-}
-
-struct OperatorBuilder {
-  typedef Operator Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_opcode_index(uint32_t opcode_index) {
-    fbb_.AddElement<uint32_t>(Operator::VT_OPCODE_INDEX, opcode_index, 0);
-  }
-  void add_inputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs) {
-    fbb_.AddOffset(Operator::VT_INPUTS, inputs);
-  }
-  void add_outputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs) {
-    fbb_.AddOffset(Operator::VT_OUTPUTS, outputs);
-  }
-  void add_builtin_options_type(tflite::BuiltinOptions builtin_options_type) {
-    fbb_.AddElement<uint8_t>(Operator::VT_BUILTIN_OPTIONS_TYPE, static_cast<uint8_t>(builtin_options_type), 0);
-  }
-  void add_builtin_options(flatbuffers::Offset<void> builtin_options) {
-    fbb_.AddOffset(Operator::VT_BUILTIN_OPTIONS, builtin_options);
-  }
-  void add_custom_options(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom_options) {
-    fbb_.AddOffset(Operator::VT_CUSTOM_OPTIONS, custom_options);
-  }
-  void add_custom_options_format(tflite::CustomOptionsFormat custom_options_format) {
-    fbb_.AddElement<int8_t>(Operator::VT_CUSTOM_OPTIONS_FORMAT, static_cast<int8_t>(custom_options_format), 0);
-  }
-  void add_mutating_variable_inputs(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> mutating_variable_inputs) {
-    fbb_.AddOffset(Operator::VT_MUTATING_VARIABLE_INPUTS, mutating_variable_inputs);
-  }
-  void add_intermediates(flatbuffers::Offset<flatbuffers::Vector<int32_t>> intermediates) {
-    fbb_.AddOffset(Operator::VT_INTERMEDIATES, intermediates);
-  }
-  explicit OperatorBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<Operator> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Operator>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Operator> CreateOperator(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    uint32_t opcode_index = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs = 0,
-    tflite::BuiltinOptions builtin_options_type = tflite::BuiltinOptions_NONE,
-    flatbuffers::Offset<void> builtin_options = 0,
-    flatbuffers::Offset<flatbuffers::Vector<uint8_t>> custom_options = 0,
-    tflite::CustomOptionsFormat custom_options_format = tflite::CustomOptionsFormat_FLEXBUFFERS,
-    flatbuffers::Offset<flatbuffers::Vector<uint8_t>> mutating_variable_inputs = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> intermediates = 0) {
-  OperatorBuilder builder_(_fbb);
-  builder_.add_intermediates(intermediates);
-  builder_.add_mutating_variable_inputs(mutating_variable_inputs);
-  builder_.add_custom_options(custom_options);
-  builder_.add_builtin_options(builtin_options);
-  builder_.add_outputs(outputs);
-  builder_.add_inputs(inputs);
-  builder_.add_opcode_index(opcode_index);
-  builder_.add_custom_options_format(custom_options_format);
-  builder_.add_builtin_options_type(builtin_options_type);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Operator> CreateOperatorDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    uint32_t opcode_index = 0,
-    const std::vector<int32_t> *inputs = nullptr,
-    const std::vector<int32_t> *outputs = nullptr,
-    tflite::BuiltinOptions builtin_options_type = tflite::BuiltinOptions_NONE,
-    flatbuffers::Offset<void> builtin_options = 0,
-    const std::vector<uint8_t> *custom_options = nullptr,
-    tflite::CustomOptionsFormat custom_options_format = tflite::CustomOptionsFormat_FLEXBUFFERS,
-    const std::vector<uint8_t> *mutating_variable_inputs = nullptr,
-    const std::vector<int32_t> *intermediates = nullptr) {
-  auto inputs__ = inputs ? _fbb.CreateVector<int32_t>(*inputs) : 0;
-  auto outputs__ = outputs ? _fbb.CreateVector<int32_t>(*outputs) : 0;
-  auto custom_options__ = custom_options ? _fbb.CreateVector<uint8_t>(*custom_options) : 0;
-  auto mutating_variable_inputs__ = mutating_variable_inputs ? _fbb.CreateVector<uint8_t>(*mutating_variable_inputs) : 0;
-  auto intermediates__ = intermediates ? _fbb.CreateVector<int32_t>(*intermediates) : 0;
-  return tflite::CreateOperator(
-      _fbb,
-      opcode_index,
-      inputs__,
-      outputs__,
-      builtin_options_type,
-      builtin_options,
-      custom_options__,
-      custom_options_format,
-      mutating_variable_inputs__,
-      intermediates__);
-}
-
-flatbuffers::Offset<Operator> CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SubGraphT : public flatbuffers::NativeTable {
-  typedef SubGraph TableType;
-  std::vector<std::unique_ptr<tflite::TensorT>> tensors{};
-  std::vector<int32_t> inputs{};
-  std::vector<int32_t> outputs{};
-  std::vector<std::unique_ptr<tflite::OperatorT>> operators{};
-  std::string name{};
-  SubGraphT() = default;
-  SubGraphT(const SubGraphT &o);
-  SubGraphT(SubGraphT&&) FLATBUFFERS_NOEXCEPT = default;
-  SubGraphT &operator=(SubGraphT o) FLATBUFFERS_NOEXCEPT;
-};
-
-struct SubGraph FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SubGraphT NativeTableType;
-  typedef SubGraphBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_TENSORS = 4,
-    VT_INPUTS = 6,
-    VT_OUTPUTS = 8,
-    VT_OPERATORS = 10,
-    VT_NAME = 12
-  };
-  const flatbuffers::Vector<flatbuffers::Offset<tflite::Tensor>> *tensors() const {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::Tensor>> *>(VT_TENSORS);
-  }
-  const flatbuffers::Vector<int32_t> *inputs() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_INPUTS);
-  }
-  const flatbuffers::Vector<int32_t> *outputs() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_OUTPUTS);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<tflite::Operator>> *operators() const {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::Operator>> *>(VT_OPERATORS);
-  }
-  const flatbuffers::String *name() const {
-    return GetPointer<const flatbuffers::String *>(VT_NAME);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_TENSORS) &&
-           verifier.VerifyVector(tensors()) &&
-           verifier.VerifyVectorOfTables(tensors()) &&
-           VerifyOffset(verifier, VT_INPUTS) &&
-           verifier.VerifyVector(inputs()) &&
-           VerifyOffset(verifier, VT_OUTPUTS) &&
-           verifier.VerifyVector(outputs()) &&
-           VerifyOffset(verifier, VT_OPERATORS) &&
-           verifier.VerifyVector(operators()) &&
-           verifier.VerifyVectorOfTables(operators()) &&
-           VerifyOffset(verifier, VT_NAME) &&
-           verifier.VerifyString(name()) &&
-           verifier.EndTable();
-  }
-  SubGraphT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SubGraph> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SubGraphBuilder {
-  typedef SubGraph Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_tensors(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Tensor>>> tensors) {
-    fbb_.AddOffset(SubGraph::VT_TENSORS, tensors);
-  }
-  void add_inputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs) {
-    fbb_.AddOffset(SubGraph::VT_INPUTS, inputs);
-  }
-  void add_outputs(flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs) {
-    fbb_.AddOffset(SubGraph::VT_OUTPUTS, outputs);
-  }
-  void add_operators(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Operator>>> operators) {
-    fbb_.AddOffset(SubGraph::VT_OPERATORS, operators);
-  }
-  void add_name(flatbuffers::Offset<flatbuffers::String> name) {
-    fbb_.AddOffset(SubGraph::VT_NAME, name);
-  }
-  explicit SubGraphBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SubGraph> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SubGraph>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SubGraph> CreateSubGraph(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Tensor>>> tensors = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> inputs = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> outputs = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Operator>>> operators = 0,
-    flatbuffers::Offset<flatbuffers::String> name = 0) {
-  SubGraphBuilder builder_(_fbb);
-  builder_.add_name(name);
-  builder_.add_operators(operators);
-  builder_.add_outputs(outputs);
-  builder_.add_inputs(inputs);
-  builder_.add_tensors(tensors);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<SubGraph> CreateSubGraphDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<flatbuffers::Offset<tflite::Tensor>> *tensors = nullptr,
-    const std::vector<int32_t> *inputs = nullptr,
-    const std::vector<int32_t> *outputs = nullptr,
-    const std::vector<flatbuffers::Offset<tflite::Operator>> *operators = nullptr,
-    const char *name = nullptr) {
-  auto tensors__ = tensors ? _fbb.CreateVector<flatbuffers::Offset<tflite::Tensor>>(*tensors) : 0;
-  auto inputs__ = inputs ? _fbb.CreateVector<int32_t>(*inputs) : 0;
-  auto outputs__ = outputs ? _fbb.CreateVector<int32_t>(*outputs) : 0;
-  auto operators__ = operators ? _fbb.CreateVector<flatbuffers::Offset<tflite::Operator>>(*operators) : 0;
-  auto name__ = name ? _fbb.CreateString(name) : 0;
-  return tflite::CreateSubGraph(
-      _fbb,
-      tensors__,
-      inputs__,
-      outputs__,
-      operators__,
-      name__);
-}
-
-flatbuffers::Offset<SubGraph> CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct BufferT : public flatbuffers::NativeTable {
-  typedef Buffer TableType;
-  std::vector<uint8_t> data{};
-};
-
-struct Buffer FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef BufferT NativeTableType;
-  typedef BufferBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_DATA = 4
-  };
-  const flatbuffers::Vector<uint8_t> *data() const {
-    return GetPointer<const flatbuffers::Vector<uint8_t> *>(VT_DATA);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_DATA) &&
-           verifier.VerifyVector(data()) &&
-           verifier.EndTable();
-  }
-  BufferT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<Buffer> Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct BufferBuilder {
-  typedef Buffer Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_data(flatbuffers::Offset<flatbuffers::Vector<uint8_t>> data) {
-    fbb_.AddOffset(Buffer::VT_DATA, data);
-  }
-  explicit BufferBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<Buffer> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Buffer>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Buffer> CreateBuffer(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<uint8_t>> data = 0) {
-  BufferBuilder builder_(_fbb);
-  builder_.add_data(data);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Buffer> CreateBufferDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<uint8_t> *data = nullptr) {
-  if (data) { _fbb.ForceVectorAlignment(data->size(), sizeof(uint8_t), 16); }
-  auto data__ = data ? _fbb.CreateVector<uint8_t>(*data) : 0;
-  return tflite::CreateBuffer(
-      _fbb,
-      data__);
-}
-
-flatbuffers::Offset<Buffer> CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct MetadataT : public flatbuffers::NativeTable {
-  typedef Metadata TableType;
-  std::string name{};
-  uint32_t buffer = 0;
-};
-
-struct Metadata FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef MetadataT NativeTableType;
-  typedef MetadataBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_NAME = 4,
-    VT_BUFFER = 6
-  };
-  const flatbuffers::String *name() const {
-    return GetPointer<const flatbuffers::String *>(VT_NAME);
-  }
-  uint32_t buffer() const {
-    return GetField<uint32_t>(VT_BUFFER, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_NAME) &&
-           verifier.VerifyString(name()) &&
-           VerifyField<uint32_t>(verifier, VT_BUFFER, 4) &&
-           verifier.EndTable();
-  }
-  MetadataT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(MetadataT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<Metadata> Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct MetadataBuilder {
-  typedef Metadata Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_name(flatbuffers::Offset<flatbuffers::String> name) {
-    fbb_.AddOffset(Metadata::VT_NAME, name);
-  }
-  void add_buffer(uint32_t buffer) {
-    fbb_.AddElement<uint32_t>(Metadata::VT_BUFFER, buffer, 0);
-  }
-  explicit MetadataBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<Metadata> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Metadata>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Metadata> CreateMetadata(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::String> name = 0,
-    uint32_t buffer = 0) {
-  MetadataBuilder builder_(_fbb);
-  builder_.add_buffer(buffer);
-  builder_.add_name(name);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Metadata> CreateMetadataDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const char *name = nullptr,
-    uint32_t buffer = 0) {
-  auto name__ = name ? _fbb.CreateString(name) : 0;
-  return tflite::CreateMetadata(
-      _fbb,
-      name__,
-      buffer);
-}
-
-flatbuffers::Offset<Metadata> CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct TensorMapT : public flatbuffers::NativeTable {
-  typedef TensorMap TableType;
-  std::string name{};
-  uint32_t tensor_index = 0;
-};
-
-struct TensorMap FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef TensorMapT NativeTableType;
-  typedef TensorMapBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_NAME = 4,
-    VT_TENSOR_INDEX = 6
-  };
-  const flatbuffers::String *name() const {
-    return GetPointer<const flatbuffers::String *>(VT_NAME);
-  }
-  uint32_t tensor_index() const {
-    return GetField<uint32_t>(VT_TENSOR_INDEX, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_NAME) &&
-           verifier.VerifyString(name()) &&
-           VerifyField<uint32_t>(verifier, VT_TENSOR_INDEX, 4) &&
-           verifier.EndTable();
-  }
-  TensorMapT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(TensorMapT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<TensorMap> Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct TensorMapBuilder {
-  typedef TensorMap Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_name(flatbuffers::Offset<flatbuffers::String> name) {
-    fbb_.AddOffset(TensorMap::VT_NAME, name);
-  }
-  void add_tensor_index(uint32_t tensor_index) {
-    fbb_.AddElement<uint32_t>(TensorMap::VT_TENSOR_INDEX, tensor_index, 0);
-  }
-  explicit TensorMapBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<TensorMap> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<TensorMap>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<TensorMap> CreateTensorMap(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::String> name = 0,
-    uint32_t tensor_index = 0) {
-  TensorMapBuilder builder_(_fbb);
-  builder_.add_tensor_index(tensor_index);
-  builder_.add_name(name);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<TensorMap> CreateTensorMapDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const char *name = nullptr,
-    uint32_t tensor_index = 0) {
-  auto name__ = name ? _fbb.CreateString(name) : 0;
-  return tflite::CreateTensorMap(
-      _fbb,
-      name__,
-      tensor_index);
-}
-
-flatbuffers::Offset<TensorMap> CreateTensorMap(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct SignatureDefT : public flatbuffers::NativeTable {
-  typedef SignatureDef TableType;
-  std::vector<std::unique_ptr<tflite::TensorMapT>> inputs{};
-  std::vector<std::unique_ptr<tflite::TensorMapT>> outputs{};
-  std::string signature_key{};
-  uint32_t subgraph_index = 0;
-  SignatureDefT() = default;
-  SignatureDefT(const SignatureDefT &o);
-  SignatureDefT(SignatureDefT&&) FLATBUFFERS_NOEXCEPT = default;
-  SignatureDefT &operator=(SignatureDefT o) FLATBUFFERS_NOEXCEPT;
-};
-
-struct SignatureDef FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef SignatureDefT NativeTableType;
-  typedef SignatureDefBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_INPUTS = 4,
-    VT_OUTPUTS = 6,
-    VT_SIGNATURE_KEY = 8,
-    VT_SUBGRAPH_INDEX = 12
-  };
-  const flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>> *inputs() const {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>> *>(VT_INPUTS);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>> *outputs() const {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>> *>(VT_OUTPUTS);
-  }
-  const flatbuffers::String *signature_key() const {
-    return GetPointer<const flatbuffers::String *>(VT_SIGNATURE_KEY);
-  }
-  uint32_t subgraph_index() const {
-    return GetField<uint32_t>(VT_SUBGRAPH_INDEX, 0);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyOffset(verifier, VT_INPUTS) &&
-           verifier.VerifyVector(inputs()) &&
-           verifier.VerifyVectorOfTables(inputs()) &&
-           VerifyOffset(verifier, VT_OUTPUTS) &&
-           verifier.VerifyVector(outputs()) &&
-           verifier.VerifyVectorOfTables(outputs()) &&
-           VerifyOffset(verifier, VT_SIGNATURE_KEY) &&
-           verifier.VerifyString(signature_key()) &&
-           VerifyField<uint32_t>(verifier, VT_SUBGRAPH_INDEX, 4) &&
-           verifier.EndTable();
-  }
-  SignatureDefT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(SignatureDefT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<SignatureDef> Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct SignatureDefBuilder {
-  typedef SignatureDef Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_inputs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>>> inputs) {
-    fbb_.AddOffset(SignatureDef::VT_INPUTS, inputs);
-  }
-  void add_outputs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>>> outputs) {
-    fbb_.AddOffset(SignatureDef::VT_OUTPUTS, outputs);
-  }
-  void add_signature_key(flatbuffers::Offset<flatbuffers::String> signature_key) {
-    fbb_.AddOffset(SignatureDef::VT_SIGNATURE_KEY, signature_key);
-  }
-  void add_subgraph_index(uint32_t subgraph_index) {
-    fbb_.AddElement<uint32_t>(SignatureDef::VT_SUBGRAPH_INDEX, subgraph_index, 0);
-  }
-  explicit SignatureDefBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<SignatureDef> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<SignatureDef>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<SignatureDef> CreateSignatureDef(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>>> inputs = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::TensorMap>>> outputs = 0,
-    flatbuffers::Offset<flatbuffers::String> signature_key = 0,
-    uint32_t subgraph_index = 0) {
-  SignatureDefBuilder builder_(_fbb);
-  builder_.add_subgraph_index(subgraph_index);
-  builder_.add_signature_key(signature_key);
-  builder_.add_outputs(outputs);
-  builder_.add_inputs(inputs);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<SignatureDef> CreateSignatureDefDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    const std::vector<flatbuffers::Offset<tflite::TensorMap>> *inputs = nullptr,
-    const std::vector<flatbuffers::Offset<tflite::TensorMap>> *outputs = nullptr,
-    const char *signature_key = nullptr,
-    uint32_t subgraph_index = 0) {
-  auto inputs__ = inputs ? _fbb.CreateVector<flatbuffers::Offset<tflite::TensorMap>>(*inputs) : 0;
-  auto outputs__ = outputs ? _fbb.CreateVector<flatbuffers::Offset<tflite::TensorMap>>(*outputs) : 0;
-  auto signature_key__ = signature_key ? _fbb.CreateString(signature_key) : 0;
-  return tflite::CreateSignatureDef(
-      _fbb,
-      inputs__,
-      outputs__,
-      signature_key__,
-      subgraph_index);
-}
-
-flatbuffers::Offset<SignatureDef> CreateSignatureDef(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-struct ModelT : public flatbuffers::NativeTable {
-  typedef Model TableType;
-  uint32_t version = 0;
-  std::vector<std::unique_ptr<tflite::OperatorCodeT>> operator_codes{};
-  std::vector<std::unique_ptr<tflite::SubGraphT>> subgraphs{};
-  std::string description{};
-  std::vector<std::unique_ptr<tflite::BufferT>> buffers{};
-  std::vector<int32_t> metadata_buffer{};
-  std::vector<std::unique_ptr<tflite::MetadataT>> metadata{};
-  std::vector<std::unique_ptr<tflite::SignatureDefT>> signature_defs{};
-  ModelT() = default;
-  ModelT(const ModelT &o);
-  ModelT(ModelT&&) FLATBUFFERS_NOEXCEPT = default;
-  ModelT &operator=(ModelT o) FLATBUFFERS_NOEXCEPT;
-};
-
-struct Model FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table {
-  typedef ModelT NativeTableType;
-  typedef ModelBuilder Builder;
-  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
-    VT_VERSION = 4,
-    VT_OPERATOR_CODES = 6,
-    VT_SUBGRAPHS = 8,
-    VT_DESCRIPTION = 10,
-    VT_BUFFERS = 12,
-    VT_METADATA_BUFFER = 14,
-    VT_METADATA = 16,
-    VT_SIGNATURE_DEFS = 18
-  };
-  uint32_t version() const {
-    return GetField<uint32_t>(VT_VERSION, 0);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<tflite::OperatorCode>> *operator_codes() const {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::OperatorCode>> *>(VT_OPERATOR_CODES);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<tflite::SubGraph>> *subgraphs() const {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::SubGraph>> *>(VT_SUBGRAPHS);
-  }
-  const flatbuffers::String *description() const {
-    return GetPointer<const flatbuffers::String *>(VT_DESCRIPTION);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<tflite::Buffer>> *buffers() const {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::Buffer>> *>(VT_BUFFERS);
-  }
-  const flatbuffers::Vector<int32_t> *metadata_buffer() const {
-    return GetPointer<const flatbuffers::Vector<int32_t> *>(VT_METADATA_BUFFER);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<tflite::Metadata>> *metadata() const {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::Metadata>> *>(VT_METADATA);
-  }
-  const flatbuffers::Vector<flatbuffers::Offset<tflite::SignatureDef>> *signature_defs() const {
-    return GetPointer<const flatbuffers::Vector<flatbuffers::Offset<tflite::SignatureDef>> *>(VT_SIGNATURE_DEFS);
-  }
-  bool Verify(flatbuffers::Verifier &verifier) const {
-    return VerifyTableStart(verifier) &&
-           VerifyField<uint32_t>(verifier, VT_VERSION, 4) &&
-           VerifyOffset(verifier, VT_OPERATOR_CODES) &&
-           verifier.VerifyVector(operator_codes()) &&
-           verifier.VerifyVectorOfTables(operator_codes()) &&
-           VerifyOffset(verifier, VT_SUBGRAPHS) &&
-           verifier.VerifyVector(subgraphs()) &&
-           verifier.VerifyVectorOfTables(subgraphs()) &&
-           VerifyOffset(verifier, VT_DESCRIPTION) &&
-           verifier.VerifyString(description()) &&
-           VerifyOffset(verifier, VT_BUFFERS) &&
-           verifier.VerifyVector(buffers()) &&
-           verifier.VerifyVectorOfTables(buffers()) &&
-           VerifyOffset(verifier, VT_METADATA_BUFFER) &&
-           verifier.VerifyVector(metadata_buffer()) &&
-           VerifyOffset(verifier, VT_METADATA) &&
-           verifier.VerifyVector(metadata()) &&
-           verifier.VerifyVectorOfTables(metadata()) &&
-           VerifyOffset(verifier, VT_SIGNATURE_DEFS) &&
-           verifier.VerifyVector(signature_defs()) &&
-           verifier.VerifyVectorOfTables(signature_defs()) &&
-           verifier.EndTable();
-  }
-  ModelT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  void UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const;
-  static flatbuffers::Offset<Model> Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-};
-
-struct ModelBuilder {
-  typedef Model Table;
-  flatbuffers::FlatBufferBuilder &fbb_;
-  flatbuffers::uoffset_t start_;
-  void add_version(uint32_t version) {
-    fbb_.AddElement<uint32_t>(Model::VT_VERSION, version, 0);
-  }
-  void add_operator_codes(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::OperatorCode>>> operator_codes) {
-    fbb_.AddOffset(Model::VT_OPERATOR_CODES, operator_codes);
-  }
-  void add_subgraphs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::SubGraph>>> subgraphs) {
-    fbb_.AddOffset(Model::VT_SUBGRAPHS, subgraphs);
-  }
-  void add_description(flatbuffers::Offset<flatbuffers::String> description) {
-    fbb_.AddOffset(Model::VT_DESCRIPTION, description);
-  }
-  void add_buffers(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Buffer>>> buffers) {
-    fbb_.AddOffset(Model::VT_BUFFERS, buffers);
-  }
-  void add_metadata_buffer(flatbuffers::Offset<flatbuffers::Vector<int32_t>> metadata_buffer) {
-    fbb_.AddOffset(Model::VT_METADATA_BUFFER, metadata_buffer);
-  }
-  void add_metadata(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Metadata>>> metadata) {
-    fbb_.AddOffset(Model::VT_METADATA, metadata);
-  }
-  void add_signature_defs(flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::SignatureDef>>> signature_defs) {
-    fbb_.AddOffset(Model::VT_SIGNATURE_DEFS, signature_defs);
-  }
-  explicit ModelBuilder(flatbuffers::FlatBufferBuilder &_fbb)
-        : fbb_(_fbb) {
-    start_ = fbb_.StartTable();
-  }
-  flatbuffers::Offset<Model> Finish() {
-    const auto end = fbb_.EndTable(start_);
-    auto o = flatbuffers::Offset<Model>(end);
-    return o;
-  }
-};
-
-inline flatbuffers::Offset<Model> CreateModel(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    uint32_t version = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::OperatorCode>>> operator_codes = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::SubGraph>>> subgraphs = 0,
-    flatbuffers::Offset<flatbuffers::String> description = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Buffer>>> buffers = 0,
-    flatbuffers::Offset<flatbuffers::Vector<int32_t>> metadata_buffer = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::Metadata>>> metadata = 0,
-    flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<tflite::SignatureDef>>> signature_defs = 0) {
-  ModelBuilder builder_(_fbb);
-  builder_.add_signature_defs(signature_defs);
-  builder_.add_metadata(metadata);
-  builder_.add_metadata_buffer(metadata_buffer);
-  builder_.add_buffers(buffers);
-  builder_.add_description(description);
-  builder_.add_subgraphs(subgraphs);
-  builder_.add_operator_codes(operator_codes);
-  builder_.add_version(version);
-  return builder_.Finish();
-}
-
-inline flatbuffers::Offset<Model> CreateModelDirect(
-    flatbuffers::FlatBufferBuilder &_fbb,
-    uint32_t version = 0,
-    const std::vector<flatbuffers::Offset<tflite::OperatorCode>> *operator_codes = nullptr,
-    const std::vector<flatbuffers::Offset<tflite::SubGraph>> *subgraphs = nullptr,
-    const char *description = nullptr,
-    const std::vector<flatbuffers::Offset<tflite::Buffer>> *buffers = nullptr,
-    const std::vector<int32_t> *metadata_buffer = nullptr,
-    const std::vector<flatbuffers::Offset<tflite::Metadata>> *metadata = nullptr,
-    const std::vector<flatbuffers::Offset<tflite::SignatureDef>> *signature_defs = nullptr) {
-  auto operator_codes__ = operator_codes ? _fbb.CreateVector<flatbuffers::Offset<tflite::OperatorCode>>(*operator_codes) : 0;
-  auto subgraphs__ = subgraphs ? _fbb.CreateVector<flatbuffers::Offset<tflite::SubGraph>>(*subgraphs) : 0;
-  auto description__ = description ? _fbb.CreateString(description) : 0;
-  auto buffers__ = buffers ? _fbb.CreateVector<flatbuffers::Offset<tflite::Buffer>>(*buffers) : 0;
-  auto metadata_buffer__ = metadata_buffer ? _fbb.CreateVector<int32_t>(*metadata_buffer) : 0;
-  auto metadata__ = metadata ? _fbb.CreateVector<flatbuffers::Offset<tflite::Metadata>>(*metadata) : 0;
-  auto signature_defs__ = signature_defs ? _fbb.CreateVector<flatbuffers::Offset<tflite::SignatureDef>>(*signature_defs) : 0;
-  return tflite::CreateModel(
-      _fbb,
-      version,
-      operator_codes__,
-      subgraphs__,
-      description__,
-      buffers__,
-      metadata_buffer__,
-      metadata__,
-      signature_defs__);
-}
-
-flatbuffers::Offset<Model> CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr);
-
-inline CustomQuantizationT *CustomQuantization::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<CustomQuantizationT>(new CustomQuantizationT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void CustomQuantization::UnPackTo(CustomQuantizationT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = custom(); if (_e) { _o->custom.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom.begin()); } }
-}
-
-inline flatbuffers::Offset<CustomQuantization> CustomQuantization::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateCustomQuantization(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<CustomQuantization> CreateCustomQuantization(flatbuffers::FlatBufferBuilder &_fbb, const CustomQuantizationT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CustomQuantizationT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  _fbb.ForceVectorAlignment(_o->custom.size(), sizeof(uint8_t), 16);
-  auto _custom = _o->custom.size() ? _fbb.CreateVector(_o->custom) : 0;
-  return tflite::CreateCustomQuantization(
-      _fbb,
-      _custom);
-}
-
-inline QuantizationParametersT *QuantizationParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<QuantizationParametersT>(new QuantizationParametersT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void QuantizationParameters::UnPackTo(QuantizationParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = min(); if (_e) { _o->min.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->min[_i] = _e->Get(_i); } } }
-  { auto _e = max(); if (_e) { _o->max.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->max[_i] = _e->Get(_i); } } }
-  { auto _e = scale(); if (_e) { _o->scale.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->scale[_i] = _e->Get(_i); } } }
-  { auto _e = zero_point(); if (_e) { _o->zero_point.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->zero_point[_i] = _e->Get(_i); } } }
-  { auto _e = details_type(); _o->details.type = _e; }
-  { auto _e = details(); if (_e) _o->details.value = tflite::QuantizationDetailsUnion::UnPack(_e, details_type(), _resolver); }
-  { auto _e = quantized_dimension(); _o->quantized_dimension = _e; }
-}
-
-inline flatbuffers::Offset<QuantizationParameters> QuantizationParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateQuantizationParameters(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<QuantizationParameters> CreateQuantizationParameters(flatbuffers::FlatBufferBuilder &_fbb, const QuantizationParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizationParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _min = _o->min.size() ? _fbb.CreateVector(_o->min) : 0;
-  auto _max = _o->max.size() ? _fbb.CreateVector(_o->max) : 0;
-  auto _scale = _o->scale.size() ? _fbb.CreateVector(_o->scale) : 0;
-  auto _zero_point = _o->zero_point.size() ? _fbb.CreateVector(_o->zero_point) : 0;
-  auto _details_type = _o->details.type;
-  auto _details = _o->details.Pack(_fbb);
-  auto _quantized_dimension = _o->quantized_dimension;
-  return tflite::CreateQuantizationParameters(
-      _fbb,
-      _min,
-      _max,
-      _scale,
-      _zero_point,
-      _details_type,
-      _details,
-      _quantized_dimension);
-}
-
-inline Int32VectorT *Int32Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<Int32VectorT>(new Int32VectorT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void Int32Vector::UnPackTo(Int32VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } }
-}
-
-inline flatbuffers::Offset<Int32Vector> Int32Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateInt32Vector(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<Int32Vector> CreateInt32Vector(flatbuffers::FlatBufferBuilder &_fbb, const Int32VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Int32VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0;
-  return tflite::CreateInt32Vector(
-      _fbb,
-      _values);
-}
-
-inline Uint16VectorT *Uint16Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<Uint16VectorT>(new Uint16VectorT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void Uint16Vector::UnPackTo(Uint16VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = values(); if (_e) { _o->values.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->values[_i] = _e->Get(_i); } } }
-}
-
-inline flatbuffers::Offset<Uint16Vector> Uint16Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateUint16Vector(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<Uint16Vector> CreateUint16Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint16VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Uint16VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  _fbb.ForceVectorAlignment(_o->values.size(), sizeof(uint16_t), 4);
-  auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0;
-  return tflite::CreateUint16Vector(
-      _fbb,
-      _values);
-}
-
-inline Uint8VectorT *Uint8Vector::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<Uint8VectorT>(new Uint8VectorT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void Uint8Vector::UnPackTo(Uint8VectorT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = values(); if (_e) { _o->values.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->values.begin()); } }
-}
-
-inline flatbuffers::Offset<Uint8Vector> Uint8Vector::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateUint8Vector(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<Uint8Vector> CreateUint8Vector(flatbuffers::FlatBufferBuilder &_fbb, const Uint8VectorT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Uint8VectorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  _fbb.ForceVectorAlignment(_o->values.size(), sizeof(uint8_t), 4);
-  auto _values = _o->values.size() ? _fbb.CreateVector(_o->values) : 0;
-  return tflite::CreateUint8Vector(
-      _fbb,
-      _values);
-}
-
-inline DimensionMetadataT *DimensionMetadata::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<DimensionMetadataT>(new DimensionMetadataT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void DimensionMetadata::UnPackTo(DimensionMetadataT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = format(); _o->format = _e; }
-  { auto _e = dense_size(); _o->dense_size = _e; }
-  { auto _e = array_segments_type(); _o->array_segments.type = _e; }
-  { auto _e = array_segments(); if (_e) _o->array_segments.value = tflite::SparseIndexVectorUnion::UnPack(_e, array_segments_type(), _resolver); }
-  { auto _e = array_indices_type(); _o->array_indices.type = _e; }
-  { auto _e = array_indices(); if (_e) _o->array_indices.value = tflite::SparseIndexVectorUnion::UnPack(_e, array_indices_type(), _resolver); }
-}
-
-inline flatbuffers::Offset<DimensionMetadata> DimensionMetadata::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateDimensionMetadata(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<DimensionMetadata> CreateDimensionMetadata(flatbuffers::FlatBufferBuilder &_fbb, const DimensionMetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DimensionMetadataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _format = _o->format;
-  auto _dense_size = _o->dense_size;
-  auto _array_segments_type = _o->array_segments.type;
-  auto _array_segments = _o->array_segments.Pack(_fbb);
-  auto _array_indices_type = _o->array_indices.type;
-  auto _array_indices = _o->array_indices.Pack(_fbb);
-  return tflite::CreateDimensionMetadata(
-      _fbb,
-      _format,
-      _dense_size,
-      _array_segments_type,
-      _array_segments,
-      _array_indices_type,
-      _array_indices);
-}
-
-inline SparsityParametersT::SparsityParametersT(const SparsityParametersT &o)
-      : traversal_order(o.traversal_order),
-        block_map(o.block_map) {
-  dim_metadata.reserve(o.dim_metadata.size());
-  for (const auto &dim_metadata_ : o.dim_metadata) { dim_metadata.emplace_back((dim_metadata_) ? new tflite::DimensionMetadataT(*dim_metadata_) : nullptr); }
-}
-
-inline SparsityParametersT &SparsityParametersT::operator=(SparsityParametersT o) FLATBUFFERS_NOEXCEPT {
-  std::swap(traversal_order, o.traversal_order);
-  std::swap(block_map, o.block_map);
-  std::swap(dim_metadata, o.dim_metadata);
-  return *this;
-}
-
-inline SparsityParametersT *SparsityParameters::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SparsityParametersT>(new SparsityParametersT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SparsityParameters::UnPackTo(SparsityParametersT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = traversal_order(); if (_e) { _o->traversal_order.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->traversal_order[_i] = _e->Get(_i); } } }
-  { auto _e = block_map(); if (_e) { _o->block_map.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->block_map[_i] = _e->Get(_i); } } }
-  { auto _e = dim_metadata(); if (_e) { _o->dim_metadata.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->dim_metadata[_i]) { _e->Get(_i)->UnPackTo(_o->dim_metadata[_i].get(), _resolver); } else { _o->dim_metadata[_i] = std::unique_ptr<tflite::DimensionMetadataT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
-}
-
-inline flatbuffers::Offset<SparsityParameters> SparsityParameters::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSparsityParameters(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SparsityParameters> CreateSparsityParameters(flatbuffers::FlatBufferBuilder &_fbb, const SparsityParametersT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SparsityParametersT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _traversal_order = _o->traversal_order.size() ? _fbb.CreateVector(_o->traversal_order) : 0;
-  auto _block_map = _o->block_map.size() ? _fbb.CreateVector(_o->block_map) : 0;
-  auto _dim_metadata = _o->dim_metadata.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::DimensionMetadata>> (_o->dim_metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateDimensionMetadata(*__va->__fbb, __va->__o->dim_metadata[i].get(), __va->__rehasher); }, &_va ) : 0;
-  return tflite::CreateSparsityParameters(
-      _fbb,
-      _traversal_order,
-      _block_map,
-      _dim_metadata);
-}
-
-inline VariantSubTypeT *VariantSubType::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<VariantSubTypeT>(new VariantSubTypeT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void VariantSubType::UnPackTo(VariantSubTypeT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } }
-  { auto _e = type(); _o->type = _e; }
-  { auto _e = has_rank(); _o->has_rank = _e; }
-}
-
-inline flatbuffers::Offset<VariantSubType> VariantSubType::Pack(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateVariantSubType(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<VariantSubType> CreateVariantSubType(flatbuffers::FlatBufferBuilder &_fbb, const VariantSubTypeT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const VariantSubTypeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0;
-  auto _type = _o->type;
-  auto _has_rank = _o->has_rank;
-  return tflite::CreateVariantSubType(
-      _fbb,
-      _shape,
-      _type,
-      _has_rank);
-}
-
-inline TensorT::TensorT(const TensorT &o)
-      : shape(o.shape),
-        type(o.type),
-        buffer(o.buffer),
-        name(o.name),
-        quantization((o.quantization) ? new tflite::QuantizationParametersT(*o.quantization) : nullptr),
-        is_variable(o.is_variable),
-        sparsity((o.sparsity) ? new tflite::SparsityParametersT(*o.sparsity) : nullptr),
-        shape_signature(o.shape_signature),
-        has_rank(o.has_rank) {
-  variant_tensors.reserve(o.variant_tensors.size());
-  for (const auto &variant_tensors_ : o.variant_tensors) { variant_tensors.emplace_back((variant_tensors_) ? new tflite::VariantSubTypeT(*variant_tensors_) : nullptr); }
-}
-
-inline TensorT &TensorT::operator=(TensorT o) FLATBUFFERS_NOEXCEPT {
-  std::swap(shape, o.shape);
-  std::swap(type, o.type);
-  std::swap(buffer, o.buffer);
-  std::swap(name, o.name);
-  std::swap(quantization, o.quantization);
-  std::swap(is_variable, o.is_variable);
-  std::swap(sparsity, o.sparsity);
-  std::swap(shape_signature, o.shape_signature);
-  std::swap(has_rank, o.has_rank);
-  std::swap(variant_tensors, o.variant_tensors);
-  return *this;
-}
-
-inline TensorT *Tensor::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<TensorT>(new TensorT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void Tensor::UnPackTo(TensorT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = shape(); if (_e) { _o->shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape[_i] = _e->Get(_i); } } }
-  { auto _e = type(); _o->type = _e; }
-  { auto _e = buffer(); _o->buffer = _e; }
-  { auto _e = name(); if (_e) _o->name = _e->str(); }
-  { auto _e = quantization(); if (_e) { if(_o->quantization) { _e->UnPackTo(_o->quantization.get(), _resolver); } else { _o->quantization = std::unique_ptr<tflite::QuantizationParametersT>(_e->UnPack(_resolver)); } } }
-  { auto _e = is_variable(); _o->is_variable = _e; }
-  { auto _e = sparsity(); if (_e) { if(_o->sparsity) { _e->UnPackTo(_o->sparsity.get(), _resolver); } else { _o->sparsity = std::unique_ptr<tflite::SparsityParametersT>(_e->UnPack(_resolver)); } } }
-  { auto _e = shape_signature(); if (_e) { _o->shape_signature.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->shape_signature[_i] = _e->Get(_i); } } }
-  { auto _e = has_rank(); _o->has_rank = _e; }
-  { auto _e = variant_tensors(); if (_e) { _o->variant_tensors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->variant_tensors[_i]) { _e->Get(_i)->UnPackTo(_o->variant_tensors[_i].get(), _resolver); } else { _o->variant_tensors[_i] = std::unique_ptr<tflite::VariantSubTypeT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
-}
-
-inline flatbuffers::Offset<Tensor> Tensor::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateTensor(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<Tensor> CreateTensor(flatbuffers::FlatBufferBuilder &_fbb, const TensorT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TensorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _shape = _o->shape.size() ? _fbb.CreateVector(_o->shape) : 0;
-  auto _type = _o->type;
-  auto _buffer = _o->buffer;
-  auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name);
-  auto _quantization = _o->quantization ? CreateQuantizationParameters(_fbb, _o->quantization.get(), _rehasher) : 0;
-  auto _is_variable = _o->is_variable;
-  auto _sparsity = _o->sparsity ? CreateSparsityParameters(_fbb, _o->sparsity.get(), _rehasher) : 0;
-  auto _shape_signature = _o->shape_signature.size() ? _fbb.CreateVector(_o->shape_signature) : 0;
-  auto _has_rank = _o->has_rank;
-  auto _variant_tensors = _o->variant_tensors.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::VariantSubType>> (_o->variant_tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateVariantSubType(*__va->__fbb, __va->__o->variant_tensors[i].get(), __va->__rehasher); }, &_va ) : 0;
-  return tflite::CreateTensor(
-      _fbb,
-      _shape,
-      _type,
-      _buffer,
-      _name,
-      _quantization,
-      _is_variable,
-      _sparsity,
-      _shape_signature,
-      _has_rank,
-      _variant_tensors);
-}
-
-inline Conv2DOptionsT *Conv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<Conv2DOptionsT>(new Conv2DOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void Conv2DOptions::UnPackTo(Conv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = padding(); _o->padding = _e; }
-  { auto _e = stride_w(); _o->stride_w = _e; }
-  { auto _e = stride_h(); _o->stride_h = _e; }
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; }
-  { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; }
-}
-
-inline flatbuffers::Offset<Conv2DOptions> Conv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateConv2DOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<Conv2DOptions> CreateConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Conv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _padding = _o->padding;
-  auto _stride_w = _o->stride_w;
-  auto _stride_h = _o->stride_h;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _dilation_w_factor = _o->dilation_w_factor;
-  auto _dilation_h_factor = _o->dilation_h_factor;
-  return tflite::CreateConv2DOptions(
-      _fbb,
-      _padding,
-      _stride_w,
-      _stride_h,
-      _fused_activation_function,
-      _dilation_w_factor,
-      _dilation_h_factor);
-}
-
-inline Conv3DOptionsT *Conv3DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<Conv3DOptionsT>(new Conv3DOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void Conv3DOptions::UnPackTo(Conv3DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = padding(); _o->padding = _e; }
-  { auto _e = stride_d(); _o->stride_d = _e; }
-  { auto _e = stride_w(); _o->stride_w = _e; }
-  { auto _e = stride_h(); _o->stride_h = _e; }
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = dilation_d_factor(); _o->dilation_d_factor = _e; }
-  { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; }
-  { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; }
-}
-
-inline flatbuffers::Offset<Conv3DOptions> Conv3DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateConv3DOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<Conv3DOptions> CreateConv3DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Conv3DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Conv3DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _padding = _o->padding;
-  auto _stride_d = _o->stride_d;
-  auto _stride_w = _o->stride_w;
-  auto _stride_h = _o->stride_h;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _dilation_d_factor = _o->dilation_d_factor;
-  auto _dilation_w_factor = _o->dilation_w_factor;
-  auto _dilation_h_factor = _o->dilation_h_factor;
-  return tflite::CreateConv3DOptions(
-      _fbb,
-      _padding,
-      _stride_d,
-      _stride_w,
-      _stride_h,
-      _fused_activation_function,
-      _dilation_d_factor,
-      _dilation_w_factor,
-      _dilation_h_factor);
-}
-
-inline Pool2DOptionsT *Pool2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<Pool2DOptionsT>(new Pool2DOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void Pool2DOptions::UnPackTo(Pool2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = padding(); _o->padding = _e; }
-  { auto _e = stride_w(); _o->stride_w = _e; }
-  { auto _e = stride_h(); _o->stride_h = _e; }
-  { auto _e = filter_width(); _o->filter_width = _e; }
-  { auto _e = filter_height(); _o->filter_height = _e; }
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-}
-
-inline flatbuffers::Offset<Pool2DOptions> Pool2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreatePool2DOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<Pool2DOptions> CreatePool2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const Pool2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Pool2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _padding = _o->padding;
-  auto _stride_w = _o->stride_w;
-  auto _stride_h = _o->stride_h;
-  auto _filter_width = _o->filter_width;
-  auto _filter_height = _o->filter_height;
-  auto _fused_activation_function = _o->fused_activation_function;
-  return tflite::CreatePool2DOptions(
-      _fbb,
-      _padding,
-      _stride_w,
-      _stride_h,
-      _filter_width,
-      _filter_height,
-      _fused_activation_function);
-}
-
-inline DepthwiseConv2DOptionsT *DepthwiseConv2DOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<DepthwiseConv2DOptionsT>(new DepthwiseConv2DOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void DepthwiseConv2DOptions::UnPackTo(DepthwiseConv2DOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = padding(); _o->padding = _e; }
-  { auto _e = stride_w(); _o->stride_w = _e; }
-  { auto _e = stride_h(); _o->stride_h = _e; }
-  { auto _e = depth_multiplier(); _o->depth_multiplier = _e; }
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = dilation_w_factor(); _o->dilation_w_factor = _e; }
-  { auto _e = dilation_h_factor(); _o->dilation_h_factor = _e; }
-}
-
-inline flatbuffers::Offset<DepthwiseConv2DOptions> DepthwiseConv2DOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateDepthwiseConv2DOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<DepthwiseConv2DOptions> CreateDepthwiseConv2DOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthwiseConv2DOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthwiseConv2DOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _padding = _o->padding;
-  auto _stride_w = _o->stride_w;
-  auto _stride_h = _o->stride_h;
-  auto _depth_multiplier = _o->depth_multiplier;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _dilation_w_factor = _o->dilation_w_factor;
-  auto _dilation_h_factor = _o->dilation_h_factor;
-  return tflite::CreateDepthwiseConv2DOptions(
-      _fbb,
-      _padding,
-      _stride_w,
-      _stride_h,
-      _depth_multiplier,
-      _fused_activation_function,
-      _dilation_w_factor,
-      _dilation_h_factor);
-}
-
-inline ConcatEmbeddingsOptionsT *ConcatEmbeddingsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ConcatEmbeddingsOptionsT>(new ConcatEmbeddingsOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ConcatEmbeddingsOptions::UnPackTo(ConcatEmbeddingsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = num_channels(); _o->num_channels = _e; }
-  { auto _e = num_columns_per_channel(); if (_e) { _o->num_columns_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->num_columns_per_channel[_i] = _e->Get(_i); } } }
-  { auto _e = embedding_dim_per_channel(); if (_e) { _o->embedding_dim_per_channel.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->embedding_dim_per_channel[_i] = _e->Get(_i); } } }
-}
-
-inline flatbuffers::Offset<ConcatEmbeddingsOptions> ConcatEmbeddingsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateConcatEmbeddingsOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ConcatEmbeddingsOptions> CreateConcatEmbeddingsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatEmbeddingsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatEmbeddingsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _num_channels = _o->num_channels;
-  auto _num_columns_per_channel = _o->num_columns_per_channel.size() ? _fbb.CreateVector(_o->num_columns_per_channel) : 0;
-  auto _embedding_dim_per_channel = _o->embedding_dim_per_channel.size() ? _fbb.CreateVector(_o->embedding_dim_per_channel) : 0;
-  return tflite::CreateConcatEmbeddingsOptions(
-      _fbb,
-      _num_channels,
-      _num_columns_per_channel,
-      _embedding_dim_per_channel);
-}
-
-inline LSHProjectionOptionsT *LSHProjectionOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<LSHProjectionOptionsT>(new LSHProjectionOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void LSHProjectionOptions::UnPackTo(LSHProjectionOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = type(); _o->type = _e; }
-}
-
-inline flatbuffers::Offset<LSHProjectionOptions> LSHProjectionOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateLSHProjectionOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<LSHProjectionOptions> CreateLSHProjectionOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSHProjectionOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSHProjectionOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _type = _o->type;
-  return tflite::CreateLSHProjectionOptions(
-      _fbb,
-      _type);
-}
-
-inline SVDFOptionsT *SVDFOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SVDFOptionsT>(new SVDFOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SVDFOptions::UnPackTo(SVDFOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = rank(); _o->rank = _e; }
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
-}
-
-inline flatbuffers::Offset<SVDFOptions> SVDFOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSVDFOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SVDFOptions> CreateSVDFOptions(flatbuffers::FlatBufferBuilder &_fbb, const SVDFOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SVDFOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _rank = _o->rank;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
-  return tflite::CreateSVDFOptions(
-      _fbb,
-      _rank,
-      _fused_activation_function,
-      _asymmetric_quantize_inputs);
-}
-
-inline RNNOptionsT *RNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<RNNOptionsT>(new RNNOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void RNNOptions::UnPackTo(RNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
-}
-
-inline flatbuffers::Offset<RNNOptions> RNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateRNNOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<RNNOptions> CreateRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const RNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
-  return tflite::CreateRNNOptions(
-      _fbb,
-      _fused_activation_function,
-      _asymmetric_quantize_inputs);
-}
-
-inline SequenceRNNOptionsT *SequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SequenceRNNOptionsT>(new SequenceRNNOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SequenceRNNOptions::UnPackTo(SequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = time_major(); _o->time_major = _e; }
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
-}
-
-inline flatbuffers::Offset<SequenceRNNOptions> SequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSequenceRNNOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SequenceRNNOptions> CreateSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const SequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _time_major = _o->time_major;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
-  return tflite::CreateSequenceRNNOptions(
-      _fbb,
-      _time_major,
-      _fused_activation_function,
-      _asymmetric_quantize_inputs);
-}
-
-inline BidirectionalSequenceRNNOptionsT *BidirectionalSequenceRNNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<BidirectionalSequenceRNNOptionsT>(new BidirectionalSequenceRNNOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void BidirectionalSequenceRNNOptions::UnPackTo(BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = time_major(); _o->time_major = _e; }
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = merge_outputs(); _o->merge_outputs = _e; }
-  { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
-}
-
-inline flatbuffers::Offset<BidirectionalSequenceRNNOptions> BidirectionalSequenceRNNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateBidirectionalSequenceRNNOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<BidirectionalSequenceRNNOptions> CreateBidirectionalSequenceRNNOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceRNNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceRNNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _time_major = _o->time_major;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _merge_outputs = _o->merge_outputs;
-  auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
-  return tflite::CreateBidirectionalSequenceRNNOptions(
-      _fbb,
-      _time_major,
-      _fused_activation_function,
-      _merge_outputs,
-      _asymmetric_quantize_inputs);
-}
-
-inline FullyConnectedOptionsT *FullyConnectedOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<FullyConnectedOptionsT>(new FullyConnectedOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void FullyConnectedOptions::UnPackTo(FullyConnectedOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = weights_format(); _o->weights_format = _e; }
-  { auto _e = keep_num_dims(); _o->keep_num_dims = _e; }
-  { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
-}
-
-inline flatbuffers::Offset<FullyConnectedOptions> FullyConnectedOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateFullyConnectedOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<FullyConnectedOptions> CreateFullyConnectedOptions(flatbuffers::FlatBufferBuilder &_fbb, const FullyConnectedOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FullyConnectedOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _weights_format = _o->weights_format;
-  auto _keep_num_dims = _o->keep_num_dims;
-  auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
-  return tflite::CreateFullyConnectedOptions(
-      _fbb,
-      _fused_activation_function,
-      _weights_format,
-      _keep_num_dims,
-      _asymmetric_quantize_inputs);
-}
-
-inline SoftmaxOptionsT *SoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SoftmaxOptionsT>(new SoftmaxOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SoftmaxOptions::UnPackTo(SoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = beta(); _o->beta = _e; }
-}
-
-inline flatbuffers::Offset<SoftmaxOptions> SoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSoftmaxOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SoftmaxOptions> CreateSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const SoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _beta = _o->beta;
-  return tflite::CreateSoftmaxOptions(
-      _fbb,
-      _beta);
-}
-
-inline ConcatenationOptionsT *ConcatenationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ConcatenationOptionsT>(new ConcatenationOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ConcatenationOptions::UnPackTo(ConcatenationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = axis(); _o->axis = _e; }
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-}
-
-inline flatbuffers::Offset<ConcatenationOptions> ConcatenationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateConcatenationOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ConcatenationOptions> CreateConcatenationOptions(flatbuffers::FlatBufferBuilder &_fbb, const ConcatenationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ConcatenationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _axis = _o->axis;
-  auto _fused_activation_function = _o->fused_activation_function;
-  return tflite::CreateConcatenationOptions(
-      _fbb,
-      _axis,
-      _fused_activation_function);
-}
-
-inline AddOptionsT *AddOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<AddOptionsT>(new AddOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void AddOptions::UnPackTo(AddOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = pot_scale_int16(); _o->pot_scale_int16 = _e; }
-}
-
-inline flatbuffers::Offset<AddOptions> AddOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateAddOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<AddOptions> CreateAddOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _pot_scale_int16 = _o->pot_scale_int16;
-  return tflite::CreateAddOptions(
-      _fbb,
-      _fused_activation_function,
-      _pot_scale_int16);
-}
-
-inline MulOptionsT *MulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<MulOptionsT>(new MulOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void MulOptions::UnPackTo(MulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-}
-
-inline flatbuffers::Offset<MulOptions> MulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateMulOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<MulOptions> CreateMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const MulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _fused_activation_function = _o->fused_activation_function;
-  return tflite::CreateMulOptions(
-      _fbb,
-      _fused_activation_function);
-}
-
-inline L2NormOptionsT *L2NormOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<L2NormOptionsT>(new L2NormOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void L2NormOptions::UnPackTo(L2NormOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-}
-
-inline flatbuffers::Offset<L2NormOptions> L2NormOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateL2NormOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<L2NormOptions> CreateL2NormOptions(flatbuffers::FlatBufferBuilder &_fbb, const L2NormOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const L2NormOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _fused_activation_function = _o->fused_activation_function;
-  return tflite::CreateL2NormOptions(
-      _fbb,
-      _fused_activation_function);
-}
-
-inline LocalResponseNormalizationOptionsT *LocalResponseNormalizationOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<LocalResponseNormalizationOptionsT>(new LocalResponseNormalizationOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void LocalResponseNormalizationOptions::UnPackTo(LocalResponseNormalizationOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = radius(); _o->radius = _e; }
-  { auto _e = bias(); _o->bias = _e; }
-  { auto _e = alpha(); _o->alpha = _e; }
-  { auto _e = beta(); _o->beta = _e; }
-}
-
-inline flatbuffers::Offset<LocalResponseNormalizationOptions> LocalResponseNormalizationOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateLocalResponseNormalizationOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<LocalResponseNormalizationOptions> CreateLocalResponseNormalizationOptions(flatbuffers::FlatBufferBuilder &_fbb, const LocalResponseNormalizationOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LocalResponseNormalizationOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _radius = _o->radius;
-  auto _bias = _o->bias;
-  auto _alpha = _o->alpha;
-  auto _beta = _o->beta;
-  return tflite::CreateLocalResponseNormalizationOptions(
-      _fbb,
-      _radius,
-      _bias,
-      _alpha,
-      _beta);
-}
-
-inline LSTMOptionsT *LSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<LSTMOptionsT>(new LSTMOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void LSTMOptions::UnPackTo(LSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = cell_clip(); _o->cell_clip = _e; }
-  { auto _e = proj_clip(); _o->proj_clip = _e; }
-  { auto _e = kernel_type(); _o->kernel_type = _e; }
-  { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
-}
-
-inline flatbuffers::Offset<LSTMOptions> LSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateLSTMOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<LSTMOptions> CreateLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const LSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _cell_clip = _o->cell_clip;
-  auto _proj_clip = _o->proj_clip;
-  auto _kernel_type = _o->kernel_type;
-  auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
-  return tflite::CreateLSTMOptions(
-      _fbb,
-      _fused_activation_function,
-      _cell_clip,
-      _proj_clip,
-      _kernel_type,
-      _asymmetric_quantize_inputs);
-}
-
-inline UnidirectionalSequenceLSTMOptionsT *UnidirectionalSequenceLSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<UnidirectionalSequenceLSTMOptionsT>(new UnidirectionalSequenceLSTMOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void UnidirectionalSequenceLSTMOptions::UnPackTo(UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = cell_clip(); _o->cell_clip = _e; }
-  { auto _e = proj_clip(); _o->proj_clip = _e; }
-  { auto _e = time_major(); _o->time_major = _e; }
-  { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
-}
-
-inline flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> UnidirectionalSequenceLSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateUnidirectionalSequenceLSTMOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<UnidirectionalSequenceLSTMOptions> CreateUnidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnidirectionalSequenceLSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _cell_clip = _o->cell_clip;
-  auto _proj_clip = _o->proj_clip;
-  auto _time_major = _o->time_major;
-  auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
-  return tflite::CreateUnidirectionalSequenceLSTMOptions(
-      _fbb,
-      _fused_activation_function,
-      _cell_clip,
-      _proj_clip,
-      _time_major,
-      _asymmetric_quantize_inputs);
-}
-
-inline BidirectionalSequenceLSTMOptionsT *BidirectionalSequenceLSTMOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<BidirectionalSequenceLSTMOptionsT>(new BidirectionalSequenceLSTMOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void BidirectionalSequenceLSTMOptions::UnPackTo(BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = cell_clip(); _o->cell_clip = _e; }
-  { auto _e = proj_clip(); _o->proj_clip = _e; }
-  { auto _e = merge_outputs(); _o->merge_outputs = _e; }
-  { auto _e = time_major(); _o->time_major = _e; }
-  { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
-}
-
-inline flatbuffers::Offset<BidirectionalSequenceLSTMOptions> BidirectionalSequenceLSTMOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateBidirectionalSequenceLSTMOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<BidirectionalSequenceLSTMOptions> CreateBidirectionalSequenceLSTMOptions(flatbuffers::FlatBufferBuilder &_fbb, const BidirectionalSequenceLSTMOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BidirectionalSequenceLSTMOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _cell_clip = _o->cell_clip;
-  auto _proj_clip = _o->proj_clip;
-  auto _merge_outputs = _o->merge_outputs;
-  auto _time_major = _o->time_major;
-  auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
-  return tflite::CreateBidirectionalSequenceLSTMOptions(
-      _fbb,
-      _fused_activation_function,
-      _cell_clip,
-      _proj_clip,
-      _merge_outputs,
-      _time_major,
-      _asymmetric_quantize_inputs);
-}
-
-inline ResizeBilinearOptionsT *ResizeBilinearOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ResizeBilinearOptionsT>(new ResizeBilinearOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ResizeBilinearOptions::UnPackTo(ResizeBilinearOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = align_corners(); _o->align_corners = _e; }
-  { auto _e = half_pixel_centers(); _o->half_pixel_centers = _e; }
-}
-
-inline flatbuffers::Offset<ResizeBilinearOptions> ResizeBilinearOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateResizeBilinearOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ResizeBilinearOptions> CreateResizeBilinearOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeBilinearOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeBilinearOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _align_corners = _o->align_corners;
-  auto _half_pixel_centers = _o->half_pixel_centers;
-  return tflite::CreateResizeBilinearOptions(
-      _fbb,
-      _align_corners,
-      _half_pixel_centers);
-}
-
-inline ResizeNearestNeighborOptionsT *ResizeNearestNeighborOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ResizeNearestNeighborOptionsT>(new ResizeNearestNeighborOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ResizeNearestNeighborOptions::UnPackTo(ResizeNearestNeighborOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = align_corners(); _o->align_corners = _e; }
-  { auto _e = half_pixel_centers(); _o->half_pixel_centers = _e; }
-}
-
-inline flatbuffers::Offset<ResizeNearestNeighborOptions> ResizeNearestNeighborOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateResizeNearestNeighborOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ResizeNearestNeighborOptions> CreateResizeNearestNeighborOptions(flatbuffers::FlatBufferBuilder &_fbb, const ResizeNearestNeighborOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ResizeNearestNeighborOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _align_corners = _o->align_corners;
-  auto _half_pixel_centers = _o->half_pixel_centers;
-  return tflite::CreateResizeNearestNeighborOptions(
-      _fbb,
-      _align_corners,
-      _half_pixel_centers);
-}
-
-inline CallOptionsT *CallOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<CallOptionsT>(new CallOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void CallOptions::UnPackTo(CallOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = subgraph(); _o->subgraph = _e; }
-}
-
-inline flatbuffers::Offset<CallOptions> CallOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateCallOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<CallOptions> CreateCallOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CallOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _subgraph = _o->subgraph;
-  return tflite::CreateCallOptions(
-      _fbb,
-      _subgraph);
-}
-
-inline PadOptionsT *PadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<PadOptionsT>(new PadOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void PadOptions::UnPackTo(PadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<PadOptions> PadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreatePadOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<PadOptions> CreatePadOptions(flatbuffers::FlatBufferBuilder &_fbb, const PadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreatePadOptions(
-      _fbb);
-}
-
-inline PadV2OptionsT *PadV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<PadV2OptionsT>(new PadV2OptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void PadV2Options::UnPackTo(PadV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<PadV2Options> PadV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreatePadV2Options(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<PadV2Options> CreatePadV2Options(flatbuffers::FlatBufferBuilder &_fbb, const PadV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PadV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreatePadV2Options(
-      _fbb);
-}
-
-inline ReshapeOptionsT *ReshapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ReshapeOptionsT>(new ReshapeOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ReshapeOptions::UnPackTo(ReshapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = new_shape(); if (_e) { _o->new_shape.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->new_shape[_i] = _e->Get(_i); } } }
-}
-
-inline flatbuffers::Offset<ReshapeOptions> ReshapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateReshapeOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ReshapeOptions> CreateReshapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReshapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReshapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _new_shape = _o->new_shape.size() ? _fbb.CreateVector(_o->new_shape) : 0;
-  return tflite::CreateReshapeOptions(
-      _fbb,
-      _new_shape);
-}
-
-inline SpaceToBatchNDOptionsT *SpaceToBatchNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SpaceToBatchNDOptionsT>(new SpaceToBatchNDOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SpaceToBatchNDOptions::UnPackTo(SpaceToBatchNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<SpaceToBatchNDOptions> SpaceToBatchNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSpaceToBatchNDOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SpaceToBatchNDOptions> CreateSpaceToBatchNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToBatchNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToBatchNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateSpaceToBatchNDOptions(
-      _fbb);
-}
-
-inline BatchToSpaceNDOptionsT *BatchToSpaceNDOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<BatchToSpaceNDOptionsT>(new BatchToSpaceNDOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void BatchToSpaceNDOptions::UnPackTo(BatchToSpaceNDOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<BatchToSpaceNDOptions> BatchToSpaceNDOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateBatchToSpaceNDOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<BatchToSpaceNDOptions> CreateBatchToSpaceNDOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchToSpaceNDOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchToSpaceNDOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateBatchToSpaceNDOptions(
-      _fbb);
-}
-
-inline SkipGramOptionsT *SkipGramOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SkipGramOptionsT>(new SkipGramOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SkipGramOptions::UnPackTo(SkipGramOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = ngram_size(); _o->ngram_size = _e; }
-  { auto _e = max_skip_size(); _o->max_skip_size = _e; }
-  { auto _e = include_all_ngrams(); _o->include_all_ngrams = _e; }
-}
-
-inline flatbuffers::Offset<SkipGramOptions> SkipGramOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSkipGramOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SkipGramOptions> CreateSkipGramOptions(flatbuffers::FlatBufferBuilder &_fbb, const SkipGramOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SkipGramOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _ngram_size = _o->ngram_size;
-  auto _max_skip_size = _o->max_skip_size;
-  auto _include_all_ngrams = _o->include_all_ngrams;
-  return tflite::CreateSkipGramOptions(
-      _fbb,
-      _ngram_size,
-      _max_skip_size,
-      _include_all_ngrams);
-}
-
-inline SpaceToDepthOptionsT *SpaceToDepthOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SpaceToDepthOptionsT>(new SpaceToDepthOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SpaceToDepthOptions::UnPackTo(SpaceToDepthOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = block_size(); _o->block_size = _e; }
-}
-
-inline flatbuffers::Offset<SpaceToDepthOptions> SpaceToDepthOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSpaceToDepthOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SpaceToDepthOptions> CreateSpaceToDepthOptions(flatbuffers::FlatBufferBuilder &_fbb, const SpaceToDepthOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SpaceToDepthOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _block_size = _o->block_size;
-  return tflite::CreateSpaceToDepthOptions(
-      _fbb,
-      _block_size);
-}
-
-inline DepthToSpaceOptionsT *DepthToSpaceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<DepthToSpaceOptionsT>(new DepthToSpaceOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void DepthToSpaceOptions::UnPackTo(DepthToSpaceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = block_size(); _o->block_size = _e; }
-}
-
-inline flatbuffers::Offset<DepthToSpaceOptions> DepthToSpaceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateDepthToSpaceOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<DepthToSpaceOptions> CreateDepthToSpaceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DepthToSpaceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DepthToSpaceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _block_size = _o->block_size;
-  return tflite::CreateDepthToSpaceOptions(
-      _fbb,
-      _block_size);
-}
-
-inline SubOptionsT *SubOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SubOptionsT>(new SubOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SubOptions::UnPackTo(SubOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-  { auto _e = pot_scale_int16(); _o->pot_scale_int16 = _e; }
-}
-
-inline flatbuffers::Offset<SubOptions> SubOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSubOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SubOptions> CreateSubOptions(flatbuffers::FlatBufferBuilder &_fbb, const SubOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _fused_activation_function = _o->fused_activation_function;
-  auto _pot_scale_int16 = _o->pot_scale_int16;
-  return tflite::CreateSubOptions(
-      _fbb,
-      _fused_activation_function,
-      _pot_scale_int16);
-}
-
-inline DivOptionsT *DivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<DivOptionsT>(new DivOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void DivOptions::UnPackTo(DivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = fused_activation_function(); _o->fused_activation_function = _e; }
-}
-
-inline flatbuffers::Offset<DivOptions> DivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateDivOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<DivOptions> CreateDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const DivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _fused_activation_function = _o->fused_activation_function;
-  return tflite::CreateDivOptions(
-      _fbb,
-      _fused_activation_function);
-}
-
-inline TopKV2OptionsT *TopKV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<TopKV2OptionsT>(new TopKV2OptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void TopKV2Options::UnPackTo(TopKV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<TopKV2Options> TopKV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateTopKV2Options(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<TopKV2Options> CreateTopKV2Options(flatbuffers::FlatBufferBuilder &_fbb, const TopKV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TopKV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateTopKV2Options(
-      _fbb);
-}
-
-inline EmbeddingLookupSparseOptionsT *EmbeddingLookupSparseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<EmbeddingLookupSparseOptionsT>(new EmbeddingLookupSparseOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void EmbeddingLookupSparseOptions::UnPackTo(EmbeddingLookupSparseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = combiner(); _o->combiner = _e; }
-}
-
-inline flatbuffers::Offset<EmbeddingLookupSparseOptions> EmbeddingLookupSparseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateEmbeddingLookupSparseOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<EmbeddingLookupSparseOptions> CreateEmbeddingLookupSparseOptions(flatbuffers::FlatBufferBuilder &_fbb, const EmbeddingLookupSparseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EmbeddingLookupSparseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _combiner = _o->combiner;
-  return tflite::CreateEmbeddingLookupSparseOptions(
-      _fbb,
-      _combiner);
-}
-
-inline GatherOptionsT *GatherOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<GatherOptionsT>(new GatherOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void GatherOptions::UnPackTo(GatherOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = axis(); _o->axis = _e; }
-  { auto _e = batch_dims(); _o->batch_dims = _e; }
-}
-
-inline flatbuffers::Offset<GatherOptions> GatherOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateGatherOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<GatherOptions> CreateGatherOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _axis = _o->axis;
-  auto _batch_dims = _o->batch_dims;
-  return tflite::CreateGatherOptions(
-      _fbb,
-      _axis,
-      _batch_dims);
-}
-
-inline TransposeOptionsT *TransposeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<TransposeOptionsT>(new TransposeOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void TransposeOptions::UnPackTo(TransposeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<TransposeOptions> TransposeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateTransposeOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<TransposeOptions> CreateTransposeOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateTransposeOptions(
-      _fbb);
-}
-
-inline ExpOptionsT *ExpOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ExpOptionsT>(new ExpOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ExpOptions::UnPackTo(ExpOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<ExpOptions> ExpOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateExpOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ExpOptions> CreateExpOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateExpOptions(
-      _fbb);
-}
-
-inline CosOptionsT *CosOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<CosOptionsT>(new CosOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void CosOptions::UnPackTo(CosOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<CosOptions> CosOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateCosOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<CosOptions> CreateCosOptions(flatbuffers::FlatBufferBuilder &_fbb, const CosOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CosOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateCosOptions(
-      _fbb);
-}
-
-inline ReducerOptionsT *ReducerOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ReducerOptionsT>(new ReducerOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ReducerOptions::UnPackTo(ReducerOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = keep_dims(); _o->keep_dims = _e; }
-}
-
-inline flatbuffers::Offset<ReducerOptions> ReducerOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateReducerOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ReducerOptions> CreateReducerOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReducerOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReducerOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _keep_dims = _o->keep_dims;
-  return tflite::CreateReducerOptions(
-      _fbb,
-      _keep_dims);
-}
-
-inline SqueezeOptionsT *SqueezeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SqueezeOptionsT>(new SqueezeOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SqueezeOptions::UnPackTo(SqueezeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = squeeze_dims(); if (_e) { _o->squeeze_dims.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->squeeze_dims[_i] = _e->Get(_i); } } }
-}
-
-inline flatbuffers::Offset<SqueezeOptions> SqueezeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSqueezeOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SqueezeOptions> CreateSqueezeOptions(flatbuffers::FlatBufferBuilder &_fbb, const SqueezeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SqueezeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _squeeze_dims = _o->squeeze_dims.size() ? _fbb.CreateVector(_o->squeeze_dims) : 0;
-  return tflite::CreateSqueezeOptions(
-      _fbb,
-      _squeeze_dims);
-}
-
-inline SplitOptionsT *SplitOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SplitOptionsT>(new SplitOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SplitOptions::UnPackTo(SplitOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = num_splits(); _o->num_splits = _e; }
-}
-
-inline flatbuffers::Offset<SplitOptions> SplitOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSplitOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SplitOptions> CreateSplitOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _num_splits = _o->num_splits;
-  return tflite::CreateSplitOptions(
-      _fbb,
-      _num_splits);
-}
-
-inline SplitVOptionsT *SplitVOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SplitVOptionsT>(new SplitVOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SplitVOptions::UnPackTo(SplitVOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = num_splits(); _o->num_splits = _e; }
-}
-
-inline flatbuffers::Offset<SplitVOptions> SplitVOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSplitVOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SplitVOptions> CreateSplitVOptions(flatbuffers::FlatBufferBuilder &_fbb, const SplitVOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SplitVOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _num_splits = _o->num_splits;
-  return tflite::CreateSplitVOptions(
-      _fbb,
-      _num_splits);
-}
-
-inline StridedSliceOptionsT *StridedSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<StridedSliceOptionsT>(new StridedSliceOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void StridedSliceOptions::UnPackTo(StridedSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = begin_mask(); _o->begin_mask = _e; }
-  { auto _e = end_mask(); _o->end_mask = _e; }
-  { auto _e = ellipsis_mask(); _o->ellipsis_mask = _e; }
-  { auto _e = new_axis_mask(); _o->new_axis_mask = _e; }
-  { auto _e = shrink_axis_mask(); _o->shrink_axis_mask = _e; }
-}
-
-inline flatbuffers::Offset<StridedSliceOptions> StridedSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateStridedSliceOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<StridedSliceOptions> CreateStridedSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const StridedSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const StridedSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _begin_mask = _o->begin_mask;
-  auto _end_mask = _o->end_mask;
-  auto _ellipsis_mask = _o->ellipsis_mask;
-  auto _new_axis_mask = _o->new_axis_mask;
-  auto _shrink_axis_mask = _o->shrink_axis_mask;
-  return tflite::CreateStridedSliceOptions(
-      _fbb,
-      _begin_mask,
-      _end_mask,
-      _ellipsis_mask,
-      _new_axis_mask,
-      _shrink_axis_mask);
-}
-
-inline LogSoftmaxOptionsT *LogSoftmaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<LogSoftmaxOptionsT>(new LogSoftmaxOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void LogSoftmaxOptions::UnPackTo(LogSoftmaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<LogSoftmaxOptions> LogSoftmaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateLogSoftmaxOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<LogSoftmaxOptions> CreateLogSoftmaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogSoftmaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogSoftmaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateLogSoftmaxOptions(
-      _fbb);
-}
-
-inline CastOptionsT *CastOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<CastOptionsT>(new CastOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void CastOptions::UnPackTo(CastOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = in_data_type(); _o->in_data_type = _e; }
-  { auto _e = out_data_type(); _o->out_data_type = _e; }
-}
-
-inline flatbuffers::Offset<CastOptions> CastOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateCastOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<CastOptions> CreateCastOptions(flatbuffers::FlatBufferBuilder &_fbb, const CastOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CastOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _in_data_type = _o->in_data_type;
-  auto _out_data_type = _o->out_data_type;
-  return tflite::CreateCastOptions(
-      _fbb,
-      _in_data_type,
-      _out_data_type);
-}
-
-inline DequantizeOptionsT *DequantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<DequantizeOptionsT>(new DequantizeOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void DequantizeOptions::UnPackTo(DequantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<DequantizeOptions> DequantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateDequantizeOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<DequantizeOptions> CreateDequantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const DequantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DequantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateDequantizeOptions(
-      _fbb);
-}
-
-inline MaximumMinimumOptionsT *MaximumMinimumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<MaximumMinimumOptionsT>(new MaximumMinimumOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void MaximumMinimumOptions::UnPackTo(MaximumMinimumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<MaximumMinimumOptions> MaximumMinimumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateMaximumMinimumOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<MaximumMinimumOptions> CreateMaximumMinimumOptions(flatbuffers::FlatBufferBuilder &_fbb, const MaximumMinimumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MaximumMinimumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateMaximumMinimumOptions(
-      _fbb);
-}
-
-inline TileOptionsT *TileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<TileOptionsT>(new TileOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void TileOptions::UnPackTo(TileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<TileOptions> TileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateTileOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<TileOptions> CreateTileOptions(flatbuffers::FlatBufferBuilder &_fbb, const TileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateTileOptions(
-      _fbb);
-}
-
-inline ArgMaxOptionsT *ArgMaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ArgMaxOptionsT>(new ArgMaxOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ArgMaxOptions::UnPackTo(ArgMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = output_type(); _o->output_type = _e; }
-}
-
-inline flatbuffers::Offset<ArgMaxOptions> ArgMaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateArgMaxOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ArgMaxOptions> CreateArgMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _output_type = _o->output_type;
-  return tflite::CreateArgMaxOptions(
-      _fbb,
-      _output_type);
-}
-
-inline ArgMinOptionsT *ArgMinOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ArgMinOptionsT>(new ArgMinOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ArgMinOptions::UnPackTo(ArgMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = output_type(); _o->output_type = _e; }
-}
-
-inline flatbuffers::Offset<ArgMinOptions> ArgMinOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateArgMinOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ArgMinOptions> CreateArgMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const ArgMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ArgMinOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _output_type = _o->output_type;
-  return tflite::CreateArgMinOptions(
-      _fbb,
-      _output_type);
-}
-
-inline GreaterOptionsT *GreaterOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<GreaterOptionsT>(new GreaterOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void GreaterOptions::UnPackTo(GreaterOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<GreaterOptions> GreaterOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateGreaterOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<GreaterOptions> CreateGreaterOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateGreaterOptions(
-      _fbb);
-}
-
-inline GreaterEqualOptionsT *GreaterEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<GreaterEqualOptionsT>(new GreaterEqualOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void GreaterEqualOptions::UnPackTo(GreaterEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<GreaterEqualOptions> GreaterEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateGreaterEqualOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<GreaterEqualOptions> CreateGreaterEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const GreaterEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GreaterEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateGreaterEqualOptions(
-      _fbb);
-}
-
-inline LessOptionsT *LessOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<LessOptionsT>(new LessOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void LessOptions::UnPackTo(LessOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<LessOptions> LessOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateLessOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<LessOptions> CreateLessOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateLessOptions(
-      _fbb);
-}
-
-inline LessEqualOptionsT *LessEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<LessEqualOptionsT>(new LessEqualOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void LessEqualOptions::UnPackTo(LessEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<LessEqualOptions> LessEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateLessEqualOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<LessEqualOptions> CreateLessEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const LessEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LessEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateLessEqualOptions(
-      _fbb);
-}
-
-inline NegOptionsT *NegOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<NegOptionsT>(new NegOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void NegOptions::UnPackTo(NegOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<NegOptions> NegOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateNegOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<NegOptions> CreateNegOptions(flatbuffers::FlatBufferBuilder &_fbb, const NegOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NegOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateNegOptions(
-      _fbb);
-}
-
-inline SelectOptionsT *SelectOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SelectOptionsT>(new SelectOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SelectOptions::UnPackTo(SelectOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<SelectOptions> SelectOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSelectOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SelectOptions> CreateSelectOptions(flatbuffers::FlatBufferBuilder &_fbb, const SelectOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateSelectOptions(
-      _fbb);
-}
-
-inline SliceOptionsT *SliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SliceOptionsT>(new SliceOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SliceOptions::UnPackTo(SliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<SliceOptions> SliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSliceOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SliceOptions> CreateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateSliceOptions(
-      _fbb);
-}
-
-inline TransposeConvOptionsT *TransposeConvOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<TransposeConvOptionsT>(new TransposeConvOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void TransposeConvOptions::UnPackTo(TransposeConvOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = padding(); _o->padding = _e; }
-  { auto _e = stride_w(); _o->stride_w = _e; }
-  { auto _e = stride_h(); _o->stride_h = _e; }
-}
-
-inline flatbuffers::Offset<TransposeConvOptions> TransposeConvOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateTransposeConvOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<TransposeConvOptions> CreateTransposeConvOptions(flatbuffers::FlatBufferBuilder &_fbb, const TransposeConvOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TransposeConvOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _padding = _o->padding;
-  auto _stride_w = _o->stride_w;
-  auto _stride_h = _o->stride_h;
-  return tflite::CreateTransposeConvOptions(
-      _fbb,
-      _padding,
-      _stride_w,
-      _stride_h);
-}
-
-inline ExpandDimsOptionsT *ExpandDimsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ExpandDimsOptionsT>(new ExpandDimsOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ExpandDimsOptions::UnPackTo(ExpandDimsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<ExpandDimsOptions> ExpandDimsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateExpandDimsOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ExpandDimsOptions> CreateExpandDimsOptions(flatbuffers::FlatBufferBuilder &_fbb, const ExpandDimsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ExpandDimsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateExpandDimsOptions(
-      _fbb);
-}
-
-inline SparseToDenseOptionsT *SparseToDenseOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SparseToDenseOptionsT>(new SparseToDenseOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SparseToDenseOptions::UnPackTo(SparseToDenseOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = validate_indices(); _o->validate_indices = _e; }
-}
-
-inline flatbuffers::Offset<SparseToDenseOptions> SparseToDenseOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSparseToDenseOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SparseToDenseOptions> CreateSparseToDenseOptions(flatbuffers::FlatBufferBuilder &_fbb, const SparseToDenseOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SparseToDenseOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _validate_indices = _o->validate_indices;
-  return tflite::CreateSparseToDenseOptions(
-      _fbb,
-      _validate_indices);
-}
-
-inline EqualOptionsT *EqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<EqualOptionsT>(new EqualOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void EqualOptions::UnPackTo(EqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<EqualOptions> EqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateEqualOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<EqualOptions> CreateEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const EqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const EqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateEqualOptions(
-      _fbb);
-}
-
-inline NotEqualOptionsT *NotEqualOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<NotEqualOptionsT>(new NotEqualOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void NotEqualOptions::UnPackTo(NotEqualOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<NotEqualOptions> NotEqualOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateNotEqualOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<NotEqualOptions> CreateNotEqualOptions(flatbuffers::FlatBufferBuilder &_fbb, const NotEqualOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NotEqualOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateNotEqualOptions(
-      _fbb);
-}
-
-inline ShapeOptionsT *ShapeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ShapeOptionsT>(new ShapeOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ShapeOptions::UnPackTo(ShapeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = out_type(); _o->out_type = _e; }
-}
-
-inline flatbuffers::Offset<ShapeOptions> ShapeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateShapeOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ShapeOptions> CreateShapeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ShapeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ShapeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _out_type = _o->out_type;
-  return tflite::CreateShapeOptions(
-      _fbb,
-      _out_type);
-}
-
-inline RankOptionsT *RankOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<RankOptionsT>(new RankOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void RankOptions::UnPackTo(RankOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<RankOptions> RankOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateRankOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<RankOptions> CreateRankOptions(flatbuffers::FlatBufferBuilder &_fbb, const RankOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RankOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateRankOptions(
-      _fbb);
-}
-
-inline PowOptionsT *PowOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<PowOptionsT>(new PowOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void PowOptions::UnPackTo(PowOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<PowOptions> PowOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreatePowOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<PowOptions> CreatePowOptions(flatbuffers::FlatBufferBuilder &_fbb, const PowOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PowOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreatePowOptions(
-      _fbb);
-}
-
-inline FakeQuantOptionsT *FakeQuantOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<FakeQuantOptionsT>(new FakeQuantOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void FakeQuantOptions::UnPackTo(FakeQuantOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = min(); _o->min = _e; }
-  { auto _e = max(); _o->max = _e; }
-  { auto _e = num_bits(); _o->num_bits = _e; }
-  { auto _e = narrow_range(); _o->narrow_range = _e; }
-}
-
-inline flatbuffers::Offset<FakeQuantOptions> FakeQuantOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateFakeQuantOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<FakeQuantOptions> CreateFakeQuantOptions(flatbuffers::FlatBufferBuilder &_fbb, const FakeQuantOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FakeQuantOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _min = _o->min;
-  auto _max = _o->max;
-  auto _num_bits = _o->num_bits;
-  auto _narrow_range = _o->narrow_range;
-  return tflite::CreateFakeQuantOptions(
-      _fbb,
-      _min,
-      _max,
-      _num_bits,
-      _narrow_range);
-}
-
-inline PackOptionsT *PackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<PackOptionsT>(new PackOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void PackOptions::UnPackTo(PackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = values_count(); _o->values_count = _e; }
-  { auto _e = axis(); _o->axis = _e; }
-}
-
-inline flatbuffers::Offset<PackOptions> PackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreatePackOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<PackOptions> CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const PackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _values_count = _o->values_count;
-  auto _axis = _o->axis;
-  return tflite::CreatePackOptions(
-      _fbb,
-      _values_count,
-      _axis);
-}
-
-inline LogicalOrOptionsT *LogicalOrOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<LogicalOrOptionsT>(new LogicalOrOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void LogicalOrOptions::UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<LogicalOrOptions> LogicalOrOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateLogicalOrOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<LogicalOrOptions> CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalOrOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateLogicalOrOptions(
-      _fbb);
-}
-
-inline OneHotOptionsT *OneHotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<OneHotOptionsT>(new OneHotOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void OneHotOptions::UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = axis(); _o->axis = _e; }
-}
-
-inline flatbuffers::Offset<OneHotOptions> OneHotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateOneHotOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<OneHotOptions> CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OneHotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _axis = _o->axis;
-  return tflite::CreateOneHotOptions(
-      _fbb,
-      _axis);
-}
-
-inline AbsOptionsT *AbsOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<AbsOptionsT>(new AbsOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void AbsOptions::UnPackTo(AbsOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<AbsOptions> AbsOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateAbsOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<AbsOptions> CreateAbsOptions(flatbuffers::FlatBufferBuilder &_fbb, const AbsOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AbsOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateAbsOptions(
-      _fbb);
-}
-
-inline HardSwishOptionsT *HardSwishOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<HardSwishOptionsT>(new HardSwishOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void HardSwishOptions::UnPackTo(HardSwishOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<HardSwishOptions> HardSwishOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateHardSwishOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<HardSwishOptions> CreateHardSwishOptions(flatbuffers::FlatBufferBuilder &_fbb, const HardSwishOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HardSwishOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateHardSwishOptions(
-      _fbb);
-}
-
-inline LogicalAndOptionsT *LogicalAndOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<LogicalAndOptionsT>(new LogicalAndOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void LogicalAndOptions::UnPackTo(LogicalAndOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<LogicalAndOptions> LogicalAndOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateLogicalAndOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<LogicalAndOptions> CreateLogicalAndOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalAndOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalAndOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateLogicalAndOptions(
-      _fbb);
-}
-
-inline LogicalNotOptionsT *LogicalNotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<LogicalNotOptionsT>(new LogicalNotOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void LogicalNotOptions::UnPackTo(LogicalNotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<LogicalNotOptions> LogicalNotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateLogicalNotOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<LogicalNotOptions> CreateLogicalNotOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalNotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalNotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateLogicalNotOptions(
-      _fbb);
-}
-
-inline UnpackOptionsT *UnpackOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<UnpackOptionsT>(new UnpackOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void UnpackOptions::UnPackTo(UnpackOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = num(); _o->num = _e; }
-  { auto _e = axis(); _o->axis = _e; }
-}
-
-inline flatbuffers::Offset<UnpackOptions> UnpackOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateUnpackOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<UnpackOptions> CreateUnpackOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnpackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnpackOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _num = _o->num;
-  auto _axis = _o->axis;
-  return tflite::CreateUnpackOptions(
-      _fbb,
-      _num,
-      _axis);
-}
-
-inline FloorDivOptionsT *FloorDivOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<FloorDivOptionsT>(new FloorDivOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void FloorDivOptions::UnPackTo(FloorDivOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<FloorDivOptions> FloorDivOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateFloorDivOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<FloorDivOptions> CreateFloorDivOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorDivOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FloorDivOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateFloorDivOptions(
-      _fbb);
-}
-
-inline SquareOptionsT *SquareOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SquareOptionsT>(new SquareOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SquareOptions::UnPackTo(SquareOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<SquareOptions> SquareOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSquareOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SquareOptions> CreateSquareOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquareOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SquareOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateSquareOptions(
-      _fbb);
-}
-
-inline ZerosLikeOptionsT *ZerosLikeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ZerosLikeOptionsT>(new ZerosLikeOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ZerosLikeOptions::UnPackTo(ZerosLikeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<ZerosLikeOptions> ZerosLikeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateZerosLikeOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ZerosLikeOptions> CreateZerosLikeOptions(flatbuffers::FlatBufferBuilder &_fbb, const ZerosLikeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ZerosLikeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateZerosLikeOptions(
-      _fbb);
-}
-
-inline FillOptionsT *FillOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<FillOptionsT>(new FillOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void FillOptions::UnPackTo(FillOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<FillOptions> FillOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateFillOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<FillOptions> CreateFillOptions(flatbuffers::FlatBufferBuilder &_fbb, const FillOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FillOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateFillOptions(
-      _fbb);
-}
-
-inline FloorModOptionsT *FloorModOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<FloorModOptionsT>(new FloorModOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void FloorModOptions::UnPackTo(FloorModOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<FloorModOptions> FloorModOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateFloorModOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<FloorModOptions> CreateFloorModOptions(flatbuffers::FlatBufferBuilder &_fbb, const FloorModOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const FloorModOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateFloorModOptions(
-      _fbb);
-}
-
-inline RangeOptionsT *RangeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<RangeOptionsT>(new RangeOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void RangeOptions::UnPackTo(RangeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<RangeOptions> RangeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateRangeOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<RangeOptions> CreateRangeOptions(flatbuffers::FlatBufferBuilder &_fbb, const RangeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RangeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateRangeOptions(
-      _fbb);
-}
-
-inline LeakyReluOptionsT *LeakyReluOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<LeakyReluOptionsT>(new LeakyReluOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void LeakyReluOptions::UnPackTo(LeakyReluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = alpha(); _o->alpha = _e; }
-}
-
-inline flatbuffers::Offset<LeakyReluOptions> LeakyReluOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateLeakyReluOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<LeakyReluOptions> CreateLeakyReluOptions(flatbuffers::FlatBufferBuilder &_fbb, const LeakyReluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LeakyReluOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _alpha = _o->alpha;
-  return tflite::CreateLeakyReluOptions(
-      _fbb,
-      _alpha);
-}
-
-inline SquaredDifferenceOptionsT *SquaredDifferenceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SquaredDifferenceOptionsT>(new SquaredDifferenceOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SquaredDifferenceOptions::UnPackTo(SquaredDifferenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<SquaredDifferenceOptions> SquaredDifferenceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSquaredDifferenceOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SquaredDifferenceOptions> CreateSquaredDifferenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const SquaredDifferenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SquaredDifferenceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateSquaredDifferenceOptions(
-      _fbb);
-}
-
-inline MirrorPadOptionsT *MirrorPadOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<MirrorPadOptionsT>(new MirrorPadOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void MirrorPadOptions::UnPackTo(MirrorPadOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = mode(); _o->mode = _e; }
-}
-
-inline flatbuffers::Offset<MirrorPadOptions> MirrorPadOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateMirrorPadOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<MirrorPadOptions> CreateMirrorPadOptions(flatbuffers::FlatBufferBuilder &_fbb, const MirrorPadOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MirrorPadOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _mode = _o->mode;
-  return tflite::CreateMirrorPadOptions(
-      _fbb,
-      _mode);
-}
-
-inline UniqueOptionsT *UniqueOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<UniqueOptionsT>(new UniqueOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void UniqueOptions::UnPackTo(UniqueOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = idx_out_type(); _o->idx_out_type = _e; }
-}
-
-inline flatbuffers::Offset<UniqueOptions> UniqueOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateUniqueOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<UniqueOptions> CreateUniqueOptions(flatbuffers::FlatBufferBuilder &_fbb, const UniqueOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UniqueOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _idx_out_type = _o->idx_out_type;
-  return tflite::CreateUniqueOptions(
-      _fbb,
-      _idx_out_type);
-}
-
-inline ReverseV2OptionsT *ReverseV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ReverseV2OptionsT>(new ReverseV2OptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ReverseV2Options::UnPackTo(ReverseV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<ReverseV2Options> ReverseV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateReverseV2Options(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ReverseV2Options> CreateReverseV2Options(flatbuffers::FlatBufferBuilder &_fbb, const ReverseV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReverseV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateReverseV2Options(
-      _fbb);
-}
-
-inline AddNOptionsT *AddNOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<AddNOptionsT>(new AddNOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void AddNOptions::UnPackTo(AddNOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<AddNOptions> AddNOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateAddNOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<AddNOptions> CreateAddNOptions(flatbuffers::FlatBufferBuilder &_fbb, const AddNOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AddNOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateAddNOptions(
-      _fbb);
-}
-
-inline GatherNdOptionsT *GatherNdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<GatherNdOptionsT>(new GatherNdOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void GatherNdOptions::UnPackTo(GatherNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<GatherNdOptions> GatherNdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateGatherNdOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<GatherNdOptions> CreateGatherNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const GatherNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GatherNdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateGatherNdOptions(
-      _fbb);
-}
-
-inline WhereOptionsT *WhereOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<WhereOptionsT>(new WhereOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void WhereOptions::UnPackTo(WhereOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<WhereOptions> WhereOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateWhereOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<WhereOptions> CreateWhereOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhereOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WhereOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateWhereOptions(
-      _fbb);
-}
-
-inline ReverseSequenceOptionsT *ReverseSequenceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ReverseSequenceOptionsT>(new ReverseSequenceOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ReverseSequenceOptions::UnPackTo(ReverseSequenceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = seq_dim(); _o->seq_dim = _e; }
-  { auto _e = batch_dim(); _o->batch_dim = _e; }
-}
-
-inline flatbuffers::Offset<ReverseSequenceOptions> ReverseSequenceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateReverseSequenceOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ReverseSequenceOptions> CreateReverseSequenceOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReverseSequenceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReverseSequenceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _seq_dim = _o->seq_dim;
-  auto _batch_dim = _o->batch_dim;
-  return tflite::CreateReverseSequenceOptions(
-      _fbb,
-      _seq_dim,
-      _batch_dim);
-}
-
-inline MatrixDiagOptionsT *MatrixDiagOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<MatrixDiagOptionsT>(new MatrixDiagOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void MatrixDiagOptions::UnPackTo(MatrixDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<MatrixDiagOptions> MatrixDiagOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateMatrixDiagOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<MatrixDiagOptions> CreateMatrixDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MatrixDiagOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateMatrixDiagOptions(
-      _fbb);
-}
-
-inline QuantizeOptionsT *QuantizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<QuantizeOptionsT>(new QuantizeOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void QuantizeOptions::UnPackTo(QuantizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<QuantizeOptions> QuantizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateQuantizeOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<QuantizeOptions> CreateQuantizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const QuantizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const QuantizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateQuantizeOptions(
-      _fbb);
-}
-
-inline MatrixSetDiagOptionsT *MatrixSetDiagOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<MatrixSetDiagOptionsT>(new MatrixSetDiagOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void MatrixSetDiagOptions::UnPackTo(MatrixSetDiagOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<MatrixSetDiagOptions> MatrixSetDiagOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateMatrixSetDiagOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<MatrixSetDiagOptions> CreateMatrixSetDiagOptions(flatbuffers::FlatBufferBuilder &_fbb, const MatrixSetDiagOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MatrixSetDiagOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateMatrixSetDiagOptions(
-      _fbb);
-}
-
-inline IfOptionsT *IfOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<IfOptionsT>(new IfOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void IfOptions::UnPackTo(IfOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = then_subgraph_index(); _o->then_subgraph_index = _e; }
-  { auto _e = else_subgraph_index(); _o->else_subgraph_index = _e; }
-}
-
-inline flatbuffers::Offset<IfOptions> IfOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateIfOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<IfOptions> CreateIfOptions(flatbuffers::FlatBufferBuilder &_fbb, const IfOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const IfOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _then_subgraph_index = _o->then_subgraph_index;
-  auto _else_subgraph_index = _o->else_subgraph_index;
-  return tflite::CreateIfOptions(
-      _fbb,
-      _then_subgraph_index,
-      _else_subgraph_index);
-}
-
-inline CallOnceOptionsT *CallOnceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<CallOnceOptionsT>(new CallOnceOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void CallOnceOptions::UnPackTo(CallOnceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = init_subgraph_index(); _o->init_subgraph_index = _e; }
-}
-
-inline flatbuffers::Offset<CallOnceOptions> CallOnceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateCallOnceOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<CallOnceOptions> CreateCallOnceOptions(flatbuffers::FlatBufferBuilder &_fbb, const CallOnceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CallOnceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _init_subgraph_index = _o->init_subgraph_index;
-  return tflite::CreateCallOnceOptions(
-      _fbb,
-      _init_subgraph_index);
-}
-
-inline WhileOptionsT *WhileOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<WhileOptionsT>(new WhileOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void WhileOptions::UnPackTo(WhileOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = cond_subgraph_index(); _o->cond_subgraph_index = _e; }
-  { auto _e = body_subgraph_index(); _o->body_subgraph_index = _e; }
-}
-
-inline flatbuffers::Offset<WhileOptions> WhileOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateWhileOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<WhileOptions> CreateWhileOptions(flatbuffers::FlatBufferBuilder &_fbb, const WhileOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const WhileOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _cond_subgraph_index = _o->cond_subgraph_index;
-  auto _body_subgraph_index = _o->body_subgraph_index;
-  return tflite::CreateWhileOptions(
-      _fbb,
-      _cond_subgraph_index,
-      _body_subgraph_index);
-}
-
-inline NonMaxSuppressionV4OptionsT *NonMaxSuppressionV4Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<NonMaxSuppressionV4OptionsT>(new NonMaxSuppressionV4OptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void NonMaxSuppressionV4Options::UnPackTo(NonMaxSuppressionV4OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<NonMaxSuppressionV4Options> NonMaxSuppressionV4Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateNonMaxSuppressionV4Options(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<NonMaxSuppressionV4Options> CreateNonMaxSuppressionV4Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV4OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV4OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateNonMaxSuppressionV4Options(
-      _fbb);
-}
-
-inline NonMaxSuppressionV5OptionsT *NonMaxSuppressionV5Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<NonMaxSuppressionV5OptionsT>(new NonMaxSuppressionV5OptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void NonMaxSuppressionV5Options::UnPackTo(NonMaxSuppressionV5OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<NonMaxSuppressionV5Options> NonMaxSuppressionV5Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateNonMaxSuppressionV5Options(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<NonMaxSuppressionV5Options> CreateNonMaxSuppressionV5Options(flatbuffers::FlatBufferBuilder &_fbb, const NonMaxSuppressionV5OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const NonMaxSuppressionV5OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateNonMaxSuppressionV5Options(
-      _fbb);
-}
-
-inline ScatterNdOptionsT *ScatterNdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ScatterNdOptionsT>(new ScatterNdOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ScatterNdOptions::UnPackTo(ScatterNdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<ScatterNdOptions> ScatterNdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateScatterNdOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ScatterNdOptions> CreateScatterNdOptions(flatbuffers::FlatBufferBuilder &_fbb, const ScatterNdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ScatterNdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateScatterNdOptions(
-      _fbb);
-}
-
-inline SelectV2OptionsT *SelectV2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SelectV2OptionsT>(new SelectV2OptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SelectV2Options::UnPackTo(SelectV2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<SelectV2Options> SelectV2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSelectV2Options(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SelectV2Options> CreateSelectV2Options(flatbuffers::FlatBufferBuilder &_fbb, const SelectV2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SelectV2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateSelectV2Options(
-      _fbb);
-}
-
-inline DensifyOptionsT *DensifyOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<DensifyOptionsT>(new DensifyOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void DensifyOptions::UnPackTo(DensifyOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<DensifyOptions> DensifyOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateDensifyOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<DensifyOptions> CreateDensifyOptions(flatbuffers::FlatBufferBuilder &_fbb, const DensifyOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DensifyOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateDensifyOptions(
-      _fbb);
-}
-
-inline SegmentSumOptionsT *SegmentSumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SegmentSumOptionsT>(new SegmentSumOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SegmentSumOptions::UnPackTo(SegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<SegmentSumOptions> SegmentSumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSegmentSumOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SegmentSumOptions> CreateSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const SegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SegmentSumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateSegmentSumOptions(
-      _fbb);
-}
-
-inline BatchMatMulOptionsT *BatchMatMulOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<BatchMatMulOptionsT>(new BatchMatMulOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void BatchMatMulOptions::UnPackTo(BatchMatMulOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = adj_x(); _o->adj_x = _e; }
-  { auto _e = adj_y(); _o->adj_y = _e; }
-  { auto _e = asymmetric_quantize_inputs(); _o->asymmetric_quantize_inputs = _e; }
-}
-
-inline flatbuffers::Offset<BatchMatMulOptions> BatchMatMulOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateBatchMatMulOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<BatchMatMulOptions> CreateBatchMatMulOptions(flatbuffers::FlatBufferBuilder &_fbb, const BatchMatMulOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BatchMatMulOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _adj_x = _o->adj_x;
-  auto _adj_y = _o->adj_y;
-  auto _asymmetric_quantize_inputs = _o->asymmetric_quantize_inputs;
-  return tflite::CreateBatchMatMulOptions(
-      _fbb,
-      _adj_x,
-      _adj_y,
-      _asymmetric_quantize_inputs);
-}
-
-inline CumsumOptionsT *CumsumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<CumsumOptionsT>(new CumsumOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void CumsumOptions::UnPackTo(CumsumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = exclusive(); _o->exclusive = _e; }
-  { auto _e = reverse(); _o->reverse = _e; }
-}
-
-inline flatbuffers::Offset<CumsumOptions> CumsumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateCumsumOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<CumsumOptions> CreateCumsumOptions(flatbuffers::FlatBufferBuilder &_fbb, const CumsumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const CumsumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _exclusive = _o->exclusive;
-  auto _reverse = _o->reverse;
-  return tflite::CreateCumsumOptions(
-      _fbb,
-      _exclusive,
-      _reverse);
-}
-
-inline BroadcastToOptionsT *BroadcastToOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<BroadcastToOptionsT>(new BroadcastToOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void BroadcastToOptions::UnPackTo(BroadcastToOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<BroadcastToOptions> BroadcastToOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateBroadcastToOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<BroadcastToOptions> CreateBroadcastToOptions(flatbuffers::FlatBufferBuilder &_fbb, const BroadcastToOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BroadcastToOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateBroadcastToOptions(
-      _fbb);
-}
-
-inline Rfft2dOptionsT *Rfft2dOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<Rfft2dOptionsT>(new Rfft2dOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void Rfft2dOptions::UnPackTo(Rfft2dOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<Rfft2dOptions> Rfft2dOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateRfft2dOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<Rfft2dOptions> CreateRfft2dOptions(flatbuffers::FlatBufferBuilder &_fbb, const Rfft2dOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const Rfft2dOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateRfft2dOptions(
-      _fbb);
-}
-
-inline HashtableOptionsT *HashtableOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<HashtableOptionsT>(new HashtableOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void HashtableOptions::UnPackTo(HashtableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = table_id(); _o->table_id = _e; }
-  { auto _e = key_dtype(); _o->key_dtype = _e; }
-  { auto _e = value_dtype(); _o->value_dtype = _e; }
-}
-
-inline flatbuffers::Offset<HashtableOptions> HashtableOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateHashtableOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<HashtableOptions> CreateHashtableOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _table_id = _o->table_id;
-  auto _key_dtype = _o->key_dtype;
-  auto _value_dtype = _o->value_dtype;
-  return tflite::CreateHashtableOptions(
-      _fbb,
-      _table_id,
-      _key_dtype,
-      _value_dtype);
-}
-
-inline HashtableFindOptionsT *HashtableFindOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<HashtableFindOptionsT>(new HashtableFindOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void HashtableFindOptions::UnPackTo(HashtableFindOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<HashtableFindOptions> HashtableFindOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateHashtableFindOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<HashtableFindOptions> CreateHashtableFindOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableFindOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableFindOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateHashtableFindOptions(
-      _fbb);
-}
-
-inline HashtableImportOptionsT *HashtableImportOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<HashtableImportOptionsT>(new HashtableImportOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void HashtableImportOptions::UnPackTo(HashtableImportOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<HashtableImportOptions> HashtableImportOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateHashtableImportOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<HashtableImportOptions> CreateHashtableImportOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableImportOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableImportOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateHashtableImportOptions(
-      _fbb);
-}
-
-inline HashtableSizeOptionsT *HashtableSizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<HashtableSizeOptionsT>(new HashtableSizeOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void HashtableSizeOptions::UnPackTo(HashtableSizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<HashtableSizeOptions> HashtableSizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateHashtableSizeOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<HashtableSizeOptions> CreateHashtableSizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const HashtableSizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const HashtableSizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateHashtableSizeOptions(
-      _fbb);
-}
-
-inline VarHandleOptionsT *VarHandleOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<VarHandleOptionsT>(new VarHandleOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void VarHandleOptions::UnPackTo(VarHandleOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = container(); if (_e) _o->container = _e->str(); }
-  { auto _e = shared_name(); if (_e) _o->shared_name = _e->str(); }
-}
-
-inline flatbuffers::Offset<VarHandleOptions> VarHandleOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateVarHandleOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<VarHandleOptions> CreateVarHandleOptions(flatbuffers::FlatBufferBuilder &_fbb, const VarHandleOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const VarHandleOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _container = _o->container.empty() ? 0 : _fbb.CreateString(_o->container);
-  auto _shared_name = _o->shared_name.empty() ? 0 : _fbb.CreateString(_o->shared_name);
-  return tflite::CreateVarHandleOptions(
-      _fbb,
-      _container,
-      _shared_name);
-}
-
-inline ReadVariableOptionsT *ReadVariableOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ReadVariableOptionsT>(new ReadVariableOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ReadVariableOptions::UnPackTo(ReadVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<ReadVariableOptions> ReadVariableOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateReadVariableOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ReadVariableOptions> CreateReadVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const ReadVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ReadVariableOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateReadVariableOptions(
-      _fbb);
-}
-
-inline AssignVariableOptionsT *AssignVariableOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<AssignVariableOptionsT>(new AssignVariableOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void AssignVariableOptions::UnPackTo(AssignVariableOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<AssignVariableOptions> AssignVariableOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateAssignVariableOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<AssignVariableOptions> CreateAssignVariableOptions(flatbuffers::FlatBufferBuilder &_fbb, const AssignVariableOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const AssignVariableOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateAssignVariableOptions(
-      _fbb);
-}
-
-inline RandomOptionsT *RandomOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<RandomOptionsT>(new RandomOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void RandomOptions::UnPackTo(RandomOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = seed(); _o->seed = _e; }
-  { auto _e = seed2(); _o->seed2 = _e; }
-}
-
-inline flatbuffers::Offset<RandomOptions> RandomOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateRandomOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<RandomOptions> CreateRandomOptions(flatbuffers::FlatBufferBuilder &_fbb, const RandomOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const RandomOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _seed = _o->seed;
-  auto _seed2 = _o->seed2;
-  return tflite::CreateRandomOptions(
-      _fbb,
-      _seed,
-      _seed2);
-}
-
-inline BucketizeOptionsT *BucketizeOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<BucketizeOptionsT>(new BucketizeOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void BucketizeOptions::UnPackTo(BucketizeOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = boundaries(); if (_e) { _o->boundaries.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->boundaries[_i] = _e->Get(_i); } } }
-}
-
-inline flatbuffers::Offset<BucketizeOptions> BucketizeOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateBucketizeOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<BucketizeOptions> CreateBucketizeOptions(flatbuffers::FlatBufferBuilder &_fbb, const BucketizeOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BucketizeOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _boundaries = _o->boundaries.size() ? _fbb.CreateVector(_o->boundaries) : 0;
-  return tflite::CreateBucketizeOptions(
-      _fbb,
-      _boundaries);
-}
-
-inline GeluOptionsT *GeluOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<GeluOptionsT>(new GeluOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void GeluOptions::UnPackTo(GeluOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = approximate(); _o->approximate = _e; }
-}
-
-inline flatbuffers::Offset<GeluOptions> GeluOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateGeluOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<GeluOptions> CreateGeluOptions(flatbuffers::FlatBufferBuilder &_fbb, const GeluOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const GeluOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _approximate = _o->approximate;
-  return tflite::CreateGeluOptions(
-      _fbb,
-      _approximate);
-}
-
-inline DynamicUpdateSliceOptionsT *DynamicUpdateSliceOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<DynamicUpdateSliceOptionsT>(new DynamicUpdateSliceOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void DynamicUpdateSliceOptions::UnPackTo(DynamicUpdateSliceOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<DynamicUpdateSliceOptions> DynamicUpdateSliceOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateDynamicUpdateSliceOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<DynamicUpdateSliceOptions> CreateDynamicUpdateSliceOptions(flatbuffers::FlatBufferBuilder &_fbb, const DynamicUpdateSliceOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const DynamicUpdateSliceOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateDynamicUpdateSliceOptions(
-      _fbb);
-}
-
-inline UnsortedSegmentProdOptionsT *UnsortedSegmentProdOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<UnsortedSegmentProdOptionsT>(new UnsortedSegmentProdOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void UnsortedSegmentProdOptions::UnPackTo(UnsortedSegmentProdOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<UnsortedSegmentProdOptions> UnsortedSegmentProdOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateUnsortedSegmentProdOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<UnsortedSegmentProdOptions> CreateUnsortedSegmentProdOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentProdOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentProdOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateUnsortedSegmentProdOptions(
-      _fbb);
-}
-
-inline UnsortedSegmentMaxOptionsT *UnsortedSegmentMaxOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<UnsortedSegmentMaxOptionsT>(new UnsortedSegmentMaxOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void UnsortedSegmentMaxOptions::UnPackTo(UnsortedSegmentMaxOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<UnsortedSegmentMaxOptions> UnsortedSegmentMaxOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateUnsortedSegmentMaxOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<UnsortedSegmentMaxOptions> CreateUnsortedSegmentMaxOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMaxOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentMaxOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateUnsortedSegmentMaxOptions(
-      _fbb);
-}
-
-inline UnsortedSegmentSumOptionsT *UnsortedSegmentSumOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<UnsortedSegmentSumOptionsT>(new UnsortedSegmentSumOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void UnsortedSegmentSumOptions::UnPackTo(UnsortedSegmentSumOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<UnsortedSegmentSumOptions> UnsortedSegmentSumOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateUnsortedSegmentSumOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<UnsortedSegmentSumOptions> CreateUnsortedSegmentSumOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentSumOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentSumOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateUnsortedSegmentSumOptions(
-      _fbb);
-}
-
-inline ATan2OptionsT *ATan2Options::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ATan2OptionsT>(new ATan2OptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void ATan2Options::UnPackTo(ATan2OptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<ATan2Options> ATan2Options::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateATan2Options(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<ATan2Options> CreateATan2Options(flatbuffers::FlatBufferBuilder &_fbb, const ATan2OptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ATan2OptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateATan2Options(
-      _fbb);
-}
-
-inline UnsortedSegmentMinOptionsT *UnsortedSegmentMinOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<UnsortedSegmentMinOptionsT>(new UnsortedSegmentMinOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void UnsortedSegmentMinOptions::UnPackTo(UnsortedSegmentMinOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<UnsortedSegmentMinOptions> UnsortedSegmentMinOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateUnsortedSegmentMinOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<UnsortedSegmentMinOptions> CreateUnsortedSegmentMinOptions(flatbuffers::FlatBufferBuilder &_fbb, const UnsortedSegmentMinOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const UnsortedSegmentMinOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateUnsortedSegmentMinOptions(
-      _fbb);
-}
-
-inline SignOptionsT *SignOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SignOptionsT>(new SignOptionsT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SignOptions::UnPackTo(SignOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-}
-
-inline flatbuffers::Offset<SignOptions> SignOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSignOptions(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SignOptions> CreateSignOptions(flatbuffers::FlatBufferBuilder &_fbb, const SignOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SignOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  return tflite::CreateSignOptions(
-      _fbb);
-}
-
-inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<OperatorCodeT>(new OperatorCodeT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void OperatorCode::UnPackTo(OperatorCodeT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = deprecated_builtin_code(); _o->deprecated_builtin_code = _e; }
-  { auto _e = custom_code(); if (_e) _o->custom_code = _e->str(); }
-  { auto _e = version(); _o->version = _e; }
-  { auto _e = builtin_code(); _o->builtin_code = _e; }
-}
-
-inline flatbuffers::Offset<OperatorCode> OperatorCode::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateOperatorCode(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<OperatorCode> CreateOperatorCode(flatbuffers::FlatBufferBuilder &_fbb, const OperatorCodeT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorCodeT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _deprecated_builtin_code = _o->deprecated_builtin_code;
-  auto _custom_code = _o->custom_code.empty() ? 0 : _fbb.CreateString(_o->custom_code);
-  auto _version = _o->version;
-  auto _builtin_code = _o->builtin_code;
-  return tflite::CreateOperatorCode(
-      _fbb,
-      _deprecated_builtin_code,
-      _custom_code,
-      _version,
-      _builtin_code);
-}
-
-inline OperatorT *Operator::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<OperatorT>(new OperatorT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void Operator::UnPackTo(OperatorT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = opcode_index(); _o->opcode_index = _e; }
-  { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } }
-  { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } }
-  { auto _e = builtin_options_type(); _o->builtin_options.type = _e; }
-  { auto _e = builtin_options(); if (_e) _o->builtin_options.value = tflite::BuiltinOptionsUnion::UnPack(_e, builtin_options_type(), _resolver); }
-  { auto _e = custom_options(); if (_e) { _o->custom_options.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->custom_options.begin()); } }
-  { auto _e = custom_options_format(); _o->custom_options_format = _e; }
-  { auto _e = mutating_variable_inputs(); if (_e) { _o->mutating_variable_inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->mutating_variable_inputs[_i] = _e->Get(_i) != 0; } } }
-  { auto _e = intermediates(); if (_e) { _o->intermediates.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->intermediates[_i] = _e->Get(_i); } } }
-}
-
-inline flatbuffers::Offset<Operator> Operator::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateOperator(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<Operator> CreateOperator(flatbuffers::FlatBufferBuilder &_fbb, const OperatorT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OperatorT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _opcode_index = _o->opcode_index;
-  auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0;
-  auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0;
-  auto _builtin_options_type = _o->builtin_options.type;
-  auto _builtin_options = _o->builtin_options.Pack(_fbb);
-  auto _custom_options = _o->custom_options.size() ? _fbb.CreateVector(_o->custom_options) : 0;
-  auto _custom_options_format = _o->custom_options_format;
-  auto _mutating_variable_inputs = _o->mutating_variable_inputs.size() ? _fbb.CreateVector(_o->mutating_variable_inputs) : 0;
-  auto _intermediates = _o->intermediates.size() ? _fbb.CreateVector(_o->intermediates) : 0;
-  return tflite::CreateOperator(
-      _fbb,
-      _opcode_index,
-      _inputs,
-      _outputs,
-      _builtin_options_type,
-      _builtin_options,
-      _custom_options,
-      _custom_options_format,
-      _mutating_variable_inputs,
-      _intermediates);
-}
-
-inline SubGraphT::SubGraphT(const SubGraphT &o)
-      : inputs(o.inputs),
-        outputs(o.outputs),
-        name(o.name) {
-  tensors.reserve(o.tensors.size());
-  for (const auto &tensors_ : o.tensors) { tensors.emplace_back((tensors_) ? new tflite::TensorT(*tensors_) : nullptr); }
-  operators.reserve(o.operators.size());
-  for (const auto &operators_ : o.operators) { operators.emplace_back((operators_) ? new tflite::OperatorT(*operators_) : nullptr); }
-}
-
-inline SubGraphT &SubGraphT::operator=(SubGraphT o) FLATBUFFERS_NOEXCEPT {
-  std::swap(tensors, o.tensors);
-  std::swap(inputs, o.inputs);
-  std::swap(outputs, o.outputs);
-  std::swap(operators, o.operators);
-  std::swap(name, o.name);
-  return *this;
-}
-
-inline SubGraphT *SubGraph::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SubGraphT>(new SubGraphT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SubGraph::UnPackTo(SubGraphT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = tensors(); if (_e) { _o->tensors.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->tensors[_i]) { _e->Get(_i)->UnPackTo(_o->tensors[_i].get(), _resolver); } else { _o->tensors[_i] = std::unique_ptr<tflite::TensorT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
-  { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->inputs[_i] = _e->Get(_i); } } }
-  { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->outputs[_i] = _e->Get(_i); } } }
-  { auto _e = operators(); if (_e) { _o->operators.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->operators[_i]) { _e->Get(_i)->UnPackTo(_o->operators[_i].get(), _resolver); } else { _o->operators[_i] = std::unique_ptr<tflite::OperatorT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
-  { auto _e = name(); if (_e) _o->name = _e->str(); }
-}
-
-inline flatbuffers::Offset<SubGraph> SubGraph::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSubGraph(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SubGraph> CreateSubGraph(flatbuffers::FlatBufferBuilder &_fbb, const SubGraphT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SubGraphT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _tensors = _o->tensors.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::Tensor>> (_o->tensors.size(), [](size_t i, _VectorArgs *__va) { return CreateTensor(*__va->__fbb, __va->__o->tensors[i].get(), __va->__rehasher); }, &_va ) : 0;
-  auto _inputs = _o->inputs.size() ? _fbb.CreateVector(_o->inputs) : 0;
-  auto _outputs = _o->outputs.size() ? _fbb.CreateVector(_o->outputs) : 0;
-  auto _operators = _o->operators.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::Operator>> (_o->operators.size(), [](size_t i, _VectorArgs *__va) { return CreateOperator(*__va->__fbb, __va->__o->operators[i].get(), __va->__rehasher); }, &_va ) : 0;
-  auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name);
-  return tflite::CreateSubGraph(
-      _fbb,
-      _tensors,
-      _inputs,
-      _outputs,
-      _operators,
-      _name);
-}
-
-inline BufferT *Buffer::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<BufferT>(new BufferT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void Buffer::UnPackTo(BufferT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = data(); if (_e) { _o->data.resize(_e->size()); std::copy(_e->begin(), _e->end(), _o->data.begin()); } }
-}
-
-inline flatbuffers::Offset<Buffer> Buffer::Pack(flatbuffers::FlatBufferBuilder &_fbb, const BufferT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateBuffer(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<Buffer> CreateBuffer(flatbuffers::FlatBufferBuilder &_fbb, const BufferT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const BufferT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  _fbb.ForceVectorAlignment(_o->data.size(), sizeof(uint8_t), 16);
-  auto _data = _o->data.size() ? _fbb.CreateVector(_o->data) : 0;
-  return tflite::CreateBuffer(
-      _fbb,
-      _data);
-}
-
-inline MetadataT *Metadata::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<MetadataT>(new MetadataT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void Metadata::UnPackTo(MetadataT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = name(); if (_e) _o->name = _e->str(); }
-  { auto _e = buffer(); _o->buffer = _e; }
-}
-
-inline flatbuffers::Offset<Metadata> Metadata::Pack(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateMetadata(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<Metadata> CreateMetadata(flatbuffers::FlatBufferBuilder &_fbb, const MetadataT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const MetadataT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name);
-  auto _buffer = _o->buffer;
-  return tflite::CreateMetadata(
-      _fbb,
-      _name,
-      _buffer);
-}
-
-inline TensorMapT *TensorMap::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<TensorMapT>(new TensorMapT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void TensorMap::UnPackTo(TensorMapT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = name(); if (_e) _o->name = _e->str(); }
-  { auto _e = tensor_index(); _o->tensor_index = _e; }
-}
-
-inline flatbuffers::Offset<TensorMap> TensorMap::Pack(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateTensorMap(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<TensorMap> CreateTensorMap(flatbuffers::FlatBufferBuilder &_fbb, const TensorMapT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const TensorMapT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _name = _o->name.empty() ? 0 : _fbb.CreateString(_o->name);
-  auto _tensor_index = _o->tensor_index;
-  return tflite::CreateTensorMap(
-      _fbb,
-      _name,
-      _tensor_index);
-}
-
-inline SignatureDefT::SignatureDefT(const SignatureDefT &o)
-      : signature_key(o.signature_key),
-        subgraph_index(o.subgraph_index) {
-  inputs.reserve(o.inputs.size());
-  for (const auto &inputs_ : o.inputs) { inputs.emplace_back((inputs_) ? new tflite::TensorMapT(*inputs_) : nullptr); }
-  outputs.reserve(o.outputs.size());
-  for (const auto &outputs_ : o.outputs) { outputs.emplace_back((outputs_) ? new tflite::TensorMapT(*outputs_) : nullptr); }
-}
-
-inline SignatureDefT &SignatureDefT::operator=(SignatureDefT o) FLATBUFFERS_NOEXCEPT {
-  std::swap(inputs, o.inputs);
-  std::swap(outputs, o.outputs);
-  std::swap(signature_key, o.signature_key);
-  std::swap(subgraph_index, o.subgraph_index);
-  return *this;
-}
-
-inline SignatureDefT *SignatureDef::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<SignatureDefT>(new SignatureDefT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void SignatureDef::UnPackTo(SignatureDefT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = inputs(); if (_e) { _o->inputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->inputs[_i]) { _e->Get(_i)->UnPackTo(_o->inputs[_i].get(), _resolver); } else { _o->inputs[_i] = std::unique_ptr<tflite::TensorMapT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
-  { auto _e = outputs(); if (_e) { _o->outputs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->outputs[_i]) { _e->Get(_i)->UnPackTo(_o->outputs[_i].get(), _resolver); } else { _o->outputs[_i] = std::unique_ptr<tflite::TensorMapT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
-  { auto _e = signature_key(); if (_e) _o->signature_key = _e->str(); }
-  { auto _e = subgraph_index(); _o->subgraph_index = _e; }
-}
-
-inline flatbuffers::Offset<SignatureDef> SignatureDef::Pack(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateSignatureDef(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<SignatureDef> CreateSignatureDef(flatbuffers::FlatBufferBuilder &_fbb, const SignatureDefT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const SignatureDefT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _inputs = _o->inputs.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::TensorMap>> (_o->inputs.size(), [](size_t i, _VectorArgs *__va) { return CreateTensorMap(*__va->__fbb, __va->__o->inputs[i].get(), __va->__rehasher); }, &_va ) : 0;
-  auto _outputs = _o->outputs.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::TensorMap>> (_o->outputs.size(), [](size_t i, _VectorArgs *__va) { return CreateTensorMap(*__va->__fbb, __va->__o->outputs[i].get(), __va->__rehasher); }, &_va ) : 0;
-  auto _signature_key = _o->signature_key.empty() ? 0 : _fbb.CreateString(_o->signature_key);
-  auto _subgraph_index = _o->subgraph_index;
-  return tflite::CreateSignatureDef(
-      _fbb,
-      _inputs,
-      _outputs,
-      _signature_key,
-      _subgraph_index);
-}
-
-inline ModelT::ModelT(const ModelT &o)
-      : version(o.version),
-        description(o.description),
-        metadata_buffer(o.metadata_buffer) {
-  operator_codes.reserve(o.operator_codes.size());
-  for (const auto &operator_codes_ : o.operator_codes) { operator_codes.emplace_back((operator_codes_) ? new tflite::OperatorCodeT(*operator_codes_) : nullptr); }
-  subgraphs.reserve(o.subgraphs.size());
-  for (const auto &subgraphs_ : o.subgraphs) { subgraphs.emplace_back((subgraphs_) ? new tflite::SubGraphT(*subgraphs_) : nullptr); }
-  buffers.reserve(o.buffers.size());
-  for (const auto &buffers_ : o.buffers) { buffers.emplace_back((buffers_) ? new tflite::BufferT(*buffers_) : nullptr); }
-  metadata.reserve(o.metadata.size());
-  for (const auto &metadata_ : o.metadata) { metadata.emplace_back((metadata_) ? new tflite::MetadataT(*metadata_) : nullptr); }
-  signature_defs.reserve(o.signature_defs.size());
-  for (const auto &signature_defs_ : o.signature_defs) { signature_defs.emplace_back((signature_defs_) ? new tflite::SignatureDefT(*signature_defs_) : nullptr); }
-}
-
-inline ModelT &ModelT::operator=(ModelT o) FLATBUFFERS_NOEXCEPT {
-  std::swap(version, o.version);
-  std::swap(operator_codes, o.operator_codes);
-  std::swap(subgraphs, o.subgraphs);
-  std::swap(description, o.description);
-  std::swap(buffers, o.buffers);
-  std::swap(metadata_buffer, o.metadata_buffer);
-  std::swap(metadata, o.metadata);
-  std::swap(signature_defs, o.signature_defs);
-  return *this;
-}
-
-inline ModelT *Model::UnPack(const flatbuffers::resolver_function_t *_resolver) const {
-  auto _o = std::unique_ptr<ModelT>(new ModelT());
-  UnPackTo(_o.get(), _resolver);
-  return _o.release();
-}
-
-inline void Model::UnPackTo(ModelT *_o, const flatbuffers::resolver_function_t *_resolver) const {
-  (void)_o;
-  (void)_resolver;
-  { auto _e = version(); _o->version = _e; }
-  { auto _e = operator_codes(); if (_e) { _o->operator_codes.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->operator_codes[_i]) { _e->Get(_i)->UnPackTo(_o->operator_codes[_i].get(), _resolver); } else { _o->operator_codes[_i] = std::unique_ptr<tflite::OperatorCodeT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
-  { auto _e = subgraphs(); if (_e) { _o->subgraphs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->subgraphs[_i]) { _e->Get(_i)->UnPackTo(_o->subgraphs[_i].get(), _resolver); } else { _o->subgraphs[_i] = std::unique_ptr<tflite::SubGraphT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
-  { auto _e = description(); if (_e) _o->description = _e->str(); }
-  { auto _e = buffers(); if (_e) { _o->buffers.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->buffers[_i]) { _e->Get(_i)->UnPackTo(_o->buffers[_i].get(), _resolver); } else { _o->buffers[_i] = std::unique_ptr<tflite::BufferT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
-  { auto _e = metadata_buffer(); if (_e) { _o->metadata_buffer.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { _o->metadata_buffer[_i] = _e->Get(_i); } } }
-  { auto _e = metadata(); if (_e) { _o->metadata.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->metadata[_i]) { _e->Get(_i)->UnPackTo(_o->metadata[_i].get(), _resolver); } else { _o->metadata[_i] = std::unique_ptr<tflite::MetadataT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
-  { auto _e = signature_defs(); if (_e) { _o->signature_defs.resize(_e->size()); for (flatbuffers::uoffset_t _i = 0; _i < _e->size(); _i++) { if(_o->signature_defs[_i]) { _e->Get(_i)->UnPackTo(_o->signature_defs[_i].get(), _resolver); } else { _o->signature_defs[_i] = std::unique_ptr<tflite::SignatureDefT>(_e->Get(_i)->UnPack(_resolver)); }; } } }
-}
-
-inline flatbuffers::Offset<Model> Model::Pack(flatbuffers::FlatBufferBuilder &_fbb, const ModelT* _o, const flatbuffers::rehasher_function_t *_rehasher) {
-  return CreateModel(_fbb, _o, _rehasher);
-}
-
-inline flatbuffers::Offset<Model> CreateModel(flatbuffers::FlatBufferBuilder &_fbb, const ModelT *_o, const flatbuffers::rehasher_function_t *_rehasher) {
-  (void)_rehasher;
-  (void)_o;
-  struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const ModelT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va;
-  auto _version = _o->version;
-  auto _operator_codes = _o->operator_codes.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::OperatorCode>> (_o->operator_codes.size(), [](size_t i, _VectorArgs *__va) { return CreateOperatorCode(*__va->__fbb, __va->__o->operator_codes[i].get(), __va->__rehasher); }, &_va ) : 0;
-  auto _subgraphs = _o->subgraphs.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::SubGraph>> (_o->subgraphs.size(), [](size_t i, _VectorArgs *__va) { return CreateSubGraph(*__va->__fbb, __va->__o->subgraphs[i].get(), __va->__rehasher); }, &_va ) : 0;
-  auto _description = _o->description.empty() ? 0 : _fbb.CreateString(_o->description);
-  auto _buffers = _o->buffers.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::Buffer>> (_o->buffers.size(), [](size_t i, _VectorArgs *__va) { return CreateBuffer(*__va->__fbb, __va->__o->buffers[i].get(), __va->__rehasher); }, &_va ) : 0;
-  auto _metadata_buffer = _o->metadata_buffer.size() ? _fbb.CreateVector(_o->metadata_buffer) : 0;
-  auto _metadata = _o->metadata.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::Metadata>> (_o->metadata.size(), [](size_t i, _VectorArgs *__va) { return CreateMetadata(*__va->__fbb, __va->__o->metadata[i].get(), __va->__rehasher); }, &_va ) : 0;
-  auto _signature_defs = _o->signature_defs.size() ? _fbb.CreateVector<flatbuffers::Offset<tflite::SignatureDef>> (_o->signature_defs.size(), [](size_t i, _VectorArgs *__va) { return CreateSignatureDef(*__va->__fbb, __va->__o->signature_defs[i].get(), __va->__rehasher); }, &_va ) : 0;
-  return tflite::CreateModel(
-      _fbb,
-      _version,
-      _operator_codes,
-      _subgraphs,
-      _description,
-      _buffers,
-      _metadata_buffer,
-      _metadata,
-      _signature_defs);
-}
-
-inline bool VerifyQuantizationDetails(flatbuffers::Verifier &verifier, const void *obj, QuantizationDetails type) {
-  switch (type) {
-    case QuantizationDetails_NONE: {
-      return true;
-    }
-    case QuantizationDetails_CustomQuantization: {
-      auto ptr = reinterpret_cast<const tflite::CustomQuantization *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    default: return true;
-  }
-}
-
-inline bool VerifyQuantizationDetailsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types) {
-  if (!values || !types) return !values && !types;
-  if (values->size() != types->size()) return false;
-  for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) {
-    if (!VerifyQuantizationDetails(
-        verifier,  values->Get(i), types->GetEnum<QuantizationDetails>(i))) {
-      return false;
-    }
-  }
-  return true;
-}
-
-inline void *QuantizationDetailsUnion::UnPack(const void *obj, QuantizationDetails type, const flatbuffers::resolver_function_t *resolver) {
-  (void)resolver;
-  switch (type) {
-    case QuantizationDetails_CustomQuantization: {
-      auto ptr = reinterpret_cast<const tflite::CustomQuantization *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    default: return nullptr;
-  }
-}
-
-inline flatbuffers::Offset<void> QuantizationDetailsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const {
-  (void)_rehasher;
-  switch (type) {
-    case QuantizationDetails_CustomQuantization: {
-      auto ptr = reinterpret_cast<const tflite::CustomQuantizationT *>(value);
-      return CreateCustomQuantization(_fbb, ptr, _rehasher).Union();
-    }
-    default: return 0;
-  }
-}
-
-inline QuantizationDetailsUnion::QuantizationDetailsUnion(const QuantizationDetailsUnion &u) : type(u.type), value(nullptr) {
-  switch (type) {
-    case QuantizationDetails_CustomQuantization: {
-      value = new tflite::CustomQuantizationT(*reinterpret_cast<tflite::CustomQuantizationT *>(u.value));
-      break;
-    }
-    default:
-      break;
-  }
-}
-
-inline void QuantizationDetailsUnion::Reset() {
-  switch (type) {
-    case QuantizationDetails_CustomQuantization: {
-      auto ptr = reinterpret_cast<tflite::CustomQuantizationT *>(value);
-      delete ptr;
-      break;
-    }
-    default: break;
-  }
-  value = nullptr;
-  type = QuantizationDetails_NONE;
-}
-
-inline bool VerifySparseIndexVector(flatbuffers::Verifier &verifier, const void *obj, SparseIndexVector type) {
-  switch (type) {
-    case SparseIndexVector_NONE: {
-      return true;
-    }
-    case SparseIndexVector_Int32Vector: {
-      auto ptr = reinterpret_cast<const tflite::Int32Vector *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case SparseIndexVector_Uint16Vector: {
-      auto ptr = reinterpret_cast<const tflite::Uint16Vector *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case SparseIndexVector_Uint8Vector: {
-      auto ptr = reinterpret_cast<const tflite::Uint8Vector *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    default: return true;
-  }
-}
-
-inline bool VerifySparseIndexVectorVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types) {
-  if (!values || !types) return !values && !types;
-  if (values->size() != types->size()) return false;
-  for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) {
-    if (!VerifySparseIndexVector(
-        verifier,  values->Get(i), types->GetEnum<SparseIndexVector>(i))) {
-      return false;
-    }
-  }
-  return true;
-}
-
-inline void *SparseIndexVectorUnion::UnPack(const void *obj, SparseIndexVector type, const flatbuffers::resolver_function_t *resolver) {
-  (void)resolver;
-  switch (type) {
-    case SparseIndexVector_Int32Vector: {
-      auto ptr = reinterpret_cast<const tflite::Int32Vector *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case SparseIndexVector_Uint16Vector: {
-      auto ptr = reinterpret_cast<const tflite::Uint16Vector *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case SparseIndexVector_Uint8Vector: {
-      auto ptr = reinterpret_cast<const tflite::Uint8Vector *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    default: return nullptr;
-  }
-}
-
-inline flatbuffers::Offset<void> SparseIndexVectorUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const {
-  (void)_rehasher;
-  switch (type) {
-    case SparseIndexVector_Int32Vector: {
-      auto ptr = reinterpret_cast<const tflite::Int32VectorT *>(value);
-      return CreateInt32Vector(_fbb, ptr, _rehasher).Union();
-    }
-    case SparseIndexVector_Uint16Vector: {
-      auto ptr = reinterpret_cast<const tflite::Uint16VectorT *>(value);
-      return CreateUint16Vector(_fbb, ptr, _rehasher).Union();
-    }
-    case SparseIndexVector_Uint8Vector: {
-      auto ptr = reinterpret_cast<const tflite::Uint8VectorT *>(value);
-      return CreateUint8Vector(_fbb, ptr, _rehasher).Union();
-    }
-    default: return 0;
-  }
-}
-
-inline SparseIndexVectorUnion::SparseIndexVectorUnion(const SparseIndexVectorUnion &u) : type(u.type), value(nullptr) {
-  switch (type) {
-    case SparseIndexVector_Int32Vector: {
-      value = new tflite::Int32VectorT(*reinterpret_cast<tflite::Int32VectorT *>(u.value));
-      break;
-    }
-    case SparseIndexVector_Uint16Vector: {
-      value = new tflite::Uint16VectorT(*reinterpret_cast<tflite::Uint16VectorT *>(u.value));
-      break;
-    }
-    case SparseIndexVector_Uint8Vector: {
-      value = new tflite::Uint8VectorT(*reinterpret_cast<tflite::Uint8VectorT *>(u.value));
-      break;
-    }
-    default:
-      break;
-  }
-}
-
-inline void SparseIndexVectorUnion::Reset() {
-  switch (type) {
-    case SparseIndexVector_Int32Vector: {
-      auto ptr = reinterpret_cast<tflite::Int32VectorT *>(value);
-      delete ptr;
-      break;
-    }
-    case SparseIndexVector_Uint16Vector: {
-      auto ptr = reinterpret_cast<tflite::Uint16VectorT *>(value);
-      delete ptr;
-      break;
-    }
-    case SparseIndexVector_Uint8Vector: {
-      auto ptr = reinterpret_cast<tflite::Uint8VectorT *>(value);
-      delete ptr;
-      break;
-    }
-    default: break;
-  }
-  value = nullptr;
-  type = SparseIndexVector_NONE;
-}
-
-inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type) {
-  switch (type) {
-    case BuiltinOptions_NONE: {
-      return true;
-    }
-    case BuiltinOptions_Conv2DOptions: {
-      auto ptr = reinterpret_cast<const tflite::Conv2DOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_DepthwiseConv2DOptions: {
-      auto ptr = reinterpret_cast<const tflite::DepthwiseConv2DOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ConcatEmbeddingsOptions: {
-      auto ptr = reinterpret_cast<const tflite::ConcatEmbeddingsOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LSHProjectionOptions: {
-      auto ptr = reinterpret_cast<const tflite::LSHProjectionOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_Pool2DOptions: {
-      auto ptr = reinterpret_cast<const tflite::Pool2DOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SVDFOptions: {
-      auto ptr = reinterpret_cast<const tflite::SVDFOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_RNNOptions: {
-      auto ptr = reinterpret_cast<const tflite::RNNOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FullyConnectedOptions: {
-      auto ptr = reinterpret_cast<const tflite::FullyConnectedOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SoftmaxOptions: {
-      auto ptr = reinterpret_cast<const tflite::SoftmaxOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ConcatenationOptions: {
-      auto ptr = reinterpret_cast<const tflite::ConcatenationOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_AddOptions: {
-      auto ptr = reinterpret_cast<const tflite::AddOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_L2NormOptions: {
-      auto ptr = reinterpret_cast<const tflite::L2NormOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LocalResponseNormalizationOptions: {
-      auto ptr = reinterpret_cast<const tflite::LocalResponseNormalizationOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LSTMOptions: {
-      auto ptr = reinterpret_cast<const tflite::LSTMOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ResizeBilinearOptions: {
-      auto ptr = reinterpret_cast<const tflite::ResizeBilinearOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_CallOptions: {
-      auto ptr = reinterpret_cast<const tflite::CallOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ReshapeOptions: {
-      auto ptr = reinterpret_cast<const tflite::ReshapeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SkipGramOptions: {
-      auto ptr = reinterpret_cast<const tflite::SkipGramOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SpaceToDepthOptions: {
-      auto ptr = reinterpret_cast<const tflite::SpaceToDepthOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_EmbeddingLookupSparseOptions: {
-      auto ptr = reinterpret_cast<const tflite::EmbeddingLookupSparseOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_MulOptions: {
-      auto ptr = reinterpret_cast<const tflite::MulOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_PadOptions: {
-      auto ptr = reinterpret_cast<const tflite::PadOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_GatherOptions: {
-      auto ptr = reinterpret_cast<const tflite::GatherOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_BatchToSpaceNDOptions: {
-      auto ptr = reinterpret_cast<const tflite::BatchToSpaceNDOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SpaceToBatchNDOptions: {
-      auto ptr = reinterpret_cast<const tflite::SpaceToBatchNDOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_TransposeOptions: {
-      auto ptr = reinterpret_cast<const tflite::TransposeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ReducerOptions: {
-      auto ptr = reinterpret_cast<const tflite::ReducerOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SubOptions: {
-      auto ptr = reinterpret_cast<const tflite::SubOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_DivOptions: {
-      auto ptr = reinterpret_cast<const tflite::DivOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SqueezeOptions: {
-      auto ptr = reinterpret_cast<const tflite::SqueezeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SequenceRNNOptions: {
-      auto ptr = reinterpret_cast<const tflite::SequenceRNNOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_StridedSliceOptions: {
-      auto ptr = reinterpret_cast<const tflite::StridedSliceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ExpOptions: {
-      auto ptr = reinterpret_cast<const tflite::ExpOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_TopKV2Options: {
-      auto ptr = reinterpret_cast<const tflite::TopKV2Options *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SplitOptions: {
-      auto ptr = reinterpret_cast<const tflite::SplitOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LogSoftmaxOptions: {
-      auto ptr = reinterpret_cast<const tflite::LogSoftmaxOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_CastOptions: {
-      auto ptr = reinterpret_cast<const tflite::CastOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_DequantizeOptions: {
-      auto ptr = reinterpret_cast<const tflite::DequantizeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_MaximumMinimumOptions: {
-      auto ptr = reinterpret_cast<const tflite::MaximumMinimumOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ArgMaxOptions: {
-      auto ptr = reinterpret_cast<const tflite::ArgMaxOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LessOptions: {
-      auto ptr = reinterpret_cast<const tflite::LessOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_NegOptions: {
-      auto ptr = reinterpret_cast<const tflite::NegOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_PadV2Options: {
-      auto ptr = reinterpret_cast<const tflite::PadV2Options *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_GreaterOptions: {
-      auto ptr = reinterpret_cast<const tflite::GreaterOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_GreaterEqualOptions: {
-      auto ptr = reinterpret_cast<const tflite::GreaterEqualOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LessEqualOptions: {
-      auto ptr = reinterpret_cast<const tflite::LessEqualOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SelectOptions: {
-      auto ptr = reinterpret_cast<const tflite::SelectOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SliceOptions: {
-      auto ptr = reinterpret_cast<const tflite::SliceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_TransposeConvOptions: {
-      auto ptr = reinterpret_cast<const tflite::TransposeConvOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SparseToDenseOptions: {
-      auto ptr = reinterpret_cast<const tflite::SparseToDenseOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_TileOptions: {
-      auto ptr = reinterpret_cast<const tflite::TileOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ExpandDimsOptions: {
-      auto ptr = reinterpret_cast<const tflite::ExpandDimsOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_EqualOptions: {
-      auto ptr = reinterpret_cast<const tflite::EqualOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_NotEqualOptions: {
-      auto ptr = reinterpret_cast<const tflite::NotEqualOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ShapeOptions: {
-      auto ptr = reinterpret_cast<const tflite::ShapeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_PowOptions: {
-      auto ptr = reinterpret_cast<const tflite::PowOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ArgMinOptions: {
-      auto ptr = reinterpret_cast<const tflite::ArgMinOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FakeQuantOptions: {
-      auto ptr = reinterpret_cast<const tflite::FakeQuantOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_PackOptions: {
-      auto ptr = reinterpret_cast<const tflite::PackOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LogicalOrOptions: {
-      auto ptr = reinterpret_cast<const tflite::LogicalOrOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_OneHotOptions: {
-      auto ptr = reinterpret_cast<const tflite::OneHotOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LogicalAndOptions: {
-      auto ptr = reinterpret_cast<const tflite::LogicalAndOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LogicalNotOptions: {
-      auto ptr = reinterpret_cast<const tflite::LogicalNotOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_UnpackOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnpackOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FloorDivOptions: {
-      auto ptr = reinterpret_cast<const tflite::FloorDivOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SquareOptions: {
-      auto ptr = reinterpret_cast<const tflite::SquareOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ZerosLikeOptions: {
-      auto ptr = reinterpret_cast<const tflite::ZerosLikeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FillOptions: {
-      auto ptr = reinterpret_cast<const tflite::FillOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_BidirectionalSequenceLSTMOptions: {
-      auto ptr = reinterpret_cast<const tflite::BidirectionalSequenceLSTMOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_BidirectionalSequenceRNNOptions: {
-      auto ptr = reinterpret_cast<const tflite::BidirectionalSequenceRNNOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_UnidirectionalSequenceLSTMOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnidirectionalSequenceLSTMOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_FloorModOptions: {
-      auto ptr = reinterpret_cast<const tflite::FloorModOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_RangeOptions: {
-      auto ptr = reinterpret_cast<const tflite::RangeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ResizeNearestNeighborOptions: {
-      auto ptr = reinterpret_cast<const tflite::ResizeNearestNeighborOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_LeakyReluOptions: {
-      auto ptr = reinterpret_cast<const tflite::LeakyReluOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SquaredDifferenceOptions: {
-      auto ptr = reinterpret_cast<const tflite::SquaredDifferenceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_MirrorPadOptions: {
-      auto ptr = reinterpret_cast<const tflite::MirrorPadOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_AbsOptions: {
-      auto ptr = reinterpret_cast<const tflite::AbsOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SplitVOptions: {
-      auto ptr = reinterpret_cast<const tflite::SplitVOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_UniqueOptions: {
-      auto ptr = reinterpret_cast<const tflite::UniqueOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ReverseV2Options: {
-      auto ptr = reinterpret_cast<const tflite::ReverseV2Options *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_AddNOptions: {
-      auto ptr = reinterpret_cast<const tflite::AddNOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_GatherNdOptions: {
-      auto ptr = reinterpret_cast<const tflite::GatherNdOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_CosOptions: {
-      auto ptr = reinterpret_cast<const tflite::CosOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_WhereOptions: {
-      auto ptr = reinterpret_cast<const tflite::WhereOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_RankOptions: {
-      auto ptr = reinterpret_cast<const tflite::RankOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ReverseSequenceOptions: {
-      auto ptr = reinterpret_cast<const tflite::ReverseSequenceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_MatrixDiagOptions: {
-      auto ptr = reinterpret_cast<const tflite::MatrixDiagOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_QuantizeOptions: {
-      auto ptr = reinterpret_cast<const tflite::QuantizeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_MatrixSetDiagOptions: {
-      auto ptr = reinterpret_cast<const tflite::MatrixSetDiagOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_HardSwishOptions: {
-      auto ptr = reinterpret_cast<const tflite::HardSwishOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_IfOptions: {
-      auto ptr = reinterpret_cast<const tflite::IfOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_WhileOptions: {
-      auto ptr = reinterpret_cast<const tflite::WhileOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_DepthToSpaceOptions: {
-      auto ptr = reinterpret_cast<const tflite::DepthToSpaceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_NonMaxSuppressionV4Options: {
-      auto ptr = reinterpret_cast<const tflite::NonMaxSuppressionV4Options *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_NonMaxSuppressionV5Options: {
-      auto ptr = reinterpret_cast<const tflite::NonMaxSuppressionV5Options *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ScatterNdOptions: {
-      auto ptr = reinterpret_cast<const tflite::ScatterNdOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SelectV2Options: {
-      auto ptr = reinterpret_cast<const tflite::SelectV2Options *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_DensifyOptions: {
-      auto ptr = reinterpret_cast<const tflite::DensifyOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SegmentSumOptions: {
-      auto ptr = reinterpret_cast<const tflite::SegmentSumOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_BatchMatMulOptions: {
-      auto ptr = reinterpret_cast<const tflite::BatchMatMulOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_CumsumOptions: {
-      auto ptr = reinterpret_cast<const tflite::CumsumOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_CallOnceOptions: {
-      auto ptr = reinterpret_cast<const tflite::CallOnceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_BroadcastToOptions: {
-      auto ptr = reinterpret_cast<const tflite::BroadcastToOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_Rfft2dOptions: {
-      auto ptr = reinterpret_cast<const tflite::Rfft2dOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_Conv3DOptions: {
-      auto ptr = reinterpret_cast<const tflite::Conv3DOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_HashtableOptions: {
-      auto ptr = reinterpret_cast<const tflite::HashtableOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_HashtableFindOptions: {
-      auto ptr = reinterpret_cast<const tflite::HashtableFindOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_HashtableImportOptions: {
-      auto ptr = reinterpret_cast<const tflite::HashtableImportOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_HashtableSizeOptions: {
-      auto ptr = reinterpret_cast<const tflite::HashtableSizeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_VarHandleOptions: {
-      auto ptr = reinterpret_cast<const tflite::VarHandleOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ReadVariableOptions: {
-      auto ptr = reinterpret_cast<const tflite::ReadVariableOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_AssignVariableOptions: {
-      auto ptr = reinterpret_cast<const tflite::AssignVariableOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_RandomOptions: {
-      auto ptr = reinterpret_cast<const tflite::RandomOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_BucketizeOptions: {
-      auto ptr = reinterpret_cast<const tflite::BucketizeOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_GeluOptions: {
-      auto ptr = reinterpret_cast<const tflite::GeluOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_DynamicUpdateSliceOptions: {
-      auto ptr = reinterpret_cast<const tflite::DynamicUpdateSliceOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_UnsortedSegmentProdOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnsortedSegmentProdOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_UnsortedSegmentMaxOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnsortedSegmentMaxOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_UnsortedSegmentMinOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnsortedSegmentMinOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_UnsortedSegmentSumOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnsortedSegmentSumOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_ATan2Options: {
-      auto ptr = reinterpret_cast<const tflite::ATan2Options *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    case BuiltinOptions_SignOptions: {
-      auto ptr = reinterpret_cast<const tflite::SignOptions *>(obj);
-      return verifier.VerifyTable(ptr);
-    }
-    default: return true;
-  }
-}
-
-inline bool VerifyBuiltinOptionsVector(flatbuffers::Verifier &verifier, const flatbuffers::Vector<flatbuffers::Offset<void>> *values, const flatbuffers::Vector<uint8_t> *types) {
-  if (!values || !types) return !values && !types;
-  if (values->size() != types->size()) return false;
-  for (flatbuffers::uoffset_t i = 0; i < values->size(); ++i) {
-    if (!VerifyBuiltinOptions(
-        verifier,  values->Get(i), types->GetEnum<BuiltinOptions>(i))) {
-      return false;
-    }
-  }
-  return true;
-}
-
-inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, const flatbuffers::resolver_function_t *resolver) {
-  (void)resolver;
-  switch (type) {
-    case BuiltinOptions_Conv2DOptions: {
-      auto ptr = reinterpret_cast<const tflite::Conv2DOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_DepthwiseConv2DOptions: {
-      auto ptr = reinterpret_cast<const tflite::DepthwiseConv2DOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ConcatEmbeddingsOptions: {
-      auto ptr = reinterpret_cast<const tflite::ConcatEmbeddingsOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_LSHProjectionOptions: {
-      auto ptr = reinterpret_cast<const tflite::LSHProjectionOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_Pool2DOptions: {
-      auto ptr = reinterpret_cast<const tflite::Pool2DOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SVDFOptions: {
-      auto ptr = reinterpret_cast<const tflite::SVDFOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_RNNOptions: {
-      auto ptr = reinterpret_cast<const tflite::RNNOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_FullyConnectedOptions: {
-      auto ptr = reinterpret_cast<const tflite::FullyConnectedOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SoftmaxOptions: {
-      auto ptr = reinterpret_cast<const tflite::SoftmaxOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ConcatenationOptions: {
-      auto ptr = reinterpret_cast<const tflite::ConcatenationOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_AddOptions: {
-      auto ptr = reinterpret_cast<const tflite::AddOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_L2NormOptions: {
-      auto ptr = reinterpret_cast<const tflite::L2NormOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_LocalResponseNormalizationOptions: {
-      auto ptr = reinterpret_cast<const tflite::LocalResponseNormalizationOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_LSTMOptions: {
-      auto ptr = reinterpret_cast<const tflite::LSTMOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ResizeBilinearOptions: {
-      auto ptr = reinterpret_cast<const tflite::ResizeBilinearOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_CallOptions: {
-      auto ptr = reinterpret_cast<const tflite::CallOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ReshapeOptions: {
-      auto ptr = reinterpret_cast<const tflite::ReshapeOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SkipGramOptions: {
-      auto ptr = reinterpret_cast<const tflite::SkipGramOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SpaceToDepthOptions: {
-      auto ptr = reinterpret_cast<const tflite::SpaceToDepthOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_EmbeddingLookupSparseOptions: {
-      auto ptr = reinterpret_cast<const tflite::EmbeddingLookupSparseOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_MulOptions: {
-      auto ptr = reinterpret_cast<const tflite::MulOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_PadOptions: {
-      auto ptr = reinterpret_cast<const tflite::PadOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_GatherOptions: {
-      auto ptr = reinterpret_cast<const tflite::GatherOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_BatchToSpaceNDOptions: {
-      auto ptr = reinterpret_cast<const tflite::BatchToSpaceNDOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SpaceToBatchNDOptions: {
-      auto ptr = reinterpret_cast<const tflite::SpaceToBatchNDOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_TransposeOptions: {
-      auto ptr = reinterpret_cast<const tflite::TransposeOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ReducerOptions: {
-      auto ptr = reinterpret_cast<const tflite::ReducerOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SubOptions: {
-      auto ptr = reinterpret_cast<const tflite::SubOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_DivOptions: {
-      auto ptr = reinterpret_cast<const tflite::DivOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SqueezeOptions: {
-      auto ptr = reinterpret_cast<const tflite::SqueezeOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SequenceRNNOptions: {
-      auto ptr = reinterpret_cast<const tflite::SequenceRNNOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_StridedSliceOptions: {
-      auto ptr = reinterpret_cast<const tflite::StridedSliceOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ExpOptions: {
-      auto ptr = reinterpret_cast<const tflite::ExpOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_TopKV2Options: {
-      auto ptr = reinterpret_cast<const tflite::TopKV2Options *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SplitOptions: {
-      auto ptr = reinterpret_cast<const tflite::SplitOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_LogSoftmaxOptions: {
-      auto ptr = reinterpret_cast<const tflite::LogSoftmaxOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_CastOptions: {
-      auto ptr = reinterpret_cast<const tflite::CastOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_DequantizeOptions: {
-      auto ptr = reinterpret_cast<const tflite::DequantizeOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_MaximumMinimumOptions: {
-      auto ptr = reinterpret_cast<const tflite::MaximumMinimumOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ArgMaxOptions: {
-      auto ptr = reinterpret_cast<const tflite::ArgMaxOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_LessOptions: {
-      auto ptr = reinterpret_cast<const tflite::LessOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_NegOptions: {
-      auto ptr = reinterpret_cast<const tflite::NegOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_PadV2Options: {
-      auto ptr = reinterpret_cast<const tflite::PadV2Options *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_GreaterOptions: {
-      auto ptr = reinterpret_cast<const tflite::GreaterOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_GreaterEqualOptions: {
-      auto ptr = reinterpret_cast<const tflite::GreaterEqualOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_LessEqualOptions: {
-      auto ptr = reinterpret_cast<const tflite::LessEqualOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SelectOptions: {
-      auto ptr = reinterpret_cast<const tflite::SelectOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SliceOptions: {
-      auto ptr = reinterpret_cast<const tflite::SliceOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_TransposeConvOptions: {
-      auto ptr = reinterpret_cast<const tflite::TransposeConvOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SparseToDenseOptions: {
-      auto ptr = reinterpret_cast<const tflite::SparseToDenseOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_TileOptions: {
-      auto ptr = reinterpret_cast<const tflite::TileOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ExpandDimsOptions: {
-      auto ptr = reinterpret_cast<const tflite::ExpandDimsOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_EqualOptions: {
-      auto ptr = reinterpret_cast<const tflite::EqualOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_NotEqualOptions: {
-      auto ptr = reinterpret_cast<const tflite::NotEqualOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ShapeOptions: {
-      auto ptr = reinterpret_cast<const tflite::ShapeOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_PowOptions: {
-      auto ptr = reinterpret_cast<const tflite::PowOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ArgMinOptions: {
-      auto ptr = reinterpret_cast<const tflite::ArgMinOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_FakeQuantOptions: {
-      auto ptr = reinterpret_cast<const tflite::FakeQuantOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_PackOptions: {
-      auto ptr = reinterpret_cast<const tflite::PackOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_LogicalOrOptions: {
-      auto ptr = reinterpret_cast<const tflite::LogicalOrOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_OneHotOptions: {
-      auto ptr = reinterpret_cast<const tflite::OneHotOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_LogicalAndOptions: {
-      auto ptr = reinterpret_cast<const tflite::LogicalAndOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_LogicalNotOptions: {
-      auto ptr = reinterpret_cast<const tflite::LogicalNotOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_UnpackOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnpackOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_FloorDivOptions: {
-      auto ptr = reinterpret_cast<const tflite::FloorDivOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SquareOptions: {
-      auto ptr = reinterpret_cast<const tflite::SquareOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ZerosLikeOptions: {
-      auto ptr = reinterpret_cast<const tflite::ZerosLikeOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_FillOptions: {
-      auto ptr = reinterpret_cast<const tflite::FillOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_BidirectionalSequenceLSTMOptions: {
-      auto ptr = reinterpret_cast<const tflite::BidirectionalSequenceLSTMOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_BidirectionalSequenceRNNOptions: {
-      auto ptr = reinterpret_cast<const tflite::BidirectionalSequenceRNNOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_UnidirectionalSequenceLSTMOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnidirectionalSequenceLSTMOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_FloorModOptions: {
-      auto ptr = reinterpret_cast<const tflite::FloorModOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_RangeOptions: {
-      auto ptr = reinterpret_cast<const tflite::RangeOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ResizeNearestNeighborOptions: {
-      auto ptr = reinterpret_cast<const tflite::ResizeNearestNeighborOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_LeakyReluOptions: {
-      auto ptr = reinterpret_cast<const tflite::LeakyReluOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SquaredDifferenceOptions: {
-      auto ptr = reinterpret_cast<const tflite::SquaredDifferenceOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_MirrorPadOptions: {
-      auto ptr = reinterpret_cast<const tflite::MirrorPadOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_AbsOptions: {
-      auto ptr = reinterpret_cast<const tflite::AbsOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SplitVOptions: {
-      auto ptr = reinterpret_cast<const tflite::SplitVOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_UniqueOptions: {
-      auto ptr = reinterpret_cast<const tflite::UniqueOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ReverseV2Options: {
-      auto ptr = reinterpret_cast<const tflite::ReverseV2Options *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_AddNOptions: {
-      auto ptr = reinterpret_cast<const tflite::AddNOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_GatherNdOptions: {
-      auto ptr = reinterpret_cast<const tflite::GatherNdOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_CosOptions: {
-      auto ptr = reinterpret_cast<const tflite::CosOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_WhereOptions: {
-      auto ptr = reinterpret_cast<const tflite::WhereOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_RankOptions: {
-      auto ptr = reinterpret_cast<const tflite::RankOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ReverseSequenceOptions: {
-      auto ptr = reinterpret_cast<const tflite::ReverseSequenceOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_MatrixDiagOptions: {
-      auto ptr = reinterpret_cast<const tflite::MatrixDiagOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_QuantizeOptions: {
-      auto ptr = reinterpret_cast<const tflite::QuantizeOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_MatrixSetDiagOptions: {
-      auto ptr = reinterpret_cast<const tflite::MatrixSetDiagOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_HardSwishOptions: {
-      auto ptr = reinterpret_cast<const tflite::HardSwishOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_IfOptions: {
-      auto ptr = reinterpret_cast<const tflite::IfOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_WhileOptions: {
-      auto ptr = reinterpret_cast<const tflite::WhileOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_DepthToSpaceOptions: {
-      auto ptr = reinterpret_cast<const tflite::DepthToSpaceOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_NonMaxSuppressionV4Options: {
-      auto ptr = reinterpret_cast<const tflite::NonMaxSuppressionV4Options *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_NonMaxSuppressionV5Options: {
-      auto ptr = reinterpret_cast<const tflite::NonMaxSuppressionV5Options *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ScatterNdOptions: {
-      auto ptr = reinterpret_cast<const tflite::ScatterNdOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SelectV2Options: {
-      auto ptr = reinterpret_cast<const tflite::SelectV2Options *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_DensifyOptions: {
-      auto ptr = reinterpret_cast<const tflite::DensifyOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SegmentSumOptions: {
-      auto ptr = reinterpret_cast<const tflite::SegmentSumOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_BatchMatMulOptions: {
-      auto ptr = reinterpret_cast<const tflite::BatchMatMulOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_CumsumOptions: {
-      auto ptr = reinterpret_cast<const tflite::CumsumOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_CallOnceOptions: {
-      auto ptr = reinterpret_cast<const tflite::CallOnceOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_BroadcastToOptions: {
-      auto ptr = reinterpret_cast<const tflite::BroadcastToOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_Rfft2dOptions: {
-      auto ptr = reinterpret_cast<const tflite::Rfft2dOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_Conv3DOptions: {
-      auto ptr = reinterpret_cast<const tflite::Conv3DOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_HashtableOptions: {
-      auto ptr = reinterpret_cast<const tflite::HashtableOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_HashtableFindOptions: {
-      auto ptr = reinterpret_cast<const tflite::HashtableFindOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_HashtableImportOptions: {
-      auto ptr = reinterpret_cast<const tflite::HashtableImportOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_HashtableSizeOptions: {
-      auto ptr = reinterpret_cast<const tflite::HashtableSizeOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_VarHandleOptions: {
-      auto ptr = reinterpret_cast<const tflite::VarHandleOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ReadVariableOptions: {
-      auto ptr = reinterpret_cast<const tflite::ReadVariableOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_AssignVariableOptions: {
-      auto ptr = reinterpret_cast<const tflite::AssignVariableOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_RandomOptions: {
-      auto ptr = reinterpret_cast<const tflite::RandomOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_BucketizeOptions: {
-      auto ptr = reinterpret_cast<const tflite::BucketizeOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_GeluOptions: {
-      auto ptr = reinterpret_cast<const tflite::GeluOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_DynamicUpdateSliceOptions: {
-      auto ptr = reinterpret_cast<const tflite::DynamicUpdateSliceOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_UnsortedSegmentProdOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnsortedSegmentProdOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_UnsortedSegmentMaxOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnsortedSegmentMaxOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_UnsortedSegmentMinOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnsortedSegmentMinOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_UnsortedSegmentSumOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnsortedSegmentSumOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_ATan2Options: {
-      auto ptr = reinterpret_cast<const tflite::ATan2Options *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    case BuiltinOptions_SignOptions: {
-      auto ptr = reinterpret_cast<const tflite::SignOptions *>(obj);
-      return ptr->UnPack(resolver);
-    }
-    default: return nullptr;
-  }
-}
-
-inline flatbuffers::Offset<void> BuiltinOptionsUnion::Pack(flatbuffers::FlatBufferBuilder &_fbb, const flatbuffers::rehasher_function_t *_rehasher) const {
-  (void)_rehasher;
-  switch (type) {
-    case BuiltinOptions_Conv2DOptions: {
-      auto ptr = reinterpret_cast<const tflite::Conv2DOptionsT *>(value);
-      return CreateConv2DOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_DepthwiseConv2DOptions: {
-      auto ptr = reinterpret_cast<const tflite::DepthwiseConv2DOptionsT *>(value);
-      return CreateDepthwiseConv2DOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ConcatEmbeddingsOptions: {
-      auto ptr = reinterpret_cast<const tflite::ConcatEmbeddingsOptionsT *>(value);
-      return CreateConcatEmbeddingsOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_LSHProjectionOptions: {
-      auto ptr = reinterpret_cast<const tflite::LSHProjectionOptionsT *>(value);
-      return CreateLSHProjectionOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_Pool2DOptions: {
-      auto ptr = reinterpret_cast<const tflite::Pool2DOptionsT *>(value);
-      return CreatePool2DOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SVDFOptions: {
-      auto ptr = reinterpret_cast<const tflite::SVDFOptionsT *>(value);
-      return CreateSVDFOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_RNNOptions: {
-      auto ptr = reinterpret_cast<const tflite::RNNOptionsT *>(value);
-      return CreateRNNOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_FullyConnectedOptions: {
-      auto ptr = reinterpret_cast<const tflite::FullyConnectedOptionsT *>(value);
-      return CreateFullyConnectedOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SoftmaxOptions: {
-      auto ptr = reinterpret_cast<const tflite::SoftmaxOptionsT *>(value);
-      return CreateSoftmaxOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ConcatenationOptions: {
-      auto ptr = reinterpret_cast<const tflite::ConcatenationOptionsT *>(value);
-      return CreateConcatenationOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_AddOptions: {
-      auto ptr = reinterpret_cast<const tflite::AddOptionsT *>(value);
-      return CreateAddOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_L2NormOptions: {
-      auto ptr = reinterpret_cast<const tflite::L2NormOptionsT *>(value);
-      return CreateL2NormOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_LocalResponseNormalizationOptions: {
-      auto ptr = reinterpret_cast<const tflite::LocalResponseNormalizationOptionsT *>(value);
-      return CreateLocalResponseNormalizationOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_LSTMOptions: {
-      auto ptr = reinterpret_cast<const tflite::LSTMOptionsT *>(value);
-      return CreateLSTMOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ResizeBilinearOptions: {
-      auto ptr = reinterpret_cast<const tflite::ResizeBilinearOptionsT *>(value);
-      return CreateResizeBilinearOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_CallOptions: {
-      auto ptr = reinterpret_cast<const tflite::CallOptionsT *>(value);
-      return CreateCallOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ReshapeOptions: {
-      auto ptr = reinterpret_cast<const tflite::ReshapeOptionsT *>(value);
-      return CreateReshapeOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SkipGramOptions: {
-      auto ptr = reinterpret_cast<const tflite::SkipGramOptionsT *>(value);
-      return CreateSkipGramOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SpaceToDepthOptions: {
-      auto ptr = reinterpret_cast<const tflite::SpaceToDepthOptionsT *>(value);
-      return CreateSpaceToDepthOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_EmbeddingLookupSparseOptions: {
-      auto ptr = reinterpret_cast<const tflite::EmbeddingLookupSparseOptionsT *>(value);
-      return CreateEmbeddingLookupSparseOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_MulOptions: {
-      auto ptr = reinterpret_cast<const tflite::MulOptionsT *>(value);
-      return CreateMulOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_PadOptions: {
-      auto ptr = reinterpret_cast<const tflite::PadOptionsT *>(value);
-      return CreatePadOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_GatherOptions: {
-      auto ptr = reinterpret_cast<const tflite::GatherOptionsT *>(value);
-      return CreateGatherOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_BatchToSpaceNDOptions: {
-      auto ptr = reinterpret_cast<const tflite::BatchToSpaceNDOptionsT *>(value);
-      return CreateBatchToSpaceNDOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SpaceToBatchNDOptions: {
-      auto ptr = reinterpret_cast<const tflite::SpaceToBatchNDOptionsT *>(value);
-      return CreateSpaceToBatchNDOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_TransposeOptions: {
-      auto ptr = reinterpret_cast<const tflite::TransposeOptionsT *>(value);
-      return CreateTransposeOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ReducerOptions: {
-      auto ptr = reinterpret_cast<const tflite::ReducerOptionsT *>(value);
-      return CreateReducerOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SubOptions: {
-      auto ptr = reinterpret_cast<const tflite::SubOptionsT *>(value);
-      return CreateSubOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_DivOptions: {
-      auto ptr = reinterpret_cast<const tflite::DivOptionsT *>(value);
-      return CreateDivOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SqueezeOptions: {
-      auto ptr = reinterpret_cast<const tflite::SqueezeOptionsT *>(value);
-      return CreateSqueezeOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SequenceRNNOptions: {
-      auto ptr = reinterpret_cast<const tflite::SequenceRNNOptionsT *>(value);
-      return CreateSequenceRNNOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_StridedSliceOptions: {
-      auto ptr = reinterpret_cast<const tflite::StridedSliceOptionsT *>(value);
-      return CreateStridedSliceOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ExpOptions: {
-      auto ptr = reinterpret_cast<const tflite::ExpOptionsT *>(value);
-      return CreateExpOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_TopKV2Options: {
-      auto ptr = reinterpret_cast<const tflite::TopKV2OptionsT *>(value);
-      return CreateTopKV2Options(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SplitOptions: {
-      auto ptr = reinterpret_cast<const tflite::SplitOptionsT *>(value);
-      return CreateSplitOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_LogSoftmaxOptions: {
-      auto ptr = reinterpret_cast<const tflite::LogSoftmaxOptionsT *>(value);
-      return CreateLogSoftmaxOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_CastOptions: {
-      auto ptr = reinterpret_cast<const tflite::CastOptionsT *>(value);
-      return CreateCastOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_DequantizeOptions: {
-      auto ptr = reinterpret_cast<const tflite::DequantizeOptionsT *>(value);
-      return CreateDequantizeOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_MaximumMinimumOptions: {
-      auto ptr = reinterpret_cast<const tflite::MaximumMinimumOptionsT *>(value);
-      return CreateMaximumMinimumOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ArgMaxOptions: {
-      auto ptr = reinterpret_cast<const tflite::ArgMaxOptionsT *>(value);
-      return CreateArgMaxOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_LessOptions: {
-      auto ptr = reinterpret_cast<const tflite::LessOptionsT *>(value);
-      return CreateLessOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_NegOptions: {
-      auto ptr = reinterpret_cast<const tflite::NegOptionsT *>(value);
-      return CreateNegOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_PadV2Options: {
-      auto ptr = reinterpret_cast<const tflite::PadV2OptionsT *>(value);
-      return CreatePadV2Options(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_GreaterOptions: {
-      auto ptr = reinterpret_cast<const tflite::GreaterOptionsT *>(value);
-      return CreateGreaterOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_GreaterEqualOptions: {
-      auto ptr = reinterpret_cast<const tflite::GreaterEqualOptionsT *>(value);
-      return CreateGreaterEqualOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_LessEqualOptions: {
-      auto ptr = reinterpret_cast<const tflite::LessEqualOptionsT *>(value);
-      return CreateLessEqualOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SelectOptions: {
-      auto ptr = reinterpret_cast<const tflite::SelectOptionsT *>(value);
-      return CreateSelectOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SliceOptions: {
-      auto ptr = reinterpret_cast<const tflite::SliceOptionsT *>(value);
-      return CreateSliceOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_TransposeConvOptions: {
-      auto ptr = reinterpret_cast<const tflite::TransposeConvOptionsT *>(value);
-      return CreateTransposeConvOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SparseToDenseOptions: {
-      auto ptr = reinterpret_cast<const tflite::SparseToDenseOptionsT *>(value);
-      return CreateSparseToDenseOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_TileOptions: {
-      auto ptr = reinterpret_cast<const tflite::TileOptionsT *>(value);
-      return CreateTileOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ExpandDimsOptions: {
-      auto ptr = reinterpret_cast<const tflite::ExpandDimsOptionsT *>(value);
-      return CreateExpandDimsOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_EqualOptions: {
-      auto ptr = reinterpret_cast<const tflite::EqualOptionsT *>(value);
-      return CreateEqualOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_NotEqualOptions: {
-      auto ptr = reinterpret_cast<const tflite::NotEqualOptionsT *>(value);
-      return CreateNotEqualOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ShapeOptions: {
-      auto ptr = reinterpret_cast<const tflite::ShapeOptionsT *>(value);
-      return CreateShapeOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_PowOptions: {
-      auto ptr = reinterpret_cast<const tflite::PowOptionsT *>(value);
-      return CreatePowOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ArgMinOptions: {
-      auto ptr = reinterpret_cast<const tflite::ArgMinOptionsT *>(value);
-      return CreateArgMinOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_FakeQuantOptions: {
-      auto ptr = reinterpret_cast<const tflite::FakeQuantOptionsT *>(value);
-      return CreateFakeQuantOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_PackOptions: {
-      auto ptr = reinterpret_cast<const tflite::PackOptionsT *>(value);
-      return CreatePackOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_LogicalOrOptions: {
-      auto ptr = reinterpret_cast<const tflite::LogicalOrOptionsT *>(value);
-      return CreateLogicalOrOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_OneHotOptions: {
-      auto ptr = reinterpret_cast<const tflite::OneHotOptionsT *>(value);
-      return CreateOneHotOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_LogicalAndOptions: {
-      auto ptr = reinterpret_cast<const tflite::LogicalAndOptionsT *>(value);
-      return CreateLogicalAndOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_LogicalNotOptions: {
-      auto ptr = reinterpret_cast<const tflite::LogicalNotOptionsT *>(value);
-      return CreateLogicalNotOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_UnpackOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnpackOptionsT *>(value);
-      return CreateUnpackOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_FloorDivOptions: {
-      auto ptr = reinterpret_cast<const tflite::FloorDivOptionsT *>(value);
-      return CreateFloorDivOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SquareOptions: {
-      auto ptr = reinterpret_cast<const tflite::SquareOptionsT *>(value);
-      return CreateSquareOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ZerosLikeOptions: {
-      auto ptr = reinterpret_cast<const tflite::ZerosLikeOptionsT *>(value);
-      return CreateZerosLikeOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_FillOptions: {
-      auto ptr = reinterpret_cast<const tflite::FillOptionsT *>(value);
-      return CreateFillOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_BidirectionalSequenceLSTMOptions: {
-      auto ptr = reinterpret_cast<const tflite::BidirectionalSequenceLSTMOptionsT *>(value);
-      return CreateBidirectionalSequenceLSTMOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_BidirectionalSequenceRNNOptions: {
-      auto ptr = reinterpret_cast<const tflite::BidirectionalSequenceRNNOptionsT *>(value);
-      return CreateBidirectionalSequenceRNNOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_UnidirectionalSequenceLSTMOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnidirectionalSequenceLSTMOptionsT *>(value);
-      return CreateUnidirectionalSequenceLSTMOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_FloorModOptions: {
-      auto ptr = reinterpret_cast<const tflite::FloorModOptionsT *>(value);
-      return CreateFloorModOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_RangeOptions: {
-      auto ptr = reinterpret_cast<const tflite::RangeOptionsT *>(value);
-      return CreateRangeOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ResizeNearestNeighborOptions: {
-      auto ptr = reinterpret_cast<const tflite::ResizeNearestNeighborOptionsT *>(value);
-      return CreateResizeNearestNeighborOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_LeakyReluOptions: {
-      auto ptr = reinterpret_cast<const tflite::LeakyReluOptionsT *>(value);
-      return CreateLeakyReluOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SquaredDifferenceOptions: {
-      auto ptr = reinterpret_cast<const tflite::SquaredDifferenceOptionsT *>(value);
-      return CreateSquaredDifferenceOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_MirrorPadOptions: {
-      auto ptr = reinterpret_cast<const tflite::MirrorPadOptionsT *>(value);
-      return CreateMirrorPadOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_AbsOptions: {
-      auto ptr = reinterpret_cast<const tflite::AbsOptionsT *>(value);
-      return CreateAbsOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SplitVOptions: {
-      auto ptr = reinterpret_cast<const tflite::SplitVOptionsT *>(value);
-      return CreateSplitVOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_UniqueOptions: {
-      auto ptr = reinterpret_cast<const tflite::UniqueOptionsT *>(value);
-      return CreateUniqueOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ReverseV2Options: {
-      auto ptr = reinterpret_cast<const tflite::ReverseV2OptionsT *>(value);
-      return CreateReverseV2Options(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_AddNOptions: {
-      auto ptr = reinterpret_cast<const tflite::AddNOptionsT *>(value);
-      return CreateAddNOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_GatherNdOptions: {
-      auto ptr = reinterpret_cast<const tflite::GatherNdOptionsT *>(value);
-      return CreateGatherNdOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_CosOptions: {
-      auto ptr = reinterpret_cast<const tflite::CosOptionsT *>(value);
-      return CreateCosOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_WhereOptions: {
-      auto ptr = reinterpret_cast<const tflite::WhereOptionsT *>(value);
-      return CreateWhereOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_RankOptions: {
-      auto ptr = reinterpret_cast<const tflite::RankOptionsT *>(value);
-      return CreateRankOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ReverseSequenceOptions: {
-      auto ptr = reinterpret_cast<const tflite::ReverseSequenceOptionsT *>(value);
-      return CreateReverseSequenceOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_MatrixDiagOptions: {
-      auto ptr = reinterpret_cast<const tflite::MatrixDiagOptionsT *>(value);
-      return CreateMatrixDiagOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_QuantizeOptions: {
-      auto ptr = reinterpret_cast<const tflite::QuantizeOptionsT *>(value);
-      return CreateQuantizeOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_MatrixSetDiagOptions: {
-      auto ptr = reinterpret_cast<const tflite::MatrixSetDiagOptionsT *>(value);
-      return CreateMatrixSetDiagOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_HardSwishOptions: {
-      auto ptr = reinterpret_cast<const tflite::HardSwishOptionsT *>(value);
-      return CreateHardSwishOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_IfOptions: {
-      auto ptr = reinterpret_cast<const tflite::IfOptionsT *>(value);
-      return CreateIfOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_WhileOptions: {
-      auto ptr = reinterpret_cast<const tflite::WhileOptionsT *>(value);
-      return CreateWhileOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_DepthToSpaceOptions: {
-      auto ptr = reinterpret_cast<const tflite::DepthToSpaceOptionsT *>(value);
-      return CreateDepthToSpaceOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_NonMaxSuppressionV4Options: {
-      auto ptr = reinterpret_cast<const tflite::NonMaxSuppressionV4OptionsT *>(value);
-      return CreateNonMaxSuppressionV4Options(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_NonMaxSuppressionV5Options: {
-      auto ptr = reinterpret_cast<const tflite::NonMaxSuppressionV5OptionsT *>(value);
-      return CreateNonMaxSuppressionV5Options(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ScatterNdOptions: {
-      auto ptr = reinterpret_cast<const tflite::ScatterNdOptionsT *>(value);
-      return CreateScatterNdOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SelectV2Options: {
-      auto ptr = reinterpret_cast<const tflite::SelectV2OptionsT *>(value);
-      return CreateSelectV2Options(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_DensifyOptions: {
-      auto ptr = reinterpret_cast<const tflite::DensifyOptionsT *>(value);
-      return CreateDensifyOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SegmentSumOptions: {
-      auto ptr = reinterpret_cast<const tflite::SegmentSumOptionsT *>(value);
-      return CreateSegmentSumOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_BatchMatMulOptions: {
-      auto ptr = reinterpret_cast<const tflite::BatchMatMulOptionsT *>(value);
-      return CreateBatchMatMulOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_CumsumOptions: {
-      auto ptr = reinterpret_cast<const tflite::CumsumOptionsT *>(value);
-      return CreateCumsumOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_CallOnceOptions: {
-      auto ptr = reinterpret_cast<const tflite::CallOnceOptionsT *>(value);
-      return CreateCallOnceOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_BroadcastToOptions: {
-      auto ptr = reinterpret_cast<const tflite::BroadcastToOptionsT *>(value);
-      return CreateBroadcastToOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_Rfft2dOptions: {
-      auto ptr = reinterpret_cast<const tflite::Rfft2dOptionsT *>(value);
-      return CreateRfft2dOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_Conv3DOptions: {
-      auto ptr = reinterpret_cast<const tflite::Conv3DOptionsT *>(value);
-      return CreateConv3DOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_HashtableOptions: {
-      auto ptr = reinterpret_cast<const tflite::HashtableOptionsT *>(value);
-      return CreateHashtableOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_HashtableFindOptions: {
-      auto ptr = reinterpret_cast<const tflite::HashtableFindOptionsT *>(value);
-      return CreateHashtableFindOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_HashtableImportOptions: {
-      auto ptr = reinterpret_cast<const tflite::HashtableImportOptionsT *>(value);
-      return CreateHashtableImportOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_HashtableSizeOptions: {
-      auto ptr = reinterpret_cast<const tflite::HashtableSizeOptionsT *>(value);
-      return CreateHashtableSizeOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_VarHandleOptions: {
-      auto ptr = reinterpret_cast<const tflite::VarHandleOptionsT *>(value);
-      return CreateVarHandleOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ReadVariableOptions: {
-      auto ptr = reinterpret_cast<const tflite::ReadVariableOptionsT *>(value);
-      return CreateReadVariableOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_AssignVariableOptions: {
-      auto ptr = reinterpret_cast<const tflite::AssignVariableOptionsT *>(value);
-      return CreateAssignVariableOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_RandomOptions: {
-      auto ptr = reinterpret_cast<const tflite::RandomOptionsT *>(value);
-      return CreateRandomOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_BucketizeOptions: {
-      auto ptr = reinterpret_cast<const tflite::BucketizeOptionsT *>(value);
-      return CreateBucketizeOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_GeluOptions: {
-      auto ptr = reinterpret_cast<const tflite::GeluOptionsT *>(value);
-      return CreateGeluOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_DynamicUpdateSliceOptions: {
-      auto ptr = reinterpret_cast<const tflite::DynamicUpdateSliceOptionsT *>(value);
-      return CreateDynamicUpdateSliceOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_UnsortedSegmentProdOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnsortedSegmentProdOptionsT *>(value);
-      return CreateUnsortedSegmentProdOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_UnsortedSegmentMaxOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnsortedSegmentMaxOptionsT *>(value);
-      return CreateUnsortedSegmentMaxOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_UnsortedSegmentMinOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnsortedSegmentMinOptionsT *>(value);
-      return CreateUnsortedSegmentMinOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_UnsortedSegmentSumOptions: {
-      auto ptr = reinterpret_cast<const tflite::UnsortedSegmentSumOptionsT *>(value);
-      return CreateUnsortedSegmentSumOptions(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_ATan2Options: {
-      auto ptr = reinterpret_cast<const tflite::ATan2OptionsT *>(value);
-      return CreateATan2Options(_fbb, ptr, _rehasher).Union();
-    }
-    case BuiltinOptions_SignOptions: {
-      auto ptr = reinterpret_cast<const tflite::SignOptionsT *>(value);
-      return CreateSignOptions(_fbb, ptr, _rehasher).Union();
-    }
-    default: return 0;
-  }
-}
-
-inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) : type(u.type), value(nullptr) {
-  switch (type) {
-    case BuiltinOptions_Conv2DOptions: {
-      value = new tflite::Conv2DOptionsT(*reinterpret_cast<tflite::Conv2DOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_DepthwiseConv2DOptions: {
-      value = new tflite::DepthwiseConv2DOptionsT(*reinterpret_cast<tflite::DepthwiseConv2DOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ConcatEmbeddingsOptions: {
-      value = new tflite::ConcatEmbeddingsOptionsT(*reinterpret_cast<tflite::ConcatEmbeddingsOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_LSHProjectionOptions: {
-      value = new tflite::LSHProjectionOptionsT(*reinterpret_cast<tflite::LSHProjectionOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_Pool2DOptions: {
-      value = new tflite::Pool2DOptionsT(*reinterpret_cast<tflite::Pool2DOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SVDFOptions: {
-      value = new tflite::SVDFOptionsT(*reinterpret_cast<tflite::SVDFOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_RNNOptions: {
-      value = new tflite::RNNOptionsT(*reinterpret_cast<tflite::RNNOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_FullyConnectedOptions: {
-      value = new tflite::FullyConnectedOptionsT(*reinterpret_cast<tflite::FullyConnectedOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SoftmaxOptions: {
-      value = new tflite::SoftmaxOptionsT(*reinterpret_cast<tflite::SoftmaxOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ConcatenationOptions: {
-      value = new tflite::ConcatenationOptionsT(*reinterpret_cast<tflite::ConcatenationOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_AddOptions: {
-      value = new tflite::AddOptionsT(*reinterpret_cast<tflite::AddOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_L2NormOptions: {
-      value = new tflite::L2NormOptionsT(*reinterpret_cast<tflite::L2NormOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_LocalResponseNormalizationOptions: {
-      value = new tflite::LocalResponseNormalizationOptionsT(*reinterpret_cast<tflite::LocalResponseNormalizationOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_LSTMOptions: {
-      value = new tflite::LSTMOptionsT(*reinterpret_cast<tflite::LSTMOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ResizeBilinearOptions: {
-      value = new tflite::ResizeBilinearOptionsT(*reinterpret_cast<tflite::ResizeBilinearOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_CallOptions: {
-      value = new tflite::CallOptionsT(*reinterpret_cast<tflite::CallOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ReshapeOptions: {
-      value = new tflite::ReshapeOptionsT(*reinterpret_cast<tflite::ReshapeOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SkipGramOptions: {
-      value = new tflite::SkipGramOptionsT(*reinterpret_cast<tflite::SkipGramOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SpaceToDepthOptions: {
-      value = new tflite::SpaceToDepthOptionsT(*reinterpret_cast<tflite::SpaceToDepthOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_EmbeddingLookupSparseOptions: {
-      value = new tflite::EmbeddingLookupSparseOptionsT(*reinterpret_cast<tflite::EmbeddingLookupSparseOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_MulOptions: {
-      value = new tflite::MulOptionsT(*reinterpret_cast<tflite::MulOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_PadOptions: {
-      value = new tflite::PadOptionsT(*reinterpret_cast<tflite::PadOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_GatherOptions: {
-      value = new tflite::GatherOptionsT(*reinterpret_cast<tflite::GatherOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_BatchToSpaceNDOptions: {
-      value = new tflite::BatchToSpaceNDOptionsT(*reinterpret_cast<tflite::BatchToSpaceNDOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SpaceToBatchNDOptions: {
-      value = new tflite::SpaceToBatchNDOptionsT(*reinterpret_cast<tflite::SpaceToBatchNDOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_TransposeOptions: {
-      value = new tflite::TransposeOptionsT(*reinterpret_cast<tflite::TransposeOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ReducerOptions: {
-      value = new tflite::ReducerOptionsT(*reinterpret_cast<tflite::ReducerOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SubOptions: {
-      value = new tflite::SubOptionsT(*reinterpret_cast<tflite::SubOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_DivOptions: {
-      value = new tflite::DivOptionsT(*reinterpret_cast<tflite::DivOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SqueezeOptions: {
-      value = new tflite::SqueezeOptionsT(*reinterpret_cast<tflite::SqueezeOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SequenceRNNOptions: {
-      value = new tflite::SequenceRNNOptionsT(*reinterpret_cast<tflite::SequenceRNNOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_StridedSliceOptions: {
-      value = new tflite::StridedSliceOptionsT(*reinterpret_cast<tflite::StridedSliceOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ExpOptions: {
-      value = new tflite::ExpOptionsT(*reinterpret_cast<tflite::ExpOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_TopKV2Options: {
-      value = new tflite::TopKV2OptionsT(*reinterpret_cast<tflite::TopKV2OptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SplitOptions: {
-      value = new tflite::SplitOptionsT(*reinterpret_cast<tflite::SplitOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_LogSoftmaxOptions: {
-      value = new tflite::LogSoftmaxOptionsT(*reinterpret_cast<tflite::LogSoftmaxOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_CastOptions: {
-      value = new tflite::CastOptionsT(*reinterpret_cast<tflite::CastOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_DequantizeOptions: {
-      value = new tflite::DequantizeOptionsT(*reinterpret_cast<tflite::DequantizeOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_MaximumMinimumOptions: {
-      value = new tflite::MaximumMinimumOptionsT(*reinterpret_cast<tflite::MaximumMinimumOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ArgMaxOptions: {
-      value = new tflite::ArgMaxOptionsT(*reinterpret_cast<tflite::ArgMaxOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_LessOptions: {
-      value = new tflite::LessOptionsT(*reinterpret_cast<tflite::LessOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_NegOptions: {
-      value = new tflite::NegOptionsT(*reinterpret_cast<tflite::NegOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_PadV2Options: {
-      value = new tflite::PadV2OptionsT(*reinterpret_cast<tflite::PadV2OptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_GreaterOptions: {
-      value = new tflite::GreaterOptionsT(*reinterpret_cast<tflite::GreaterOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_GreaterEqualOptions: {
-      value = new tflite::GreaterEqualOptionsT(*reinterpret_cast<tflite::GreaterEqualOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_LessEqualOptions: {
-      value = new tflite::LessEqualOptionsT(*reinterpret_cast<tflite::LessEqualOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SelectOptions: {
-      value = new tflite::SelectOptionsT(*reinterpret_cast<tflite::SelectOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SliceOptions: {
-      value = new tflite::SliceOptionsT(*reinterpret_cast<tflite::SliceOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_TransposeConvOptions: {
-      value = new tflite::TransposeConvOptionsT(*reinterpret_cast<tflite::TransposeConvOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SparseToDenseOptions: {
-      value = new tflite::SparseToDenseOptionsT(*reinterpret_cast<tflite::SparseToDenseOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_TileOptions: {
-      value = new tflite::TileOptionsT(*reinterpret_cast<tflite::TileOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ExpandDimsOptions: {
-      value = new tflite::ExpandDimsOptionsT(*reinterpret_cast<tflite::ExpandDimsOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_EqualOptions: {
-      value = new tflite::EqualOptionsT(*reinterpret_cast<tflite::EqualOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_NotEqualOptions: {
-      value = new tflite::NotEqualOptionsT(*reinterpret_cast<tflite::NotEqualOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ShapeOptions: {
-      value = new tflite::ShapeOptionsT(*reinterpret_cast<tflite::ShapeOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_PowOptions: {
-      value = new tflite::PowOptionsT(*reinterpret_cast<tflite::PowOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ArgMinOptions: {
-      value = new tflite::ArgMinOptionsT(*reinterpret_cast<tflite::ArgMinOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_FakeQuantOptions: {
-      value = new tflite::FakeQuantOptionsT(*reinterpret_cast<tflite::FakeQuantOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_PackOptions: {
-      value = new tflite::PackOptionsT(*reinterpret_cast<tflite::PackOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_LogicalOrOptions: {
-      value = new tflite::LogicalOrOptionsT(*reinterpret_cast<tflite::LogicalOrOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_OneHotOptions: {
-      value = new tflite::OneHotOptionsT(*reinterpret_cast<tflite::OneHotOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_LogicalAndOptions: {
-      value = new tflite::LogicalAndOptionsT(*reinterpret_cast<tflite::LogicalAndOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_LogicalNotOptions: {
-      value = new tflite::LogicalNotOptionsT(*reinterpret_cast<tflite::LogicalNotOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_UnpackOptions: {
-      value = new tflite::UnpackOptionsT(*reinterpret_cast<tflite::UnpackOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_FloorDivOptions: {
-      value = new tflite::FloorDivOptionsT(*reinterpret_cast<tflite::FloorDivOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SquareOptions: {
-      value = new tflite::SquareOptionsT(*reinterpret_cast<tflite::SquareOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ZerosLikeOptions: {
-      value = new tflite::ZerosLikeOptionsT(*reinterpret_cast<tflite::ZerosLikeOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_FillOptions: {
-      value = new tflite::FillOptionsT(*reinterpret_cast<tflite::FillOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_BidirectionalSequenceLSTMOptions: {
-      value = new tflite::BidirectionalSequenceLSTMOptionsT(*reinterpret_cast<tflite::BidirectionalSequenceLSTMOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_BidirectionalSequenceRNNOptions: {
-      value = new tflite::BidirectionalSequenceRNNOptionsT(*reinterpret_cast<tflite::BidirectionalSequenceRNNOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_UnidirectionalSequenceLSTMOptions: {
-      value = new tflite::UnidirectionalSequenceLSTMOptionsT(*reinterpret_cast<tflite::UnidirectionalSequenceLSTMOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_FloorModOptions: {
-      value = new tflite::FloorModOptionsT(*reinterpret_cast<tflite::FloorModOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_RangeOptions: {
-      value = new tflite::RangeOptionsT(*reinterpret_cast<tflite::RangeOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ResizeNearestNeighborOptions: {
-      value = new tflite::ResizeNearestNeighborOptionsT(*reinterpret_cast<tflite::ResizeNearestNeighborOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_LeakyReluOptions: {
-      value = new tflite::LeakyReluOptionsT(*reinterpret_cast<tflite::LeakyReluOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SquaredDifferenceOptions: {
-      value = new tflite::SquaredDifferenceOptionsT(*reinterpret_cast<tflite::SquaredDifferenceOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_MirrorPadOptions: {
-      value = new tflite::MirrorPadOptionsT(*reinterpret_cast<tflite::MirrorPadOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_AbsOptions: {
-      value = new tflite::AbsOptionsT(*reinterpret_cast<tflite::AbsOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SplitVOptions: {
-      value = new tflite::SplitVOptionsT(*reinterpret_cast<tflite::SplitVOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_UniqueOptions: {
-      value = new tflite::UniqueOptionsT(*reinterpret_cast<tflite::UniqueOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ReverseV2Options: {
-      value = new tflite::ReverseV2OptionsT(*reinterpret_cast<tflite::ReverseV2OptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_AddNOptions: {
-      value = new tflite::AddNOptionsT(*reinterpret_cast<tflite::AddNOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_GatherNdOptions: {
-      value = new tflite::GatherNdOptionsT(*reinterpret_cast<tflite::GatherNdOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_CosOptions: {
-      value = new tflite::CosOptionsT(*reinterpret_cast<tflite::CosOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_WhereOptions: {
-      value = new tflite::WhereOptionsT(*reinterpret_cast<tflite::WhereOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_RankOptions: {
-      value = new tflite::RankOptionsT(*reinterpret_cast<tflite::RankOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ReverseSequenceOptions: {
-      value = new tflite::ReverseSequenceOptionsT(*reinterpret_cast<tflite::ReverseSequenceOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_MatrixDiagOptions: {
-      value = new tflite::MatrixDiagOptionsT(*reinterpret_cast<tflite::MatrixDiagOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_QuantizeOptions: {
-      value = new tflite::QuantizeOptionsT(*reinterpret_cast<tflite::QuantizeOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_MatrixSetDiagOptions: {
-      value = new tflite::MatrixSetDiagOptionsT(*reinterpret_cast<tflite::MatrixSetDiagOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_HardSwishOptions: {
-      value = new tflite::HardSwishOptionsT(*reinterpret_cast<tflite::HardSwishOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_IfOptions: {
-      value = new tflite::IfOptionsT(*reinterpret_cast<tflite::IfOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_WhileOptions: {
-      value = new tflite::WhileOptionsT(*reinterpret_cast<tflite::WhileOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_DepthToSpaceOptions: {
-      value = new tflite::DepthToSpaceOptionsT(*reinterpret_cast<tflite::DepthToSpaceOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_NonMaxSuppressionV4Options: {
-      value = new tflite::NonMaxSuppressionV4OptionsT(*reinterpret_cast<tflite::NonMaxSuppressionV4OptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_NonMaxSuppressionV5Options: {
-      value = new tflite::NonMaxSuppressionV5OptionsT(*reinterpret_cast<tflite::NonMaxSuppressionV5OptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ScatterNdOptions: {
-      value = new tflite::ScatterNdOptionsT(*reinterpret_cast<tflite::ScatterNdOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SelectV2Options: {
-      value = new tflite::SelectV2OptionsT(*reinterpret_cast<tflite::SelectV2OptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_DensifyOptions: {
-      value = new tflite::DensifyOptionsT(*reinterpret_cast<tflite::DensifyOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SegmentSumOptions: {
-      value = new tflite::SegmentSumOptionsT(*reinterpret_cast<tflite::SegmentSumOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_BatchMatMulOptions: {
-      value = new tflite::BatchMatMulOptionsT(*reinterpret_cast<tflite::BatchMatMulOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_CumsumOptions: {
-      value = new tflite::CumsumOptionsT(*reinterpret_cast<tflite::CumsumOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_CallOnceOptions: {
-      value = new tflite::CallOnceOptionsT(*reinterpret_cast<tflite::CallOnceOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_BroadcastToOptions: {
-      value = new tflite::BroadcastToOptionsT(*reinterpret_cast<tflite::BroadcastToOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_Rfft2dOptions: {
-      value = new tflite::Rfft2dOptionsT(*reinterpret_cast<tflite::Rfft2dOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_Conv3DOptions: {
-      value = new tflite::Conv3DOptionsT(*reinterpret_cast<tflite::Conv3DOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_HashtableOptions: {
-      value = new tflite::HashtableOptionsT(*reinterpret_cast<tflite::HashtableOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_HashtableFindOptions: {
-      value = new tflite::HashtableFindOptionsT(*reinterpret_cast<tflite::HashtableFindOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_HashtableImportOptions: {
-      value = new tflite::HashtableImportOptionsT(*reinterpret_cast<tflite::HashtableImportOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_HashtableSizeOptions: {
-      value = new tflite::HashtableSizeOptionsT(*reinterpret_cast<tflite::HashtableSizeOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_VarHandleOptions: {
-      value = new tflite::VarHandleOptionsT(*reinterpret_cast<tflite::VarHandleOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ReadVariableOptions: {
-      value = new tflite::ReadVariableOptionsT(*reinterpret_cast<tflite::ReadVariableOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_AssignVariableOptions: {
-      value = new tflite::AssignVariableOptionsT(*reinterpret_cast<tflite::AssignVariableOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_RandomOptions: {
-      value = new tflite::RandomOptionsT(*reinterpret_cast<tflite::RandomOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_BucketizeOptions: {
-      value = new tflite::BucketizeOptionsT(*reinterpret_cast<tflite::BucketizeOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_GeluOptions: {
-      value = new tflite::GeluOptionsT(*reinterpret_cast<tflite::GeluOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_DynamicUpdateSliceOptions: {
-      value = new tflite::DynamicUpdateSliceOptionsT(*reinterpret_cast<tflite::DynamicUpdateSliceOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_UnsortedSegmentProdOptions: {
-      value = new tflite::UnsortedSegmentProdOptionsT(*reinterpret_cast<tflite::UnsortedSegmentProdOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_UnsortedSegmentMaxOptions: {
-      value = new tflite::UnsortedSegmentMaxOptionsT(*reinterpret_cast<tflite::UnsortedSegmentMaxOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_UnsortedSegmentMinOptions: {
-      value = new tflite::UnsortedSegmentMinOptionsT(*reinterpret_cast<tflite::UnsortedSegmentMinOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_UnsortedSegmentSumOptions: {
-      value = new tflite::UnsortedSegmentSumOptionsT(*reinterpret_cast<tflite::UnsortedSegmentSumOptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_ATan2Options: {
-      value = new tflite::ATan2OptionsT(*reinterpret_cast<tflite::ATan2OptionsT *>(u.value));
-      break;
-    }
-    case BuiltinOptions_SignOptions: {
-      value = new tflite::SignOptionsT(*reinterpret_cast<tflite::SignOptionsT *>(u.value));
-      break;
-    }
-    default:
-      break;
-  }
-}
-
-inline void BuiltinOptionsUnion::Reset() {
-  switch (type) {
-    case BuiltinOptions_Conv2DOptions: {
-      auto ptr = reinterpret_cast<tflite::Conv2DOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_DepthwiseConv2DOptions: {
-      auto ptr = reinterpret_cast<tflite::DepthwiseConv2DOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ConcatEmbeddingsOptions: {
-      auto ptr = reinterpret_cast<tflite::ConcatEmbeddingsOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_LSHProjectionOptions: {
-      auto ptr = reinterpret_cast<tflite::LSHProjectionOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_Pool2DOptions: {
-      auto ptr = reinterpret_cast<tflite::Pool2DOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SVDFOptions: {
-      auto ptr = reinterpret_cast<tflite::SVDFOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_RNNOptions: {
-      auto ptr = reinterpret_cast<tflite::RNNOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_FullyConnectedOptions: {
-      auto ptr = reinterpret_cast<tflite::FullyConnectedOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SoftmaxOptions: {
-      auto ptr = reinterpret_cast<tflite::SoftmaxOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ConcatenationOptions: {
-      auto ptr = reinterpret_cast<tflite::ConcatenationOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_AddOptions: {
-      auto ptr = reinterpret_cast<tflite::AddOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_L2NormOptions: {
-      auto ptr = reinterpret_cast<tflite::L2NormOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_LocalResponseNormalizationOptions: {
-      auto ptr = reinterpret_cast<tflite::LocalResponseNormalizationOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_LSTMOptions: {
-      auto ptr = reinterpret_cast<tflite::LSTMOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ResizeBilinearOptions: {
-      auto ptr = reinterpret_cast<tflite::ResizeBilinearOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_CallOptions: {
-      auto ptr = reinterpret_cast<tflite::CallOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ReshapeOptions: {
-      auto ptr = reinterpret_cast<tflite::ReshapeOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SkipGramOptions: {
-      auto ptr = reinterpret_cast<tflite::SkipGramOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SpaceToDepthOptions: {
-      auto ptr = reinterpret_cast<tflite::SpaceToDepthOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_EmbeddingLookupSparseOptions: {
-      auto ptr = reinterpret_cast<tflite::EmbeddingLookupSparseOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_MulOptions: {
-      auto ptr = reinterpret_cast<tflite::MulOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_PadOptions: {
-      auto ptr = reinterpret_cast<tflite::PadOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_GatherOptions: {
-      auto ptr = reinterpret_cast<tflite::GatherOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_BatchToSpaceNDOptions: {
-      auto ptr = reinterpret_cast<tflite::BatchToSpaceNDOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SpaceToBatchNDOptions: {
-      auto ptr = reinterpret_cast<tflite::SpaceToBatchNDOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_TransposeOptions: {
-      auto ptr = reinterpret_cast<tflite::TransposeOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ReducerOptions: {
-      auto ptr = reinterpret_cast<tflite::ReducerOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SubOptions: {
-      auto ptr = reinterpret_cast<tflite::SubOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_DivOptions: {
-      auto ptr = reinterpret_cast<tflite::DivOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SqueezeOptions: {
-      auto ptr = reinterpret_cast<tflite::SqueezeOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SequenceRNNOptions: {
-      auto ptr = reinterpret_cast<tflite::SequenceRNNOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_StridedSliceOptions: {
-      auto ptr = reinterpret_cast<tflite::StridedSliceOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ExpOptions: {
-      auto ptr = reinterpret_cast<tflite::ExpOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_TopKV2Options: {
-      auto ptr = reinterpret_cast<tflite::TopKV2OptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SplitOptions: {
-      auto ptr = reinterpret_cast<tflite::SplitOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_LogSoftmaxOptions: {
-      auto ptr = reinterpret_cast<tflite::LogSoftmaxOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_CastOptions: {
-      auto ptr = reinterpret_cast<tflite::CastOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_DequantizeOptions: {
-      auto ptr = reinterpret_cast<tflite::DequantizeOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_MaximumMinimumOptions: {
-      auto ptr = reinterpret_cast<tflite::MaximumMinimumOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ArgMaxOptions: {
-      auto ptr = reinterpret_cast<tflite::ArgMaxOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_LessOptions: {
-      auto ptr = reinterpret_cast<tflite::LessOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_NegOptions: {
-      auto ptr = reinterpret_cast<tflite::NegOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_PadV2Options: {
-      auto ptr = reinterpret_cast<tflite::PadV2OptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_GreaterOptions: {
-      auto ptr = reinterpret_cast<tflite::GreaterOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_GreaterEqualOptions: {
-      auto ptr = reinterpret_cast<tflite::GreaterEqualOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_LessEqualOptions: {
-      auto ptr = reinterpret_cast<tflite::LessEqualOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SelectOptions: {
-      auto ptr = reinterpret_cast<tflite::SelectOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SliceOptions: {
-      auto ptr = reinterpret_cast<tflite::SliceOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_TransposeConvOptions: {
-      auto ptr = reinterpret_cast<tflite::TransposeConvOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SparseToDenseOptions: {
-      auto ptr = reinterpret_cast<tflite::SparseToDenseOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_TileOptions: {
-      auto ptr = reinterpret_cast<tflite::TileOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ExpandDimsOptions: {
-      auto ptr = reinterpret_cast<tflite::ExpandDimsOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_EqualOptions: {
-      auto ptr = reinterpret_cast<tflite::EqualOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_NotEqualOptions: {
-      auto ptr = reinterpret_cast<tflite::NotEqualOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ShapeOptions: {
-      auto ptr = reinterpret_cast<tflite::ShapeOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_PowOptions: {
-      auto ptr = reinterpret_cast<tflite::PowOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ArgMinOptions: {
-      auto ptr = reinterpret_cast<tflite::ArgMinOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_FakeQuantOptions: {
-      auto ptr = reinterpret_cast<tflite::FakeQuantOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_PackOptions: {
-      auto ptr = reinterpret_cast<tflite::PackOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_LogicalOrOptions: {
-      auto ptr = reinterpret_cast<tflite::LogicalOrOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_OneHotOptions: {
-      auto ptr = reinterpret_cast<tflite::OneHotOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_LogicalAndOptions: {
-      auto ptr = reinterpret_cast<tflite::LogicalAndOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_LogicalNotOptions: {
-      auto ptr = reinterpret_cast<tflite::LogicalNotOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_UnpackOptions: {
-      auto ptr = reinterpret_cast<tflite::UnpackOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_FloorDivOptions: {
-      auto ptr = reinterpret_cast<tflite::FloorDivOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SquareOptions: {
-      auto ptr = reinterpret_cast<tflite::SquareOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ZerosLikeOptions: {
-      auto ptr = reinterpret_cast<tflite::ZerosLikeOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_FillOptions: {
-      auto ptr = reinterpret_cast<tflite::FillOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_BidirectionalSequenceLSTMOptions: {
-      auto ptr = reinterpret_cast<tflite::BidirectionalSequenceLSTMOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_BidirectionalSequenceRNNOptions: {
-      auto ptr = reinterpret_cast<tflite::BidirectionalSequenceRNNOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_UnidirectionalSequenceLSTMOptions: {
-      auto ptr = reinterpret_cast<tflite::UnidirectionalSequenceLSTMOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_FloorModOptions: {
-      auto ptr = reinterpret_cast<tflite::FloorModOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_RangeOptions: {
-      auto ptr = reinterpret_cast<tflite::RangeOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ResizeNearestNeighborOptions: {
-      auto ptr = reinterpret_cast<tflite::ResizeNearestNeighborOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_LeakyReluOptions: {
-      auto ptr = reinterpret_cast<tflite::LeakyReluOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SquaredDifferenceOptions: {
-      auto ptr = reinterpret_cast<tflite::SquaredDifferenceOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_MirrorPadOptions: {
-      auto ptr = reinterpret_cast<tflite::MirrorPadOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_AbsOptions: {
-      auto ptr = reinterpret_cast<tflite::AbsOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SplitVOptions: {
-      auto ptr = reinterpret_cast<tflite::SplitVOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_UniqueOptions: {
-      auto ptr = reinterpret_cast<tflite::UniqueOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ReverseV2Options: {
-      auto ptr = reinterpret_cast<tflite::ReverseV2OptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_AddNOptions: {
-      auto ptr = reinterpret_cast<tflite::AddNOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_GatherNdOptions: {
-      auto ptr = reinterpret_cast<tflite::GatherNdOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_CosOptions: {
-      auto ptr = reinterpret_cast<tflite::CosOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_WhereOptions: {
-      auto ptr = reinterpret_cast<tflite::WhereOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_RankOptions: {
-      auto ptr = reinterpret_cast<tflite::RankOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ReverseSequenceOptions: {
-      auto ptr = reinterpret_cast<tflite::ReverseSequenceOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_MatrixDiagOptions: {
-      auto ptr = reinterpret_cast<tflite::MatrixDiagOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_QuantizeOptions: {
-      auto ptr = reinterpret_cast<tflite::QuantizeOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_MatrixSetDiagOptions: {
-      auto ptr = reinterpret_cast<tflite::MatrixSetDiagOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_HardSwishOptions: {
-      auto ptr = reinterpret_cast<tflite::HardSwishOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_IfOptions: {
-      auto ptr = reinterpret_cast<tflite::IfOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_WhileOptions: {
-      auto ptr = reinterpret_cast<tflite::WhileOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_DepthToSpaceOptions: {
-      auto ptr = reinterpret_cast<tflite::DepthToSpaceOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_NonMaxSuppressionV4Options: {
-      auto ptr = reinterpret_cast<tflite::NonMaxSuppressionV4OptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_NonMaxSuppressionV5Options: {
-      auto ptr = reinterpret_cast<tflite::NonMaxSuppressionV5OptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ScatterNdOptions: {
-      auto ptr = reinterpret_cast<tflite::ScatterNdOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SelectV2Options: {
-      auto ptr = reinterpret_cast<tflite::SelectV2OptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_DensifyOptions: {
-      auto ptr = reinterpret_cast<tflite::DensifyOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SegmentSumOptions: {
-      auto ptr = reinterpret_cast<tflite::SegmentSumOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_BatchMatMulOptions: {
-      auto ptr = reinterpret_cast<tflite::BatchMatMulOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_CumsumOptions: {
-      auto ptr = reinterpret_cast<tflite::CumsumOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_CallOnceOptions: {
-      auto ptr = reinterpret_cast<tflite::CallOnceOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_BroadcastToOptions: {
-      auto ptr = reinterpret_cast<tflite::BroadcastToOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_Rfft2dOptions: {
-      auto ptr = reinterpret_cast<tflite::Rfft2dOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_Conv3DOptions: {
-      auto ptr = reinterpret_cast<tflite::Conv3DOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_HashtableOptions: {
-      auto ptr = reinterpret_cast<tflite::HashtableOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_HashtableFindOptions: {
-      auto ptr = reinterpret_cast<tflite::HashtableFindOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_HashtableImportOptions: {
-      auto ptr = reinterpret_cast<tflite::HashtableImportOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_HashtableSizeOptions: {
-      auto ptr = reinterpret_cast<tflite::HashtableSizeOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_VarHandleOptions: {
-      auto ptr = reinterpret_cast<tflite::VarHandleOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ReadVariableOptions: {
-      auto ptr = reinterpret_cast<tflite::ReadVariableOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_AssignVariableOptions: {
-      auto ptr = reinterpret_cast<tflite::AssignVariableOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_RandomOptions: {
-      auto ptr = reinterpret_cast<tflite::RandomOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_BucketizeOptions: {
-      auto ptr = reinterpret_cast<tflite::BucketizeOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_GeluOptions: {
-      auto ptr = reinterpret_cast<tflite::GeluOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_DynamicUpdateSliceOptions: {
-      auto ptr = reinterpret_cast<tflite::DynamicUpdateSliceOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_UnsortedSegmentProdOptions: {
-      auto ptr = reinterpret_cast<tflite::UnsortedSegmentProdOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_UnsortedSegmentMaxOptions: {
-      auto ptr = reinterpret_cast<tflite::UnsortedSegmentMaxOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_UnsortedSegmentMinOptions: {
-      auto ptr = reinterpret_cast<tflite::UnsortedSegmentMinOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_UnsortedSegmentSumOptions: {
-      auto ptr = reinterpret_cast<tflite::UnsortedSegmentSumOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_ATan2Options: {
-      auto ptr = reinterpret_cast<tflite::ATan2OptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    case BuiltinOptions_SignOptions: {
-      auto ptr = reinterpret_cast<tflite::SignOptionsT *>(value);
-      delete ptr;
-      break;
-    }
-    default: break;
-  }
-  value = nullptr;
-  type = BuiltinOptions_NONE;
-}
-
-inline const tflite::Model *GetModel(const void *buf) {
-  return flatbuffers::GetRoot<tflite::Model>(buf);
-}
-
-inline const tflite::Model *GetSizePrefixedModel(const void *buf) {
-  return flatbuffers::GetSizePrefixedRoot<tflite::Model>(buf);
-}
-
-inline const char *ModelIdentifier() {
-  return "TFL3";
-}
-
-inline bool ModelBufferHasIdentifier(const void *buf) {
-  return flatbuffers::BufferHasIdentifier(
-      buf, ModelIdentifier());
-}
-
-inline bool SizePrefixedModelBufferHasIdentifier(const void *buf) {
-  return flatbuffers::BufferHasIdentifier(
-      buf, ModelIdentifier(), true);
-}
-
-inline bool VerifyModelBuffer(
-    flatbuffers::Verifier &verifier) {
-  return verifier.VerifyBuffer<tflite::Model>(ModelIdentifier());
-}
-
-inline bool VerifySizePrefixedModelBuffer(
-    flatbuffers::Verifier &verifier) {
-  return verifier.VerifySizePrefixedBuffer<tflite::Model>(ModelIdentifier());
-}
-
-inline const char *ModelExtension() {
-  return "tflite";
-}
-
-inline void FinishModelBuffer(
-    flatbuffers::FlatBufferBuilder &fbb,
-    flatbuffers::Offset<tflite::Model> root) {
-  fbb.Finish(root, ModelIdentifier());
-}
-
-inline void FinishSizePrefixedModelBuffer(
-    flatbuffers::FlatBufferBuilder &fbb,
-    flatbuffers::Offset<tflite::Model> root) {
-  fbb.FinishSizePrefixed(root, ModelIdentifier());
-}
-
-inline std::unique_ptr<tflite::ModelT> UnPackModel(
-    const void *buf,
-    const flatbuffers::resolver_function_t *res = nullptr) {
-  return std::unique_ptr<tflite::ModelT>(GetModel(buf)->UnPack(res));
-}
-
-inline std::unique_ptr<tflite::ModelT> UnPackSizePrefixedModel(
-    const void *buf,
-    const flatbuffers::resolver_function_t *res = nullptr) {
-  return std::unique_ptr<tflite::ModelT>(GetSizePrefixedModel(buf)->UnPack(res));
-}
-
-}  // namespace tflite
-
-#endif  // FLATBUFFERS_GENERATED_SCHEMA_TFLITE_H_

code/components/tflite-lib/third_party/flatbuffers/include/flatbuffers/base.h

@@ -1,496 +0,0 @@
-#ifndef FLATBUFFERS_BASE_H_
-#define FLATBUFFERS_BASE_H_
-
-// For TFLM, we always want FLATBUFFERS_LOCALE_INDEPENDENT to be defined as 0.
-// We could achieve this by adding -DFLATBUFFERS_LOCALE_INDEPENDENT=0 to the
-// TFLM Makefile. However, for (at least) the Arduino, adding additional build
-// flags during the compilation can be a bit awkward. As such, we have instead
-// made a decision to change the default to be FLATBUFFERS_LOCALE_INDEPENDENT=0
-// for TFLM to make it easier for external IDE integration.
-#ifndef FLATBUFFERS_LOCALE_INDEPENDENT
-#define FLATBUFFERS_LOCALE_INDEPENDENT 0
-#endif
-
-// clang-format off
-
-// If activate should be declared and included first.
-#if defined(FLATBUFFERS_MEMORY_LEAK_TRACKING) && \
-    defined(_MSC_VER) && defined(_DEBUG)
-  // The _CRTDBG_MAP_ALLOC inside <crtdbg.h> will replace
-  // calloc/free (etc) to its debug version using #define directives.
-  #define _CRTDBG_MAP_ALLOC
-  #include <stdlib.h>
-  #include <crtdbg.h>
-  // Replace operator new by trace-enabled version.
-  #define DEBUG_NEW new(_NORMAL_BLOCK, __FILE__, __LINE__)
-  #define new DEBUG_NEW
-#endif
-
-#if !defined(FLATBUFFERS_ASSERT)
-#include <assert.h>
-#define FLATBUFFERS_ASSERT assert
-#elif defined(FLATBUFFERS_ASSERT_INCLUDE)
-// Include file with forward declaration
-#include FLATBUFFERS_ASSERT_INCLUDE
-#endif
-
-#ifndef ARDUINO
-#include <cstdint>
-#endif
-
-#include <cstddef>
-#include <cstdlib>
-#include <cstring>
-
-#if defined(ARDUINO) && !defined(ARDUINOSTL_M_H)
-  #include <utility.h>
-#else
-  #include <utility>
-#endif
-
-#include <string>
-#include <type_traits>
-#include <vector>
-#include <set>
-#include <algorithm>
-#include <iterator>
-#include <memory>
-
-#if defined(__unix__) && !defined(FLATBUFFERS_LOCALE_INDEPENDENT)
-  #include <unistd.h>
-#endif
-
-#ifdef __ANDROID__
-  #include <android/api-level.h>
-#endif
-
-#if defined(__ICCARM__)
-#include <intrinsics.h>
-#endif
-
-// Note the __clang__ check is needed, because clang presents itself
-// as an older GNUC compiler (4.2).
-// Clang 3.3 and later implement all of the ISO C++ 2011 standard.
-// Clang 3.4 and later implement all of the ISO C++ 2014 standard.
-// http://clang.llvm.org/cxx_status.html
-
-// Note the MSVC value '__cplusplus' may be incorrect:
-// The '__cplusplus' predefined macro in the MSVC stuck at the value 199711L,
-// indicating (erroneously!) that the compiler conformed to the C++98 Standard.
-// This value should be correct starting from MSVC2017-15.7-Preview-3.
-// The '__cplusplus' will be valid only if MSVC2017-15.7-P3 and the `/Zc:__cplusplus` switch is set.
-// Workaround (for details see MSDN):
-// Use the _MSC_VER and _MSVC_LANG definition instead of the __cplusplus  for compatibility.
-// The _MSVC_LANG macro reports the Standard version regardless of the '/Zc:__cplusplus' switch.
-
-#if defined(__GNUC__) && !defined(__clang__)
-  #define FLATBUFFERS_GCC (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__)
-#else
-  #define FLATBUFFERS_GCC 0
-#endif
-
-#if defined(__clang__)
-  #define FLATBUFFERS_CLANG (__clang_major__ * 10000 + __clang_minor__ * 100 + __clang_patchlevel__)
-#else
-  #define FLATBUFFERS_CLANG 0
-#endif
-
-/// @cond FLATBUFFERS_INTERNAL
-#if __cplusplus <= 199711L && \
-    (!defined(_MSC_VER) || _MSC_VER < 1600) && \
-    (!defined(__GNUC__) || \
-      (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__ < 40400))
-  #error A C++11 compatible compiler with support for the auto typing is \
-         required for FlatBuffers.
-  #error __cplusplus _MSC_VER __GNUC__  __GNUC_MINOR__  __GNUC_PATCHLEVEL__
-#endif
-
-#if !defined(__clang__) && \
-    defined(__GNUC__) && \
-    (__GNUC__ * 10000 + __GNUC_MINOR__ * 100 + __GNUC_PATCHLEVEL__ < 40600)
-  // Backwards compatibility for g++ 4.4, and 4.5 which don't have the nullptr
-  // and constexpr keywords. Note the __clang__ check is needed, because clang
-  // presents itself as an older GNUC compiler.
-  #ifndef nullptr_t
-    const class nullptr_t {
-    public:
-      template<class T> inline operator T*() const { return 0; }
-    private:
-      void operator&() const;
-    } nullptr = {};
-  #endif
-  #ifndef constexpr
-    #define constexpr const
-  #endif
-#endif
-
-// The wire format uses a little endian encoding (since that's efficient for
-// the common platforms).
-#if defined(__s390x__)
-  #define FLATBUFFERS_LITTLEENDIAN 0
-#endif // __s390x__
-#if !defined(FLATBUFFERS_LITTLEENDIAN)
-  #if defined(__GNUC__) || defined(__clang__) || defined(__ICCARM__)
-    #if (defined(__BIG_ENDIAN__) || \
-         (defined(__BYTE_ORDER__) && __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__))
-      #define FLATBUFFERS_LITTLEENDIAN 0
-    #else
-      #define FLATBUFFERS_LITTLEENDIAN 1
-    #endif // __BIG_ENDIAN__
-  #elif defined(_MSC_VER)
-    #if defined(_M_PPC)
-      #define FLATBUFFERS_LITTLEENDIAN 0
-    #else
-      #define FLATBUFFERS_LITTLEENDIAN 1
-    #endif
-  #else
-    #error Unable to determine endianness, define FLATBUFFERS_LITTLEENDIAN.
-  #endif
-#endif // !defined(FLATBUFFERS_LITTLEENDIAN)
-
-#define FLATBUFFERS_VERSION_MAJOR 2
-#define FLATBUFFERS_VERSION_MINOR 0
-#define FLATBUFFERS_VERSION_REVISION 6
-#define FLATBUFFERS_STRING_EXPAND(X) #X
-#define FLATBUFFERS_STRING(X) FLATBUFFERS_STRING_EXPAND(X)
-namespace flatbuffers {
-  // Returns version as string  "MAJOR.MINOR.REVISION".
-  const char* FLATBUFFERS_VERSION();
-}
-
-#if (!defined(_MSC_VER) || _MSC_VER > 1600) && \
-    (!defined(__GNUC__) || (__GNUC__ * 100 + __GNUC_MINOR__ >= 407)) || \
-    defined(__clang__)
-  #define FLATBUFFERS_FINAL_CLASS final
-  #define FLATBUFFERS_OVERRIDE override
-  #define FLATBUFFERS_EXPLICIT_CPP11 explicit
-  #define FLATBUFFERS_VTABLE_UNDERLYING_TYPE : flatbuffers::voffset_t
-#else
-  #define FLATBUFFERS_FINAL_CLASS
-  #define FLATBUFFERS_OVERRIDE
-  #define FLATBUFFERS_EXPLICIT_CPP11
-  #define FLATBUFFERS_VTABLE_UNDERLYING_TYPE
-#endif
-
-#if (!defined(_MSC_VER) || _MSC_VER >= 1900) && \
-    (!defined(__GNUC__) || (__GNUC__ * 100 + __GNUC_MINOR__ >= 406)) || \
-    (defined(__cpp_constexpr) && __cpp_constexpr >= 200704)
-  #define FLATBUFFERS_CONSTEXPR constexpr
-  #define FLATBUFFERS_CONSTEXPR_CPP11 constexpr
-  #define FLATBUFFERS_CONSTEXPR_DEFINED
-#else
-  #define FLATBUFFERS_CONSTEXPR const
-  #define FLATBUFFERS_CONSTEXPR_CPP11
-#endif
-
-#if (defined(__cplusplus) && __cplusplus >= 201402L) || \
-    (defined(__cpp_constexpr) && __cpp_constexpr >= 201304)
-  #define FLATBUFFERS_CONSTEXPR_CPP14 FLATBUFFERS_CONSTEXPR_CPP11
-#else
-  #define FLATBUFFERS_CONSTEXPR_CPP14
-#endif
-
-#if (defined(__GXX_EXPERIMENTAL_CXX0X__) && (__GNUC__ * 100 + __GNUC_MINOR__ >= 406)) || \
-    (defined(_MSC_FULL_VER) && (_MSC_FULL_VER >= 190023026)) || \
-    defined(__clang__)
-  #define FLATBUFFERS_NOEXCEPT noexcept
-#else
-  #define FLATBUFFERS_NOEXCEPT
-#endif
-
-// NOTE: the FLATBUFFERS_DELETE_FUNC macro may change the access mode to
-// private, so be sure to put it at the end or reset access mode explicitly.
-#if (!defined(_MSC_VER) || _MSC_FULL_VER >= 180020827) && \
-    (!defined(__GNUC__) || (__GNUC__ * 100 + __GNUC_MINOR__ >= 404)) || \
-    defined(__clang__)
-  #define FLATBUFFERS_DELETE_FUNC(func) func = delete
-#else
-  #define FLATBUFFERS_DELETE_FUNC(func) private: func
-#endif
-
-#if (!defined(_MSC_VER) || _MSC_VER >= 1900) && \
-    (!defined(__GNUC__) || (__GNUC__ * 100 + __GNUC_MINOR__ >= 409)) || \
-    defined(__clang__)
-  #define FLATBUFFERS_DEFAULT_DECLARATION
-#endif
-
-// Check if we can use template aliases
-// Not possible if Microsoft Compiler before 2012
-// Possible is the language feature __cpp_alias_templates is defined well
-// Or possible if the C++ std is C+11 or newer
-#if (defined(_MSC_VER) && _MSC_VER > 1700 /* MSVC2012 */) \
-    || (defined(__cpp_alias_templates) && __cpp_alias_templates >= 200704) \
-    || (defined(__cplusplus) && __cplusplus >= 201103L)
-  #define FLATBUFFERS_TEMPLATES_ALIASES
-#endif
-
-#ifndef FLATBUFFERS_HAS_STRING_VIEW
-  // Only provide flatbuffers::string_view if __has_include can be used
-  // to detect a header that provides an implementation
-  #if defined(__has_include)
-    // Check for std::string_view (in c++17)
-    #if __has_include(<string_view>) && (__cplusplus >= 201606 || (defined(_HAS_CXX17) && _HAS_CXX17))
-      #include <string_view>
-      namespace flatbuffers {
-        typedef std::string_view string_view;
-      }
-      #define FLATBUFFERS_HAS_STRING_VIEW 1
-    // Check for std::experimental::string_view (in c++14, compiler-dependent)
-    #elif __has_include(<experimental/string_view>) && (__cplusplus >= 201411)
-      #include <experimental/string_view>
-      namespace flatbuffers {
-        typedef std::experimental::string_view string_view;
-      }
-      #define FLATBUFFERS_HAS_STRING_VIEW 1
-    // Check for absl::string_view
-    #elif __has_include("absl/strings/string_view.h")
-      #include "absl/strings/string_view.h"
-      namespace flatbuffers {
-        typedef absl::string_view string_view;
-      }
-      #define FLATBUFFERS_HAS_STRING_VIEW 1
-    #endif
-  #endif // __has_include
-#endif // !FLATBUFFERS_HAS_STRING_VIEW
-
-#ifndef FLATBUFFERS_GENERAL_HEAP_ALLOC_OK
-  // Allow heap allocations to be used
-  #define FLATBUFFERS_GENERAL_HEAP_ALLOC_OK 1
-#endif // !FLATBUFFERS_GENERAL_HEAP_ALLOC_OK
-
-#ifndef FLATBUFFERS_HAS_NEW_STRTOD
-  // Modern (C++11) strtod and strtof functions are available for use.
-  // 1) nan/inf strings as argument of strtod;
-  // 2) hex-float  as argument of  strtod/strtof.
-  #if (defined(_MSC_VER) && _MSC_VER >= 1900) || \
-      (defined(__GNUC__) && (__GNUC__ * 100 + __GNUC_MINOR__ >= 409)) || \
-      (defined(__clang__))
-    #define FLATBUFFERS_HAS_NEW_STRTOD 1
-  #endif
-#endif // !FLATBUFFERS_HAS_NEW_STRTOD
-
-#ifndef FLATBUFFERS_LOCALE_INDEPENDENT
-  // Enable locale independent functions {strtof_l, strtod_l,strtoll_l,
-  // strtoull_l}.
-  #if (defined(_MSC_VER) && _MSC_VER >= 1800) || \
-      (defined(__ANDROID_API__) && __ANDROID_API__>= 21) || \
-      (defined(_XOPEN_VERSION) && (_XOPEN_VERSION >= 700)) && \
-        (!defined(__Fuchsia__) && !defined(__ANDROID_API__))
-    #define FLATBUFFERS_LOCALE_INDEPENDENT 1
-  #else
-    #define FLATBUFFERS_LOCALE_INDEPENDENT 0
-  #endif
-#endif  // !FLATBUFFERS_LOCALE_INDEPENDENT
-
-// Suppress Undefined Behavior Sanitizer (recoverable only). Usage:
-// - __supress_ubsan__("undefined")
-// - __supress_ubsan__("signed-integer-overflow")
-#if defined(__clang__) && (__clang_major__ > 3 || (__clang_major__ == 3 && __clang_minor__ >=7))
-  #define __supress_ubsan__(type) __attribute__((no_sanitize(type)))
-#elif defined(__GNUC__) && (__GNUC__ * 100 + __GNUC_MINOR__ >= 409)
-  #define __supress_ubsan__(type) __attribute__((no_sanitize_undefined))
-#else
-  #define __supress_ubsan__(type)
-#endif
-
-// This is constexpr function used for checking compile-time constants.
-// Avoid `#pragma warning(disable: 4127) // C4127: expression is constant`.
-template<typename T> FLATBUFFERS_CONSTEXPR inline bool IsConstTrue(T t) {
-  return !!t;
-}
-
-// Enable C++ attribute [[]] if std:c++17 or higher.
-#if ((__cplusplus >= 201703L) \
-    || (defined(_MSVC_LANG) &&  (_MSVC_LANG >= 201703L)))
-  // All attributes unknown to an implementation are ignored without causing an error.
-  #define FLATBUFFERS_ATTRIBUTE(attr) attr
-
-  #define FLATBUFFERS_FALLTHROUGH() [[fallthrough]]
-#else
-  #define FLATBUFFERS_ATTRIBUTE(attr)
-
-  #if FLATBUFFERS_CLANG >= 30800
-    #define FLATBUFFERS_FALLTHROUGH() [[clang::fallthrough]]
-  #elif FLATBUFFERS_GCC >= 70300
-    #define FLATBUFFERS_FALLTHROUGH() [[gnu::fallthrough]]
-  #else
-    #define FLATBUFFERS_FALLTHROUGH()
-  #endif
-#endif
-
-/// @endcond
-
-/// @file
-namespace flatbuffers {
-
-/// @cond FLATBUFFERS_INTERNAL
-// Our default offset / size type, 32bit on purpose on 64bit systems.
-// Also, using a consistent offset type maintains compatibility of serialized
-// offset values between 32bit and 64bit systems.
-typedef uint32_t uoffset_t;
-
-// Signed offsets for references that can go in both directions.
-typedef int32_t soffset_t;
-
-// Offset/index used in v-tables, can be changed to uint8_t in
-// format forks to save a bit of space if desired.
-typedef uint16_t voffset_t;
-
-typedef uintmax_t largest_scalar_t;
-
-// In 32bits, this evaluates to 2GB - 1
-#define FLATBUFFERS_MAX_BUFFER_SIZE ((1ULL << (sizeof(::flatbuffers::soffset_t) * 8 - 1)) - 1)
-
-// The minimum size buffer that can be a valid flatbuffer.
-// Includes the offset to the root table (uoffset_t), the offset to the vtable
-// of the root table (soffset_t), the size of the vtable (uint16_t), and the
-// size of the referring table (uint16_t).
-#define FLATBUFFERS_MIN_BUFFER_SIZE sizeof(uoffset_t) + sizeof(soffset_t) + \
-   sizeof(uint16_t) + sizeof(uint16_t)
-
-// We support aligning the contents of buffers up to this size.
-#ifndef FLATBUFFERS_MAX_ALIGNMENT
-  #define FLATBUFFERS_MAX_ALIGNMENT 32
-#endif
-
-/// @brief The length of a FlatBuffer file header.
-static const size_t kFileIdentifierLength = 4;
-
-inline bool VerifyAlignmentRequirements(size_t align, size_t min_align = 1) {
-  return (min_align <= align) && (align <= (FLATBUFFERS_MAX_ALIGNMENT)) &&
-         (align & (align - 1)) == 0;  // must be power of 2
-}
-
-#if defined(_MSC_VER)
-  #pragma warning(disable: 4351) // C4351: new behavior: elements of array ... will be default initialized
-  #pragma warning(push)
-  #pragma warning(disable: 4127) // C4127: conditional expression is constant
-#endif
-
-template<typename T> T EndianSwap(T t) {
-  #if defined(_MSC_VER)
-    #define FLATBUFFERS_BYTESWAP16 _byteswap_ushort
-    #define FLATBUFFERS_BYTESWAP32 _byteswap_ulong
-    #define FLATBUFFERS_BYTESWAP64 _byteswap_uint64
-  #elif defined(__ICCARM__)
-    #define FLATBUFFERS_BYTESWAP16 __REV16
-    #define FLATBUFFERS_BYTESWAP32 __REV
-    #define FLATBUFFERS_BYTESWAP64(x) \
-       ((__REV(static_cast<uint32_t>(x >> 32U))) | (static_cast<uint64_t>(__REV(static_cast<uint32_t>(x)))) << 32U)
-  #else
-    #if defined(__GNUC__) && __GNUC__ * 100 + __GNUC_MINOR__ < 408 && !defined(__clang__)
-      // __builtin_bswap16 was missing prior to GCC 4.8.
-      #define FLATBUFFERS_BYTESWAP16(x) \
-        static_cast<uint16_t>(__builtin_bswap32(static_cast<uint32_t>(x) << 16))
-    #else
-      #define FLATBUFFERS_BYTESWAP16 __builtin_bswap16
-    #endif
-    #define FLATBUFFERS_BYTESWAP32 __builtin_bswap32
-    #define FLATBUFFERS_BYTESWAP64 __builtin_bswap64
-  #endif
-  if (sizeof(T) == 1) {   // Compile-time if-then's.
-    return t;
-  } else if (sizeof(T) == 2) {
-    union { T t; uint16_t i; } u = { t };
-    u.i = FLATBUFFERS_BYTESWAP16(u.i);
-    return u.t;
-  } else if (sizeof(T) == 4) {
-    union { T t; uint32_t i; } u = { t };
-    u.i = FLATBUFFERS_BYTESWAP32(u.i);
-    return u.t;
-  } else if (sizeof(T) == 8) {
-    union { T t; uint64_t i; } u = { t };
-    u.i = FLATBUFFERS_BYTESWAP64(u.i);
-    return u.t;
-  } else {
-    FLATBUFFERS_ASSERT(0);
-    return t;
-  }
-}
-
-#if defined(_MSC_VER)
-  #pragma warning(pop)
-#endif
-
-
-template<typename T> T EndianScalar(T t) {
-  #if FLATBUFFERS_LITTLEENDIAN
-    return t;
-  #else
-    return EndianSwap(t);
-  #endif
-}
-
-template<typename T>
-// UBSAN: C++ aliasing type rules, see std::bit_cast<> for details.
-__supress_ubsan__("alignment")
-T ReadScalar(const void *p) {
-  return EndianScalar(*reinterpret_cast<const T *>(p));
-}
-
-// See https://github.com/google/flatbuffers/issues/5950
-
-#if (FLATBUFFERS_GCC >= 100000) && (FLATBUFFERS_GCC < 110000)
-  #pragma GCC diagnostic push
-  #pragma GCC diagnostic ignored "-Wstringop-overflow"
-#endif
-
-template<typename T>
-// UBSAN: C++ aliasing type rules, see std::bit_cast<> for details.
-__supress_ubsan__("alignment")
-void WriteScalar(void *p, T t) {
-  *reinterpret_cast<T *>(p) = EndianScalar(t);
-}
-
-template<typename T> struct Offset;
-template<typename T> __supress_ubsan__("alignment") void WriteScalar(void *p, Offset<T> t) {
-  *reinterpret_cast<uoffset_t *>(p) = EndianScalar(t.o);
-}
-
-#if (FLATBUFFERS_GCC >= 100000) && (FLATBUFFERS_GCC < 110000)
-  #pragma GCC diagnostic pop
-#endif
-
-// Computes how many bytes you'd have to pad to be able to write an
-// "scalar_size" scalar if the buffer had grown to "buf_size" (downwards in
-// memory).
-__supress_ubsan__("unsigned-integer-overflow")
-inline size_t PaddingBytes(size_t buf_size, size_t scalar_size) {
-  return ((~buf_size) + 1) & (scalar_size - 1);
-}
-
-// Generic 'operator==' with conditional specialisations.
-// T e - new value of a scalar field.
-// T def - default of scalar (is known at compile-time).
-template<typename T> inline bool IsTheSameAs(T e, T def) { return e == def; }
-
-#if defined(FLATBUFFERS_NAN_DEFAULTS) && \
-    defined(FLATBUFFERS_HAS_NEW_STRTOD) && (FLATBUFFERS_HAS_NEW_STRTOD > 0)
-// Like `operator==(e, def)` with weak NaN if T=(float|double).
-template<typename T> inline bool IsFloatTheSameAs(T e, T def) {
-  return (e == def) || ((def != def) && (e != e));
-}
-template<> inline bool IsTheSameAs<float>(float e, float def) {
-  return IsFloatTheSameAs(e, def);
-}
-template<> inline bool IsTheSameAs<double>(double e, double def) {
-  return IsFloatTheSameAs(e, def);
-}
-#endif
-
-// Check 'v' is out of closed range [low; high].
-// Workaround for GCC warning [-Werror=type-limits]:
-// comparison is always true due to limited range of data type.
-template<typename T>
-inline bool IsOutRange(const T &v, const T &low, const T &high) {
-  return (v < low) || (high < v);
-}
-
-// Check 'v' is in closed range [low; high].
-template<typename T>
-inline bool IsInRange(const T &v, const T &low, const T &high) {
-  return !IsOutRange(v, low, high);
-}
-
-}  // namespace flatbuffers
-#endif  // FLATBUFFERS_BASE_H_

code/components/tflite-lib/third_party/flatbuffers/include/flatbuffers/flatbuffer_builder.h

@@ -1,1214 +0,0 @@
-/*
- * Copyright 2021 Google Inc. All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef FLATBUFFERS_FLATBUFFER_BUILDER_H_
-#define FLATBUFFERS_FLATBUFFER_BUILDER_H_
-
-#include <functional>
-#include <initializer_list>
-
-#include "flatbuffers/allocator.h"
-#include "flatbuffers/array.h"
-#include "flatbuffers/base.h"
-#include "flatbuffers/buffer_ref.h"
-#include "flatbuffers/default_allocator.h"
-#include "flatbuffers/detached_buffer.h"
-#include "flatbuffers/stl_emulation.h"
-#include "flatbuffers/string.h"
-#include "flatbuffers/struct.h"
-#include "flatbuffers/table.h"
-#include "flatbuffers/vector.h"
-#include "flatbuffers/vector_downward.h"
-#include "flatbuffers/verifier.h"
-
-namespace flatbuffers {
-
-// Converts a Field ID to a virtual table offset.
-inline voffset_t FieldIndexToOffset(voffset_t field_id) {
-  // Should correspond to what EndTable() below builds up.
-  const int fixed_fields = 2;  // Vtable size and Object Size.
-  return static_cast<voffset_t>((field_id + fixed_fields) * sizeof(voffset_t));
-}
-
-template<typename T, typename Alloc = std::allocator<T>>
-const T *data(const std::vector<T, Alloc> &v) {
-  // Eventually the returned pointer gets passed down to memcpy, so
-  // we need it to be non-null to avoid undefined behavior.
-  static uint8_t t;
-  return v.empty() ? reinterpret_cast<const T *>(&t) : &v.front();
-}
-template<typename T, typename Alloc = std::allocator<T>>
-T *data(std::vector<T, Alloc> &v) {
-  // Eventually the returned pointer gets passed down to memcpy, so
-  // we need it to be non-null to avoid undefined behavior.
-  static uint8_t t;
-  return v.empty() ? reinterpret_cast<T *>(&t) : &v.front();
-}
-
-/// @addtogroup flatbuffers_cpp_api
-/// @{
-/// @class FlatBufferBuilder
-/// @brief Helper class to hold data needed in creation of a FlatBuffer.
-/// To serialize data, you typically call one of the `Create*()` functions in
-/// the generated code, which in turn call a sequence of `StartTable`/
-/// `PushElement`/`AddElement`/`EndTable`, or the builtin `CreateString`/
-/// `CreateVector` functions. Do this is depth-first order to build up a tree to
-/// the root. `Finish()` wraps up the buffer ready for transport.
-class FlatBufferBuilder {
- public:
-  /// @brief Default constructor for FlatBufferBuilder.
-  /// @param[in] initial_size The initial size of the buffer, in bytes. Defaults
-  /// to `1024`.
-  /// @param[in] allocator An `Allocator` to use. If null will use
-  /// `DefaultAllocator`.
-  /// @param[in] own_allocator Whether the builder/vector should own the
-  /// allocator. Defaults to / `false`.
-  /// @param[in] buffer_minalign Force the buffer to be aligned to the given
-  /// minimum alignment upon reallocation. Only needed if you intend to store
-  /// types with custom alignment AND you wish to read the buffer in-place
-  /// directly after creation.
-  explicit FlatBufferBuilder(
-      size_t initial_size = 1024, Allocator *allocator = nullptr,
-      bool own_allocator = false,
-      size_t buffer_minalign = AlignOf<largest_scalar_t>())
-      : buf_(initial_size, allocator, own_allocator, buffer_minalign),
-        num_field_loc(0),
-        max_voffset_(0),
-        nested(false),
-        finished(false),
-        minalign_(1),
-        force_defaults_(false),
-        dedup_vtables_(true),
-        string_pool(nullptr) {
-    EndianCheck();
-  }
-
-  /// @brief Move constructor for FlatBufferBuilder.
-  FlatBufferBuilder(FlatBufferBuilder &&other)
-      : buf_(1024, nullptr, false, AlignOf<largest_scalar_t>()),
-        num_field_loc(0),
-        max_voffset_(0),
-        nested(false),
-        finished(false),
-        minalign_(1),
-        force_defaults_(false),
-        dedup_vtables_(true),
-        string_pool(nullptr) {
-    EndianCheck();
-    // Default construct and swap idiom.
-    // Lack of delegating constructors in vs2010 makes it more verbose than
-    // needed.
-    Swap(other);
-  }
-
-  /// @brief Move assignment operator for FlatBufferBuilder.
-  FlatBufferBuilder &operator=(FlatBufferBuilder &&other) {
-    // Move construct a temporary and swap idiom
-    FlatBufferBuilder temp(std::move(other));
-    Swap(temp);
-    return *this;
-  }
-
-  void Swap(FlatBufferBuilder &other) {
-    using std::swap;
-    buf_.swap(other.buf_);
-    swap(num_field_loc, other.num_field_loc);
-    swap(max_voffset_, other.max_voffset_);
-    swap(nested, other.nested);
-    swap(finished, other.finished);
-    swap(minalign_, other.minalign_);
-    swap(force_defaults_, other.force_defaults_);
-    swap(dedup_vtables_, other.dedup_vtables_);
-    swap(string_pool, other.string_pool);
-  }
-
-  ~FlatBufferBuilder() {
-    if (string_pool) delete string_pool;
-  }
-
-  void Reset() {
-    Clear();       // clear builder state
-    buf_.reset();  // deallocate buffer
-  }
-
-  /// @brief Reset all the state in this FlatBufferBuilder so it can be reused
-  /// to construct another buffer.
-  void Clear() {
-    ClearOffsets();
-    buf_.clear();
-    nested = false;
-    finished = false;
-    minalign_ = 1;
-    if (string_pool) string_pool->clear();
-  }
-
-  /// @brief The current size of the serialized buffer, counting from the end.
-  /// @return Returns an `uoffset_t` with the current size of the buffer.
-  uoffset_t GetSize() const { return buf_.size(); }
-
-  /// @brief Get the serialized buffer (after you call `Finish()`).
-  /// @return Returns an `uint8_t` pointer to the FlatBuffer data inside the
-  /// buffer.
-  uint8_t *GetBufferPointer() const {
-    Finished();
-    return buf_.data();
-  }
-
-  /// @brief Get the serialized buffer (after you call `Finish()`) as a span.
-  /// @return Returns a constructed flatbuffers::span that is a view over the
-  /// FlatBuffer data inside the buffer.
-  flatbuffers::span<uint8_t> GetBufferSpan() const {
-    Finished();
-    return flatbuffers::span<uint8_t>(buf_.data(), buf_.size());
-  }
-
-  /// @brief Get a pointer to an unfinished buffer.
-  /// @return Returns a `uint8_t` pointer to the unfinished buffer.
-  uint8_t *GetCurrentBufferPointer() const { return buf_.data(); }
-
-  /// @brief Get the released pointer to the serialized buffer.
-  /// @warning Do NOT attempt to use this FlatBufferBuilder afterwards!
-  /// @return A `FlatBuffer` that owns the buffer and its allocator and
-  /// behaves similar to a `unique_ptr` with a deleter.
-  FLATBUFFERS_ATTRIBUTE([[deprecated("use Release() instead")]])
-  DetachedBuffer ReleaseBufferPointer() {
-    Finished();
-    return buf_.release();
-  }
-
-  /// @brief Get the released DetachedBuffer.
-  /// @return A `DetachedBuffer` that owns the buffer and its allocator.
-  DetachedBuffer Release() {
-    Finished();
-    return buf_.release();
-  }
-
-  /// @brief Get the released pointer to the serialized buffer.
-  /// @param size The size of the memory block containing
-  /// the serialized `FlatBuffer`.
-  /// @param offset The offset from the released pointer where the finished
-  /// `FlatBuffer` starts.
-  /// @return A raw pointer to the start of the memory block containing
-  /// the serialized `FlatBuffer`.
-  /// @remark If the allocator is owned, it gets deleted when the destructor is
-  /// called..
-  uint8_t *ReleaseRaw(size_t &size, size_t &offset) {
-    Finished();
-    return buf_.release_raw(size, offset);
-  }
-
-  /// @brief get the minimum alignment this buffer needs to be accessed
-  /// properly. This is only known once all elements have been written (after
-  /// you call Finish()). You can use this information if you need to embed
-  /// a FlatBuffer in some other buffer, such that you can later read it
-  /// without first having to copy it into its own buffer.
-  size_t GetBufferMinAlignment() const {
-    Finished();
-    return minalign_;
-  }
-
-  /// @cond FLATBUFFERS_INTERNAL
-  void Finished() const {
-    // If you get this assert, you're attempting to get access a buffer
-    // which hasn't been finished yet. Be sure to call
-    // FlatBufferBuilder::Finish with your root table.
-    // If you really need to access an unfinished buffer, call
-    // GetCurrentBufferPointer instead.
-    FLATBUFFERS_ASSERT(finished);
-  }
-  /// @endcond
-
-  /// @brief In order to save space, fields that are set to their default value
-  /// don't get serialized into the buffer.
-  /// @param[in] fd When set to `true`, always serializes default values that
-  /// are set. Optional fields which are not set explicitly, will still not be
-  /// serialized.
-  void ForceDefaults(bool fd) { force_defaults_ = fd; }
-
-  /// @brief By default vtables are deduped in order to save space.
-  /// @param[in] dedup When set to `true`, dedup vtables.
-  void DedupVtables(bool dedup) { dedup_vtables_ = dedup; }
-
-  /// @cond FLATBUFFERS_INTERNAL
-  void Pad(size_t num_bytes) { buf_.fill(num_bytes); }
-
-  void TrackMinAlign(size_t elem_size) {
-    if (elem_size > minalign_) minalign_ = elem_size;
-  }
-
-  void Align(size_t elem_size) {
-    TrackMinAlign(elem_size);
-    buf_.fill(PaddingBytes(buf_.size(), elem_size));
-  }
-
-  void PushFlatBuffer(const uint8_t *bytes, size_t size) {
-    PushBytes(bytes, size);
-    finished = true;
-  }
-
-  void PushBytes(const uint8_t *bytes, size_t size) { buf_.push(bytes, size); }
-
-  void PopBytes(size_t amount) { buf_.pop(amount); }
-
-  template<typename T> void AssertScalarT() {
-    // The code assumes power of 2 sizes and endian-swap-ability.
-    static_assert(flatbuffers::is_scalar<T>::value, "T must be a scalar type");
-  }
-
-  // Write a single aligned scalar to the buffer
-  template<typename T> uoffset_t PushElement(T element) {
-    AssertScalarT<T>();
-    Align(sizeof(T));
-    buf_.push_small(EndianScalar(element));
-    return GetSize();
-  }
-
-  template<typename T> uoffset_t PushElement(Offset<T> off) {
-    // Special case for offsets: see ReferTo below.
-    return PushElement(ReferTo(off.o));
-  }
-
-  // When writing fields, we track where they are, so we can create correct
-  // vtables later.
-  void TrackField(voffset_t field, uoffset_t off) {
-    FieldLoc fl = { off, field };
-    buf_.scratch_push_small(fl);
-    num_field_loc++;
-    if (field > max_voffset_) { max_voffset_ = field; }
-  }
-
-  // Like PushElement, but additionally tracks the field this represents.
-  template<typename T> void AddElement(voffset_t field, T e, T def) {
-    // We don't serialize values equal to the default.
-    if (IsTheSameAs(e, def) && !force_defaults_) return;
-    TrackField(field, PushElement(e));
-  }
-
-  template<typename T> void AddElement(voffset_t field, T e) {
-    TrackField(field, PushElement(e));
-  }
-
-  template<typename T> void AddOffset(voffset_t field, Offset<T> off) {
-    if (off.IsNull()) return;  // Don't store.
-    AddElement(field, ReferTo(off.o), static_cast<uoffset_t>(0));
-  }
-
-  template<typename T> void AddStruct(voffset_t field, const T *structptr) {
-    if (!structptr) return;  // Default, don't store.
-    Align(AlignOf<T>());
-    buf_.push_small(*structptr);
-    TrackField(field, GetSize());
-  }
-
-  void AddStructOffset(voffset_t field, uoffset_t off) {
-    TrackField(field, off);
-  }
-
-  // Offsets initially are relative to the end of the buffer (downwards).
-  // This function converts them to be relative to the current location
-  // in the buffer (when stored here), pointing upwards.
-  uoffset_t ReferTo(uoffset_t off) {
-    // Align to ensure GetSize() below is correct.
-    Align(sizeof(uoffset_t));
-    // Offset must refer to something already in buffer.
-    const uoffset_t size = GetSize();
-    FLATBUFFERS_ASSERT(off && off <= size);
-    return size - off + static_cast<uoffset_t>(sizeof(uoffset_t));
-  }
-
-  void NotNested() {
-    // If you hit this, you're trying to construct a Table/Vector/String
-    // during the construction of its parent table (between the MyTableBuilder
-    // and table.Finish().
-    // Move the creation of these sub-objects to above the MyTableBuilder to
-    // not get this assert.
-    // Ignoring this assert may appear to work in simple cases, but the reason
-    // it is here is that storing objects in-line may cause vtable offsets
-    // to not fit anymore. It also leads to vtable duplication.
-    FLATBUFFERS_ASSERT(!nested);
-    // If you hit this, fields were added outside the scope of a table.
-    FLATBUFFERS_ASSERT(!num_field_loc);
-  }
-
-  // From generated code (or from the parser), we call StartTable/EndTable
-  // with a sequence of AddElement calls in between.
-  uoffset_t StartTable() {
-    NotNested();
-    nested = true;
-    return GetSize();
-  }
-
-  // This finishes one serialized object by generating the vtable if it's a
-  // table, comparing it against existing vtables, and writing the
-  // resulting vtable offset.
-  uoffset_t EndTable(uoffset_t start) {
-    // If you get this assert, a corresponding StartTable wasn't called.
-    FLATBUFFERS_ASSERT(nested);
-    // Write the vtable offset, which is the start of any Table.
-    // We fill it's value later.
-    auto vtableoffsetloc = PushElement<soffset_t>(0);
-    // Write a vtable, which consists entirely of voffset_t elements.
-    // It starts with the number of offsets, followed by a type id, followed
-    // by the offsets themselves. In reverse:
-    // Include space for the last offset and ensure empty tables have a
-    // minimum size.
-    max_voffset_ =
-        (std::max)(static_cast<voffset_t>(max_voffset_ + sizeof(voffset_t)),
-                   FieldIndexToOffset(0));
-    buf_.fill_big(max_voffset_);
-    auto table_object_size = vtableoffsetloc - start;
-    // Vtable use 16bit offsets.
-    FLATBUFFERS_ASSERT(table_object_size < 0x10000);
-    WriteScalar<voffset_t>(buf_.data() + sizeof(voffset_t),
-                           static_cast<voffset_t>(table_object_size));
-    WriteScalar<voffset_t>(buf_.data(), max_voffset_);
-    // Write the offsets into the table
-    for (auto it = buf_.scratch_end() - num_field_loc * sizeof(FieldLoc);
-         it < buf_.scratch_end(); it += sizeof(FieldLoc)) {
-      auto field_location = reinterpret_cast<FieldLoc *>(it);
-      auto pos = static_cast<voffset_t>(vtableoffsetloc - field_location->off);
-      // If this asserts, it means you've set a field twice.
-      FLATBUFFERS_ASSERT(
-          !ReadScalar<voffset_t>(buf_.data() + field_location->id));
-      WriteScalar<voffset_t>(buf_.data() + field_location->id, pos);
-    }
-    ClearOffsets();
-    auto vt1 = reinterpret_cast<voffset_t *>(buf_.data());
-    auto vt1_size = ReadScalar<voffset_t>(vt1);
-    auto vt_use = GetSize();
-    // See if we already have generated a vtable with this exact same
-    // layout before. If so, make it point to the old one, remove this one.
-    if (dedup_vtables_) {
-      for (auto it = buf_.scratch_data(); it < buf_.scratch_end();
-           it += sizeof(uoffset_t)) {
-        auto vt_offset_ptr = reinterpret_cast<uoffset_t *>(it);
-        auto vt2 = reinterpret_cast<voffset_t *>(buf_.data_at(*vt_offset_ptr));
-        auto vt2_size = ReadScalar<voffset_t>(vt2);
-        if (vt1_size != vt2_size || 0 != memcmp(vt2, vt1, vt1_size)) continue;
-        vt_use = *vt_offset_ptr;
-        buf_.pop(GetSize() - vtableoffsetloc);
-        break;
-      }
-    }
-    // If this is a new vtable, remember it.
-    if (vt_use == GetSize()) { buf_.scratch_push_small(vt_use); }
-    // Fill the vtable offset we created above.
-    // The offset points from the beginning of the object to where the
-    // vtable is stored.
-    // Offsets default direction is downward in memory for future format
-    // flexibility (storing all vtables at the start of the file).
-    WriteScalar(buf_.data_at(vtableoffsetloc),
-                static_cast<soffset_t>(vt_use) -
-                    static_cast<soffset_t>(vtableoffsetloc));
-
-    nested = false;
-    return vtableoffsetloc;
-  }
-
-  FLATBUFFERS_ATTRIBUTE([[deprecated("call the version above instead")]])
-  uoffset_t EndTable(uoffset_t start, voffset_t /*numfields*/) {
-    return EndTable(start);
-  }
-
-  // This checks a required field has been set in a given table that has
-  // just been constructed.
-  template<typename T> void Required(Offset<T> table, voffset_t field);
-
-  uoffset_t StartStruct(size_t alignment) {
-    Align(alignment);
-    return GetSize();
-  }
-
-  uoffset_t EndStruct() { return GetSize(); }
-
-  void ClearOffsets() {
-    buf_.scratch_pop(num_field_loc * sizeof(FieldLoc));
-    num_field_loc = 0;
-    max_voffset_ = 0;
-  }
-
-  // Aligns such that when "len" bytes are written, an object can be written
-  // after it with "alignment" without padding.
-  void PreAlign(size_t len, size_t alignment) {
-    if (len == 0) return;
-    TrackMinAlign(alignment);
-    buf_.fill(PaddingBytes(GetSize() + len, alignment));
-  }
-  template<typename T> void PreAlign(size_t len) {
-    AssertScalarT<T>();
-    PreAlign(len, sizeof(T));
-  }
-  /// @endcond
-
-  /// @brief Store a string in the buffer, which can contain any binary data.
-  /// @param[in] str A const char pointer to the data to be stored as a string.
-  /// @param[in] len The number of bytes that should be stored from `str`.
-  /// @return Returns the offset in the buffer where the string starts.
-  Offset<String> CreateString(const char *str, size_t len) {
-    NotNested();
-    PreAlign<uoffset_t>(len + 1);  // Always 0-terminated.
-    buf_.fill(1);
-    PushBytes(reinterpret_cast<const uint8_t *>(str), len);
-    PushElement(static_cast<uoffset_t>(len));
-    return Offset<String>(GetSize());
-  }
-
-  /// @brief Store a string in the buffer, which is null-terminated.
-  /// @param[in] str A const char pointer to a C-string to add to the buffer.
-  /// @return Returns the offset in the buffer where the string starts.
-  Offset<String> CreateString(const char *str) {
-    return CreateString(str, strlen(str));
-  }
-
-  /// @brief Store a string in the buffer, which is null-terminated.
-  /// @param[in] str A char pointer to a C-string to add to the buffer.
-  /// @return Returns the offset in the buffer where the string starts.
-  Offset<String> CreateString(char *str) {
-    return CreateString(str, strlen(str));
-  }
-
-  /// @brief Store a string in the buffer, which can contain any binary data.
-  /// @param[in] str A const reference to a std::string to store in the buffer.
-  /// @return Returns the offset in the buffer where the string starts.
-  Offset<String> CreateString(const std::string &str) {
-    return CreateString(str.c_str(), str.length());
-  }
-
-  // clang-format off
-  #ifdef FLATBUFFERS_HAS_STRING_VIEW
-  /// @brief Store a string in the buffer, which can contain any binary data.
-  /// @param[in] str A const string_view to copy in to the buffer.
-  /// @return Returns the offset in the buffer where the string starts.
-  Offset<String> CreateString(flatbuffers::string_view str) {
-    return CreateString(str.data(), str.size());
-  }
-  #endif // FLATBUFFERS_HAS_STRING_VIEW
-  // clang-format on
-
-  /// @brief Store a string in the buffer, which can contain any binary data.
-  /// @param[in] str A const pointer to a `String` struct to add to the buffer.
-  /// @return Returns the offset in the buffer where the string starts
-  Offset<String> CreateString(const String *str) {
-    return str ? CreateString(str->c_str(), str->size()) : 0;
-  }
-
-  /// @brief Store a string in the buffer, which can contain any binary data.
-  /// @param[in] str A const reference to a std::string like type with support
-  /// of T::c_str() and T::length() to store in the buffer.
-  /// @return Returns the offset in the buffer where the string starts.
-  template<typename T> Offset<String> CreateString(const T &str) {
-    return CreateString(str.c_str(), str.length());
-  }
-
-  /// @brief Store a string in the buffer, which can contain any binary data.
-  /// If a string with this exact contents has already been serialized before,
-  /// instead simply returns the offset of the existing string. This uses a map
-  /// stored on the heap, but only stores the numerical offsets.
-  /// @param[in] str A const char pointer to the data to be stored as a string.
-  /// @param[in] len The number of bytes that should be stored from `str`.
-  /// @return Returns the offset in the buffer where the string starts.
-  Offset<String> CreateSharedString(const char *str, size_t len) {
-    FLATBUFFERS_ASSERT(FLATBUFFERS_GENERAL_HEAP_ALLOC_OK);
-    if (!string_pool)
-      string_pool = new StringOffsetMap(StringOffsetCompare(buf_));
-    auto size_before_string = buf_.size();
-    // Must first serialize the string, since the set is all offsets into
-    // buffer.
-    auto off = CreateString(str, len);
-    auto it = string_pool->find(off);
-    // If it exists we reuse existing serialized data!
-    if (it != string_pool->end()) {
-      // We can remove the string we serialized.
-      buf_.pop(buf_.size() - size_before_string);
-      return *it;
-    }
-    // Record this string for future use.
-    string_pool->insert(off);
-    return off;
-  }
-
-#ifdef FLATBUFFERS_HAS_STRING_VIEW
-  /// @brief Store a string in the buffer, which can contain any binary data.
-  /// If a string with this exact contents has already been serialized before,
-  /// instead simply returns the offset of the existing string. This uses a map
-  /// stored on the heap, but only stores the numerical offsets.
-  /// @param[in] str A const std::string_view to store in the buffer.
-  /// @return Returns the offset in the buffer where the string starts
-  Offset<String> CreateSharedString(const flatbuffers::string_view str) {
-    return CreateSharedString(str.data(), str.size());
-  }
-#else
-  /// @brief Store a string in the buffer, which null-terminated.
-  /// If a string with this exact contents has already been serialized before,
-  /// instead simply returns the offset of the existing string. This uses a map
-  /// stored on the heap, but only stores the numerical offsets.
-  /// @param[in] str A const char pointer to a C-string to add to the buffer.
-  /// @return Returns the offset in the buffer where the string starts.
-  Offset<String> CreateSharedString(const char *str) {
-    return CreateSharedString(str, strlen(str));
-  }
-
-  /// @brief Store a string in the buffer, which can contain any binary data.
-  /// If a string with this exact contents has already been serialized before,
-  /// instead simply returns the offset of the existing string. This uses a map
-  /// stored on the heap, but only stores the numerical offsets.
-  /// @param[in] str A const reference to a std::string to store in the buffer.
-  /// @return Returns the offset in the buffer where the string starts.
-  Offset<String> CreateSharedString(const std::string &str) {
-    return CreateSharedString(str.c_str(), str.length());
-  }
-#endif
-
-  /// @brief Store a string in the buffer, which can contain any binary data.
-  /// If a string with this exact contents has already been serialized before,
-  /// instead simply returns the offset of the existing string. This uses a map
-  /// stored on the heap, but only stores the numerical offsets.
-  /// @param[in] str A const pointer to a `String` struct to add to the buffer.
-  /// @return Returns the offset in the buffer where the string starts
-  Offset<String> CreateSharedString(const String *str) {
-    return CreateSharedString(str->c_str(), str->size());
-  }
-
-  /// @cond FLATBUFFERS_INTERNAL
-  uoffset_t EndVector(size_t len) {
-    FLATBUFFERS_ASSERT(nested);  // Hit if no corresponding StartVector.
-    nested = false;
-    return PushElement(static_cast<uoffset_t>(len));
-  }
-
-  void StartVector(size_t len, size_t elemsize) {
-    NotNested();
-    nested = true;
-    PreAlign<uoffset_t>(len * elemsize);
-    PreAlign(len * elemsize, elemsize);  // Just in case elemsize > uoffset_t.
-  }
-
-  // Call this right before StartVector/CreateVector if you want to force the
-  // alignment to be something different than what the element size would
-  // normally dictate.
-  // This is useful when storing a nested_flatbuffer in a vector of bytes,
-  // or when storing SIMD floats, etc.
-  void ForceVectorAlignment(size_t len, size_t elemsize, size_t alignment) {
-    if (len == 0) return;
-    FLATBUFFERS_ASSERT(VerifyAlignmentRequirements(alignment));
-    PreAlign(len * elemsize, alignment);
-  }
-
-  // Similar to ForceVectorAlignment but for String fields.
-  void ForceStringAlignment(size_t len, size_t alignment) {
-    if (len == 0) return;
-    FLATBUFFERS_ASSERT(VerifyAlignmentRequirements(alignment));
-    PreAlign((len + 1) * sizeof(char), alignment);
-  }
-
-  /// @endcond
-
-  /// @brief Serialize an array into a FlatBuffer `vector`.
-  /// @tparam T The data type of the array elements.
-  /// @param[in] v A pointer to the array of type `T` to serialize into the
-  /// buffer as a `vector`.
-  /// @param[in] len The number of elements to serialize.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T> Offset<Vector<T>> CreateVector(const T *v, size_t len) {
-    // If this assert hits, you're specifying a template argument that is
-    // causing the wrong overload to be selected, remove it.
-    AssertScalarT<T>();
-    StartVector(len, sizeof(T));
-    if (len == 0) { return Offset<Vector<T>>(EndVector(len)); }
-    // clang-format off
-    #if FLATBUFFERS_LITTLEENDIAN
-      PushBytes(reinterpret_cast<const uint8_t *>(v), len * sizeof(T));
-    #else
-      if (sizeof(T) == 1) {
-        PushBytes(reinterpret_cast<const uint8_t *>(v), len);
-      } else {
-        for (auto i = len; i > 0; ) {
-          PushElement(v[--i]);
-        }
-      }
-    #endif
-    // clang-format on
-    return Offset<Vector<T>>(EndVector(len));
-  }
-
-  /// @brief Serialize an array like object into a FlatBuffer `vector`.
-  /// @tparam T The data type of the array elements.
-  /// @tparam C The type of the array.
-  /// @param[in] array A reference to an array like object of type `T` to
-  /// serialize into the buffer as a `vector`.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T, class C> Offset<Vector<T>> CreateVector(const C &array) {
-    return CreateVector(array.data(), array.size());
-  }
-
-  /// @brief Serialize an initializer list into a FlatBuffer `vector`.
-  /// @tparam T The data type of the initializer list elements.
-  /// @param[in] v The value of the initializer list.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T>
-  Offset<Vector<T>> CreateVector(std::initializer_list<T> v) {
-    return CreateVector(v.begin(), v.size());
-  }
-
-  template<typename T>
-  Offset<Vector<Offset<T>>> CreateVector(const Offset<T> *v, size_t len) {
-    StartVector(len, sizeof(Offset<T>));
-    for (auto i = len; i > 0;) { PushElement(v[--i]); }
-    return Offset<Vector<Offset<T>>>(EndVector(len));
-  }
-
-  /// @brief Serialize a `std::vector` into a FlatBuffer `vector`.
-  /// @tparam T The data type of the `std::vector` elements.
-  /// @param v A const reference to the `std::vector` to serialize into the
-  /// buffer as a `vector`.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T, typename Alloc = std::allocator<T>>
-  Offset<Vector<T>> CreateVector(const std::vector<T, Alloc> &v) {
-    return CreateVector(data(v), v.size());
-  }
-
-  // vector<bool> may be implemented using a bit-set, so we can't access it as
-  // an array. Instead, read elements manually.
-  // Background: https://isocpp.org/blog/2012/11/on-vectorbool
-  Offset<Vector<uint8_t>> CreateVector(const std::vector<bool> &v) {
-    StartVector(v.size(), sizeof(uint8_t));
-    for (auto i = v.size(); i > 0;) {
-      PushElement(static_cast<uint8_t>(v[--i]));
-    }
-    return Offset<Vector<uint8_t>>(EndVector(v.size()));
-  }
-
-  /// @brief Serialize values returned by a function into a FlatBuffer `vector`.
-  /// This is a convenience function that takes care of iteration for you.
-  /// @tparam T The data type of the `std::vector` elements.
-  /// @param f A function that takes the current iteration 0..vector_size-1 and
-  /// returns any type that you can construct a FlatBuffers vector out of.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T>
-  Offset<Vector<T>> CreateVector(size_t vector_size,
-                                 const std::function<T(size_t i)> &f) {
-    FLATBUFFERS_ASSERT(FLATBUFFERS_GENERAL_HEAP_ALLOC_OK);
-    std::vector<T> elems(vector_size);
-    for (size_t i = 0; i < vector_size; i++) elems[i] = f(i);
-    return CreateVector(elems);
-  }
-
-  /// @brief Serialize values returned by a function into a FlatBuffer `vector`.
-  /// This is a convenience function that takes care of iteration for you. This
-  /// uses a vector stored on the heap to store the intermediate results of the
-  /// iteration.
-  /// @tparam T The data type of the `std::vector` elements.
-  /// @param f A function that takes the current iteration 0..vector_size-1,
-  /// and the state parameter returning any type that you can construct a
-  /// FlatBuffers vector out of.
-  /// @param state State passed to f.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T, typename F, typename S>
-  Offset<Vector<T>> CreateVector(size_t vector_size, F f, S *state) {
-    FLATBUFFERS_ASSERT(FLATBUFFERS_GENERAL_HEAP_ALLOC_OK);
-    std::vector<T> elems(vector_size);
-    for (size_t i = 0; i < vector_size; i++) elems[i] = f(i, state);
-    return CreateVector(elems);
-  }
-
-  /// @brief Serialize a `std::vector<StringType>` into a FlatBuffer `vector`.
-  /// whereas StringType is any type that is accepted by the CreateString()
-  /// overloads.
-  /// This is a convenience function for a common case.
-  /// @param v A const reference to the `std::vector` to serialize into the
-  /// buffer as a `vector`.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename StringType = std::string,
-           typename Alloc = std::allocator<StringType>>
-  Offset<Vector<Offset<String>>> CreateVectorOfStrings(
-      const std::vector<StringType, Alloc> &v) {
-    return CreateVectorOfStrings(v.cbegin(), v.cend());
-  }
-
-  /// @brief Serialize a collection of Strings into a FlatBuffer `vector`.
-  /// This is a convenience function for a common case.
-  /// @param begin The begining iterator of the collection
-  /// @param end The ending iterator of the collection
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<class It>
-  Offset<Vector<Offset<String>>> CreateVectorOfStrings(It begin, It end) {
-    auto size = std::distance(begin, end);
-    auto scratch_buffer_usage = size * sizeof(Offset<String>);
-    // If there is not enough space to store the offsets, there definitely won't
-    // be enough space to store all the strings. So ensuring space for the
-    // scratch region is OK, for it it fails, it would have failed later.
-    buf_.ensure_space(scratch_buffer_usage);
-    for (auto it = begin; it != end; ++it) {
-      buf_.scratch_push_small(CreateString(*it));
-    }
-    StartVector(size, sizeof(Offset<String>));
-    for (auto i = 1; i <= size; i++) {
-      // Note we re-evaluate the buf location each iteration to account for any
-      // underlying buffer resizing that may occur.
-      PushElement(*reinterpret_cast<Offset<String> *>(
-          buf_.scratch_end() - i * sizeof(Offset<String>)));
-    }
-    buf_.scratch_pop(scratch_buffer_usage);
-    return Offset<Vector<Offset<String>>>(EndVector(size));
-  }
-
-  /// @brief Serialize an array of structs into a FlatBuffer `vector`.
-  /// @tparam T The data type of the struct array elements.
-  /// @param[in] v A pointer to the array of type `T` to serialize into the
-  /// buffer as a `vector`.
-  /// @param[in] len The number of elements to serialize.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T>
-  Offset<Vector<const T *>> CreateVectorOfStructs(const T *v, size_t len) {
-    StartVector(len * sizeof(T) / AlignOf<T>(), AlignOf<T>());
-    if (len > 0) {
-      PushBytes(reinterpret_cast<const uint8_t *>(v), sizeof(T) * len);
-    }
-    return Offset<Vector<const T *>>(EndVector(len));
-  }
-
-  /// @brief Serialize an array of native structs into a FlatBuffer `vector`.
-  /// @tparam T The data type of the struct array elements.
-  /// @tparam S The data type of the native struct array elements.
-  /// @param[in] v A pointer to the array of type `S` to serialize into the
-  /// buffer as a `vector`.
-  /// @param[in] len The number of elements to serialize.
-  /// @param[in] pack_func Pointer to a function to convert the native struct
-  /// to the FlatBuffer struct.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T, typename S>
-  Offset<Vector<const T *>> CreateVectorOfNativeStructs(
-      const S *v, size_t len, T (*const pack_func)(const S &)) {
-    FLATBUFFERS_ASSERT(pack_func);
-    auto structs = StartVectorOfStructs<T>(len);
-    for (size_t i = 0; i < len; i++) { structs[i] = pack_func(v[i]); }
-    return EndVectorOfStructs<T>(len);
-  }
-
-  /// @brief Serialize an array of native structs into a FlatBuffer `vector`.
-  /// @tparam T The data type of the struct array elements.
-  /// @tparam S The data type of the native struct array elements.
-  /// @param[in] v A pointer to the array of type `S` to serialize into the
-  /// buffer as a `vector`.
-  /// @param[in] len The number of elements to serialize.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T, typename S>
-  Offset<Vector<const T *>> CreateVectorOfNativeStructs(const S *v,
-                                                        size_t len) {
-    extern T Pack(const S &);
-    return CreateVectorOfNativeStructs(v, len, Pack);
-  }
-
-  /// @brief Serialize an array of structs into a FlatBuffer `vector`.
-  /// @tparam T The data type of the struct array elements.
-  /// @param[in] filler A function that takes the current iteration
-  /// 0..vector_size-1 and a pointer to the struct that must be filled.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  /// This is mostly useful when flatbuffers are generated with mutation
-  /// accessors.
-  template<typename T>
-  Offset<Vector<const T *>> CreateVectorOfStructs(
-      size_t vector_size, const std::function<void(size_t i, T *)> &filler) {
-    T *structs = StartVectorOfStructs<T>(vector_size);
-    for (size_t i = 0; i < vector_size; i++) {
-      filler(i, structs);
-      structs++;
-    }
-    return EndVectorOfStructs<T>(vector_size);
-  }
-
-  /// @brief Serialize an array of structs into a FlatBuffer `vector`.
-  /// @tparam T The data type of the struct array elements.
-  /// @param[in] f A function that takes the current iteration 0..vector_size-1,
-  /// a pointer to the struct that must be filled and the state argument.
-  /// @param[in] state Arbitrary state to pass to f.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  /// This is mostly useful when flatbuffers are generated with mutation
-  /// accessors.
-  template<typename T, typename F, typename S>
-  Offset<Vector<const T *>> CreateVectorOfStructs(size_t vector_size, F f,
-                                                  S *state) {
-    T *structs = StartVectorOfStructs<T>(vector_size);
-    for (size_t i = 0; i < vector_size; i++) {
-      f(i, structs, state);
-      structs++;
-    }
-    return EndVectorOfStructs<T>(vector_size);
-  }
-
-  /// @brief Serialize a `std::vector` of structs into a FlatBuffer `vector`.
-  /// @tparam T The data type of the `std::vector` struct elements.
-  /// @param[in] v A const reference to the `std::vector` of structs to
-  /// serialize into the buffer as a `vector`.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T, typename Alloc = std::allocator<T>>
-  Offset<Vector<const T *>> CreateVectorOfStructs(
-      const std::vector<T, Alloc> &v) {
-    return CreateVectorOfStructs(data(v), v.size());
-  }
-
-  /// @brief Serialize a `std::vector` of native structs into a FlatBuffer
-  /// `vector`.
-  /// @tparam T The data type of the `std::vector` struct elements.
-  /// @tparam S The data type of the `std::vector` native struct elements.
-  /// @param[in] v A const reference to the `std::vector` of structs to
-  /// serialize into the buffer as a `vector`.
-  /// @param[in] pack_func Pointer to a function to convert the native struct
-  /// to the FlatBuffer struct.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T, typename S, typename Alloc = std::allocator<T>>
-  Offset<Vector<const T *>> CreateVectorOfNativeStructs(
-      const std::vector<S, Alloc> &v, T (*const pack_func)(const S &)) {
-    return CreateVectorOfNativeStructs<T, S>(data(v), v.size(), pack_func);
-  }
-
-  /// @brief Serialize a `std::vector` of native structs into a FlatBuffer
-  /// `vector`.
-  /// @tparam T The data type of the `std::vector` struct elements.
-  /// @tparam S The data type of the `std::vector` native struct elements.
-  /// @param[in] v A const reference to the `std::vector` of structs to
-  /// serialize into the buffer as a `vector`.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T, typename S, typename Alloc = std::allocator<S>>
-  Offset<Vector<const T *>> CreateVectorOfNativeStructs(
-      const std::vector<S, Alloc> &v) {
-    return CreateVectorOfNativeStructs<T, S>(data(v), v.size());
-  }
-
-  /// @cond FLATBUFFERS_INTERNAL
-  template<typename T> struct StructKeyComparator {
-    bool operator()(const T &a, const T &b) const {
-      return a.KeyCompareLessThan(&b);
-    }
-  };
-  /// @endcond
-
-  /// @brief Serialize a `std::vector` of structs into a FlatBuffer `vector`
-  /// in sorted order.
-  /// @tparam T The data type of the `std::vector` struct elements.
-  /// @param[in] v A const reference to the `std::vector` of structs to
-  /// serialize into the buffer as a `vector`.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T, typename Alloc = std::allocator<T>>
-  Offset<Vector<const T *>> CreateVectorOfSortedStructs(
-      std::vector<T, Alloc> *v) {
-    return CreateVectorOfSortedStructs(data(*v), v->size());
-  }
-
-  /// @brief Serialize a `std::vector` of native structs into a FlatBuffer
-  /// `vector` in sorted order.
-  /// @tparam T The data type of the `std::vector` struct elements.
-  /// @tparam S The data type of the `std::vector` native struct elements.
-  /// @param[in] v A const reference to the `std::vector` of structs to
-  /// serialize into the buffer as a `vector`.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T, typename S, typename Alloc = std::allocator<T>>
-  Offset<Vector<const T *>> CreateVectorOfSortedNativeStructs(
-      std::vector<S, Alloc> *v) {
-    return CreateVectorOfSortedNativeStructs<T, S>(data(*v), v->size());
-  }
-
-  /// @brief Serialize an array of structs into a FlatBuffer `vector` in sorted
-  /// order.
-  /// @tparam T The data type of the struct array elements.
-  /// @param[in] v A pointer to the array of type `T` to serialize into the
-  /// buffer as a `vector`.
-  /// @param[in] len The number of elements to serialize.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T>
-  Offset<Vector<const T *>> CreateVectorOfSortedStructs(T *v, size_t len) {
-    std::stable_sort(v, v + len, StructKeyComparator<T>());
-    return CreateVectorOfStructs(v, len);
-  }
-
-  /// @brief Serialize an array of native structs into a FlatBuffer `vector` in
-  /// sorted order.
-  /// @tparam T The data type of the struct array elements.
-  /// @tparam S The data type of the native struct array elements.
-  /// @param[in] v A pointer to the array of type `S` to serialize into the
-  /// buffer as a `vector`.
-  /// @param[in] len The number of elements to serialize.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T, typename S>
-  Offset<Vector<const T *>> CreateVectorOfSortedNativeStructs(S *v,
-                                                              size_t len) {
-    extern T Pack(const S &);
-    auto structs = StartVectorOfStructs<T>(len);
-    for (size_t i = 0; i < len; i++) { structs[i] = Pack(v[i]); }
-    std::stable_sort(structs, structs + len, StructKeyComparator<T>());
-    return EndVectorOfStructs<T>(len);
-  }
-
-  /// @cond FLATBUFFERS_INTERNAL
-  template<typename T> struct TableKeyComparator {
-    TableKeyComparator(vector_downward &buf) : buf_(buf) {}
-    TableKeyComparator(const TableKeyComparator &other) : buf_(other.buf_) {}
-    bool operator()(const Offset<T> &a, const Offset<T> &b) const {
-      auto table_a = reinterpret_cast<T *>(buf_.data_at(a.o));
-      auto table_b = reinterpret_cast<T *>(buf_.data_at(b.o));
-      return table_a->KeyCompareLessThan(table_b);
-    }
-    vector_downward &buf_;
-
-   private:
-    FLATBUFFERS_DELETE_FUNC(
-        TableKeyComparator &operator=(const TableKeyComparator &other));
-  };
-  /// @endcond
-
-  /// @brief Serialize an array of `table` offsets as a `vector` in the buffer
-  /// in sorted order.
-  /// @tparam T The data type that the offset refers to.
-  /// @param[in] v An array of type `Offset<T>` that contains the `table`
-  /// offsets to store in the buffer in sorted order.
-  /// @param[in] len The number of elements to store in the `vector`.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T>
-  Offset<Vector<Offset<T>>> CreateVectorOfSortedTables(Offset<T> *v,
-                                                       size_t len) {
-    std::stable_sort(v, v + len, TableKeyComparator<T>(buf_));
-    return CreateVector(v, len);
-  }
-
-  /// @brief Serialize an array of `table` offsets as a `vector` in the buffer
-  /// in sorted order.
-  /// @tparam T The data type that the offset refers to.
-  /// @param[in] v An array of type `Offset<T>` that contains the `table`
-  /// offsets to store in the buffer in sorted order.
-  /// @return Returns a typed `Offset` into the serialized data indicating
-  /// where the vector is stored.
-  template<typename T, typename Alloc = std::allocator<T>>
-  Offset<Vector<Offset<T>>> CreateVectorOfSortedTables(
-      std::vector<Offset<T>, Alloc> *v) {
-    return CreateVectorOfSortedTables(data(*v), v->size());
-  }
-
-  /// @brief Specialized version of `CreateVector` for non-copying use cases.
-  /// Write the data any time later to the returned buffer pointer `buf`.
-  /// @param[in] len The number of elements to store in the `vector`.
-  /// @param[in] elemsize The size of each element in the `vector`.
-  /// @param[out] buf A pointer to a `uint8_t` pointer that can be
-  /// written to at a later time to serialize the data into a `vector`
-  /// in the buffer.
-  uoffset_t CreateUninitializedVector(size_t len, size_t elemsize,
-                                      uint8_t **buf) {
-    NotNested();
-    StartVector(len, elemsize);
-    buf_.make_space(len * elemsize);
-    auto vec_start = GetSize();
-    auto vec_end = EndVector(len);
-    *buf = buf_.data_at(vec_start);
-    return vec_end;
-  }
-
-  /// @brief Specialized version of `CreateVector` for non-copying use cases.
-  /// Write the data any time later to the returned buffer pointer `buf`.
-  /// @tparam T The data type of the data that will be stored in the buffer
-  /// as a `vector`.
-  /// @param[in] len The number of elements to store in the `vector`.
-  /// @param[out] buf A pointer to a pointer of type `T` that can be
-  /// written to at a later time to serialize the data into a `vector`
-  /// in the buffer.
-  template<typename T>
-  Offset<Vector<T>> CreateUninitializedVector(size_t len, T **buf) {
-    AssertScalarT<T>();
-    return CreateUninitializedVector(len, sizeof(T),
-                                     reinterpret_cast<uint8_t **>(buf));
-  }
-
-  template<typename T>
-  Offset<Vector<const T *>> CreateUninitializedVectorOfStructs(size_t len,
-                                                               T **buf) {
-    return CreateUninitializedVector(len, sizeof(T),
-                                     reinterpret_cast<uint8_t **>(buf));
-  }
-
-  // @brief Create a vector of scalar type T given as input a vector of scalar
-  // type U, useful with e.g. pre "enum class" enums, or any existing scalar
-  // data of the wrong type.
-  template<typename T, typename U>
-  Offset<Vector<T>> CreateVectorScalarCast(const U *v, size_t len) {
-    AssertScalarT<T>();
-    AssertScalarT<U>();
-    StartVector(len, sizeof(T));
-    for (auto i = len; i > 0;) { PushElement(static_cast<T>(v[--i])); }
-    return Offset<Vector<T>>(EndVector(len));
-  }
-
-  /// @brief Write a struct by itself, typically to be part of a union.
-  template<typename T> Offset<const T *> CreateStruct(const T &structobj) {
-    NotNested();
-    Align(AlignOf<T>());
-    buf_.push_small(structobj);
-    return Offset<const T *>(GetSize());
-  }
-
-  /// @brief Finish serializing a buffer by writing the root offset.
-  /// @param[in] file_identifier If a `file_identifier` is given, the buffer
-  /// will be prefixed with a standard FlatBuffers file header.
-  template<typename T>
-  void Finish(Offset<T> root, const char *file_identifier = nullptr) {
-    Finish(root.o, file_identifier, false);
-  }
-
-  /// @brief Finish a buffer with a 32 bit size field pre-fixed (size of the
-  /// buffer following the size field). These buffers are NOT compatible
-  /// with standard buffers created by Finish, i.e. you can't call GetRoot
-  /// on them, you have to use GetSizePrefixedRoot instead.
-  /// All >32 bit quantities in this buffer will be aligned when the whole
-  /// size pre-fixed buffer is aligned.
-  /// These kinds of buffers are useful for creating a stream of FlatBuffers.
-  template<typename T>
-  void FinishSizePrefixed(Offset<T> root,
-                          const char *file_identifier = nullptr) {
-    Finish(root.o, file_identifier, true);
-  }
-
-  void SwapBufAllocator(FlatBufferBuilder &other) {
-    buf_.swap_allocator(other.buf_);
-  }
-
-  /// @brief The length of a FlatBuffer file header.
-  static const size_t kFileIdentifierLength =
-      ::flatbuffers::kFileIdentifierLength;
-
- protected:
-  // You shouldn't really be copying instances of this class.
-  FlatBufferBuilder(const FlatBufferBuilder &);
-  FlatBufferBuilder &operator=(const FlatBufferBuilder &);
-
-  void Finish(uoffset_t root, const char *file_identifier, bool size_prefix) {
-    NotNested();
-    buf_.clear_scratch();
-    // This will cause the whole buffer to be aligned.
-    PreAlign((size_prefix ? sizeof(uoffset_t) : 0) + sizeof(uoffset_t) +
-                 (file_identifier ? kFileIdentifierLength : 0),
-             minalign_);
-    if (file_identifier) {
-      FLATBUFFERS_ASSERT(strlen(file_identifier) == kFileIdentifierLength);
-      PushBytes(reinterpret_cast<const uint8_t *>(file_identifier),
-                kFileIdentifierLength);
-    }
-    PushElement(ReferTo(root));  // Location of root.
-    if (size_prefix) { PushElement(GetSize()); }
-    finished = true;
-  }
-
-  struct FieldLoc {
-    uoffset_t off;
-    voffset_t id;
-  };
-
-  vector_downward buf_;
-
-  // Accumulating offsets of table members while it is being built.
-  // We store these in the scratch pad of buf_, after the vtable offsets.
-  uoffset_t num_field_loc;
-  // Track how much of the vtable is in use, so we can output the most compact
-  // possible vtable.
-  voffset_t max_voffset_;
-
-  // Ensure objects are not nested.
-  bool nested;
-
-  // Ensure the buffer is finished before it is being accessed.
-  bool finished;
-
-  size_t minalign_;
-
-  bool force_defaults_;  // Serialize values equal to their defaults anyway.
-
-  bool dedup_vtables_;
-
-  struct StringOffsetCompare {
-    StringOffsetCompare(const vector_downward &buf) : buf_(&buf) {}
-    bool operator()(const Offset<String> &a, const Offset<String> &b) const {
-      auto stra = reinterpret_cast<const String *>(buf_->data_at(a.o));
-      auto strb = reinterpret_cast<const String *>(buf_->data_at(b.o));
-      return StringLessThan(stra->data(), stra->size(), strb->data(),
-                            strb->size());
-    }
-    const vector_downward *buf_;
-  };
-
-  // For use with CreateSharedString. Instantiated on first use only.
-  typedef std::set<Offset<String>, StringOffsetCompare> StringOffsetMap;
-  StringOffsetMap *string_pool;
-
- private:
-  // Allocates space for a vector of structures.
-  // Must be completed with EndVectorOfStructs().
-  template<typename T> T *StartVectorOfStructs(size_t vector_size) {
-    StartVector(vector_size * sizeof(T) / AlignOf<T>(), AlignOf<T>());
-    return reinterpret_cast<T *>(buf_.make_space(vector_size * sizeof(T)));
-  }
-
-  // End the vector of structures in the flatbuffers.
-  // Vector should have previously be started with StartVectorOfStructs().
-  template<typename T>
-  Offset<Vector<const T *>> EndVectorOfStructs(size_t vector_size) {
-    return Offset<Vector<const T *>>(EndVector(vector_size));
-  }
-};
-/// @}
-
-/// Helpers to get a typed pointer to objects that are currently being built.
-/// @warning Creating new objects will lead to reallocations and invalidates
-/// the pointer!
-template<typename T>
-T *GetMutableTemporaryPointer(FlatBufferBuilder &fbb, Offset<T> offset) {
-  return reinterpret_cast<T *>(fbb.GetCurrentBufferPointer() + fbb.GetSize() -
-                               offset.o);
-}
-
-template<typename T>
-const T *GetTemporaryPointer(FlatBufferBuilder &fbb, Offset<T> offset) {
-  return GetMutableTemporaryPointer<T>(fbb, offset);
-}
-
-template<typename T>
-void FlatBufferBuilder::Required(Offset<T> table, voffset_t field) {
-  auto table_ptr = reinterpret_cast<const Table *>(buf_.data_at(table.o));
-  bool ok = table_ptr->GetOptionalFieldOffset(field) != 0;
-  // If this fails, the caller will show what field needs to be set.
-  FLATBUFFERS_ASSERT(ok);
-  (void)ok;
-}
-
-}  // namespace flatbuffers
-
-#endif  // FLATBUFFERS_VECTOR_DOWNWARD_H_

code/components/tflite-lib/third_party/flatbuffers/include/flatbuffers/flatbuffers.h

@@ -1,270 +0,0 @@
-/*
- * Copyright 2014 Google Inc. All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef FLATBUFFERS_H_
-#define FLATBUFFERS_H_
-
-// TODO: These includes are for mitigating the pains of users editing their
-// source because they relied on flatbuffers.h to include everything for them.
-#include "flatbuffers/array.h"
-#include "flatbuffers/base.h"
-#include "flatbuffers/buffer.h"
-#include "flatbuffers/buffer_ref.h"
-#include "flatbuffers/detached_buffer.h"
-#include "flatbuffers/flatbuffer_builder.h"
-#include "flatbuffers/stl_emulation.h"
-#include "flatbuffers/string.h"
-#include "flatbuffers/struct.h"
-#include "flatbuffers/table.h"
-#include "flatbuffers/vector.h"
-#include "flatbuffers/vector_downward.h"
-#include "flatbuffers/verifier.h"
-
-namespace flatbuffers {
-
-/// @brief This can compute the start of a FlatBuffer from a root pointer, i.e.
-/// it is the opposite transformation of GetRoot().
-/// This may be useful if you want to pass on a root and have the recipient
-/// delete the buffer afterwards.
-inline const uint8_t *GetBufferStartFromRootPointer(const void *root) {
-  auto table = reinterpret_cast<const Table *>(root);
-  auto vtable = table->GetVTable();
-  // Either the vtable is before the root or after the root.
-  auto start = (std::min)(vtable, reinterpret_cast<const uint8_t *>(root));
-  // Align to at least sizeof(uoffset_t).
-  start = reinterpret_cast<const uint8_t *>(reinterpret_cast<uintptr_t>(start) &
-                                            ~(sizeof(uoffset_t) - 1));
-  // Additionally, there may be a file_identifier in the buffer, and the root
-  // offset. The buffer may have been aligned to any size between
-  // sizeof(uoffset_t) and FLATBUFFERS_MAX_ALIGNMENT (see "force_align").
-  // Sadly, the exact alignment is only known when constructing the buffer,
-  // since it depends on the presence of values with said alignment properties.
-  // So instead, we simply look at the next uoffset_t values (root,
-  // file_identifier, and alignment padding) to see which points to the root.
-  // None of the other values can "impersonate" the root since they will either
-  // be 0 or four ASCII characters.
-  static_assert(flatbuffers::kFileIdentifierLength == sizeof(uoffset_t),
-                "file_identifier is assumed to be the same size as uoffset_t");
-  for (auto possible_roots = FLATBUFFERS_MAX_ALIGNMENT / sizeof(uoffset_t) + 1;
-       possible_roots; possible_roots--) {
-    start -= sizeof(uoffset_t);
-    if (ReadScalar<uoffset_t>(start) + start ==
-        reinterpret_cast<const uint8_t *>(root))
-      return start;
-  }
-  // We didn't find the root, either the "root" passed isn't really a root,
-  // or the buffer is corrupt.
-  // Assert, because calling this function with bad data may cause reads
-  // outside of buffer boundaries.
-  FLATBUFFERS_ASSERT(false);
-  return nullptr;
-}
-
-/// @brief This return the prefixed size of a FlatBuffer.
-inline uoffset_t GetPrefixedSize(const uint8_t *buf) {
-  return ReadScalar<uoffset_t>(buf);
-}
-
-// Base class for native objects (FlatBuffer data de-serialized into native
-// C++ data structures).
-// Contains no functionality, purely documentative.
-struct NativeTable {};
-
-/// @brief Function types to be used with resolving hashes into objects and
-/// back again. The resolver gets a pointer to a field inside an object API
-/// object that is of the type specified in the schema using the attribute
-/// `cpp_type` (it is thus important whatever you write to this address
-/// matches that type). The value of this field is initially null, so you
-/// may choose to implement a delayed binding lookup using this function
-/// if you wish. The resolver does the opposite lookup, for when the object
-/// is being serialized again.
-typedef uint64_t hash_value_t;
-typedef std::function<void(void **pointer_adr, hash_value_t hash)>
-    resolver_function_t;
-typedef std::function<hash_value_t(void *pointer)> rehasher_function_t;
-
-// Helper function to test if a field is present, using any of the field
-// enums in the generated code.
-// `table` must be a generated table type. Since this is a template parameter,
-// this is not typechecked to be a subclass of Table, so beware!
-// Note: this function will return false for fields equal to the default
-// value, since they're not stored in the buffer (unless force_defaults was
-// used).
-template<typename T>
-bool IsFieldPresent(const T *table, typename T::FlatBuffersVTableOffset field) {
-  // Cast, since Table is a private baseclass of any table types.
-  return reinterpret_cast<const Table *>(table)->CheckField(
-      static_cast<voffset_t>(field));
-}
-
-// Utility function for reverse lookups on the EnumNames*() functions
-// (in the generated C++ code)
-// names must be NULL terminated.
-inline int LookupEnum(const char **names, const char *name) {
-  for (const char **p = names; *p; p++)
-    if (!strcmp(*p, name)) return static_cast<int>(p - names);
-  return -1;
-}
-
-// These macros allow us to layout a struct with a guarantee that they'll end
-// up looking the same on different compilers and platforms.
-// It does this by disallowing the compiler to do any padding, and then
-// does padding itself by inserting extra padding fields that make every
-// element aligned to its own size.
-// Additionally, it manually sets the alignment of the struct as a whole,
-// which is typically its largest element, or a custom size set in the schema
-// by the force_align attribute.
-// These are used in the generated code only.
-
-// clang-format off
-#if defined(_MSC_VER)
-  #define FLATBUFFERS_MANUALLY_ALIGNED_STRUCT(alignment) \
-    __pragma(pack(1)) \
-    struct __declspec(align(alignment))
-  #define FLATBUFFERS_STRUCT_END(name, size) \
-    __pragma(pack()) \
-    static_assert(sizeof(name) == size, "compiler breaks packing rules")
-#elif defined(__GNUC__) || defined(__clang__) || defined(__ICCARM__)
-  #define FLATBUFFERS_MANUALLY_ALIGNED_STRUCT(alignment) \
-    _Pragma("pack(1)") \
-    struct __attribute__((aligned(alignment)))
-  #define FLATBUFFERS_STRUCT_END(name, size) \
-    _Pragma("pack()") \
-    static_assert(sizeof(name) == size, "compiler breaks packing rules")
-#else
-  #error Unknown compiler, please define structure alignment macros
-#endif
-// clang-format on
-
-// Minimal reflection via code generation.
-// Besides full-fat reflection (see reflection.h) and parsing/printing by
-// loading schemas (see idl.h), we can also have code generation for minimal
-// reflection data which allows pretty-printing and other uses without needing
-// a schema or a parser.
-// Generate code with --reflect-types (types only) or --reflect-names (names
-// also) to enable.
-// See minireflect.h for utilities using this functionality.
-
-// These types are organized slightly differently as the ones in idl.h.
-enum SequenceType { ST_TABLE, ST_STRUCT, ST_UNION, ST_ENUM };
-
-// Scalars have the same order as in idl.h
-// clang-format off
-#define FLATBUFFERS_GEN_ELEMENTARY_TYPES(ET) \
-  ET(ET_UTYPE) \
-  ET(ET_BOOL) \
-  ET(ET_CHAR) \
-  ET(ET_UCHAR) \
-  ET(ET_SHORT) \
-  ET(ET_USHORT) \
-  ET(ET_INT) \
-  ET(ET_UINT) \
-  ET(ET_LONG) \
-  ET(ET_ULONG) \
-  ET(ET_FLOAT) \
-  ET(ET_DOUBLE) \
-  ET(ET_STRING) \
-  ET(ET_SEQUENCE)  // See SequenceType.
-
-enum ElementaryType {
-  #define FLATBUFFERS_ET(E) E,
-    FLATBUFFERS_GEN_ELEMENTARY_TYPES(FLATBUFFERS_ET)
-  #undef FLATBUFFERS_ET
-};
-
-inline const char * const *ElementaryTypeNames() {
-  static const char * const names[] = {
-    #define FLATBUFFERS_ET(E) #E,
-      FLATBUFFERS_GEN_ELEMENTARY_TYPES(FLATBUFFERS_ET)
-    #undef FLATBUFFERS_ET
-  };
-  return names;
-}
-// clang-format on
-
-// Basic type info cost just 16bits per field!
-// We're explicitly defining the signedness since the signedness of integer
-// bitfields is otherwise implementation-defined and causes warnings on older
-// GCC compilers.
-struct TypeCode {
-  // ElementaryType
-  unsigned short base_type : 4;
-  // Either vector (in table) or array (in struct)
-  unsigned short is_repeating : 1;
-  // Index into type_refs below, or -1 for none.
-  signed short sequence_ref : 11;
-};
-
-static_assert(sizeof(TypeCode) == 2, "TypeCode");
-
-struct TypeTable;
-
-// Signature of the static method present in each type.
-typedef const TypeTable *(*TypeFunction)();
-
-struct TypeTable {
-  SequenceType st;
-  size_t num_elems;  // of type_codes, values, names (but not type_refs).
-  const TypeCode *type_codes;     // num_elems count
-  const TypeFunction *type_refs;  // less than num_elems entries (see TypeCode).
-  const int16_t *array_sizes;     // less than num_elems entries (see TypeCode).
-  const int64_t *values;  // Only set for non-consecutive enum/union or structs.
-  const char *const *names;  // Only set if compiled with --reflect-names.
-};
-
-// String which identifies the current version of FlatBuffers.
-inline const char *flatbuffers_version_string() {
-  return "FlatBuffers " FLATBUFFERS_STRING(FLATBUFFERS_VERSION_MAJOR) "."
-      FLATBUFFERS_STRING(FLATBUFFERS_VERSION_MINOR) "."
-      FLATBUFFERS_STRING(FLATBUFFERS_VERSION_REVISION);
-}
-
-// clang-format off
-#define FLATBUFFERS_DEFINE_BITMASK_OPERATORS(E, T)\
-    inline E operator | (E lhs, E rhs){\
-        return E(T(lhs) | T(rhs));\
-    }\
-    inline E operator & (E lhs, E rhs){\
-        return E(T(lhs) & T(rhs));\
-    }\
-    inline E operator ^ (E lhs, E rhs){\
-        return E(T(lhs) ^ T(rhs));\
-    }\
-    inline E operator ~ (E lhs){\
-        return E(~T(lhs));\
-    }\
-    inline E operator |= (E &lhs, E rhs){\
-        lhs = lhs | rhs;\
-        return lhs;\
-    }\
-    inline E operator &= (E &lhs, E rhs){\
-        lhs = lhs & rhs;\
-        return lhs;\
-    }\
-    inline E operator ^= (E &lhs, E rhs){\
-        lhs = lhs ^ rhs;\
-        return lhs;\
-    }\
-    inline bool operator !(E rhs) \
-    {\
-        return !bool(T(rhs)); \
-    }
-/// @endcond
-}  // namespace flatbuffers
-
-// clang-format on
-
-#endif  // FLATBUFFERS_H_

code/components/tflite-lib/third_party/flatbuffers/include/flatbuffers/flexbuffers.h

@@ -1,1903 +0,0 @@
-/*
- * Copyright 2017 Google Inc. All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef FLATBUFFERS_FLEXBUFFERS_H_
-#define FLATBUFFERS_FLEXBUFFERS_H_
-
-#include <map>
-// Used to select STL variant.
-#include "flatbuffers/base.h"
-// We use the basic binary writing functions from the regular FlatBuffers.
-#include "flatbuffers/util.h"
-
-#ifdef _MSC_VER
-#  include <intrin.h>
-#endif
-
-#if defined(_MSC_VER)
-#  pragma warning(push)
-#  pragma warning(disable : 4127)  // C4127: conditional expression is constant
-#endif
-
-namespace flexbuffers {
-
-class Reference;
-class Map;
-
-// These are used in the lower 2 bits of a type field to determine the size of
-// the elements (and or size field) of the item pointed to (e.g. vector).
-enum BitWidth {
-  BIT_WIDTH_8 = 0,
-  BIT_WIDTH_16 = 1,
-  BIT_WIDTH_32 = 2,
-  BIT_WIDTH_64 = 3,
-};
-
-// These are used as the upper 6 bits of a type field to indicate the actual
-// type.
-enum Type {
-  FBT_NULL = 0,
-  FBT_INT = 1,
-  FBT_UINT = 2,
-  FBT_FLOAT = 3,
-  // Types above stored inline, types below (except FBT_BOOL) store an offset.
-  FBT_KEY = 4,
-  FBT_STRING = 5,
-  FBT_INDIRECT_INT = 6,
-  FBT_INDIRECT_UINT = 7,
-  FBT_INDIRECT_FLOAT = 8,
-  FBT_MAP = 9,
-  FBT_VECTOR = 10,      // Untyped.
-  FBT_VECTOR_INT = 11,  // Typed any size (stores no type table).
-  FBT_VECTOR_UINT = 12,
-  FBT_VECTOR_FLOAT = 13,
-  FBT_VECTOR_KEY = 14,
-  // DEPRECATED, use FBT_VECTOR or FBT_VECTOR_KEY instead.
-  // Read test.cpp/FlexBuffersDeprecatedTest() for details on why.
-  FBT_VECTOR_STRING_DEPRECATED = 15,
-  FBT_VECTOR_INT2 = 16,  // Typed tuple (no type table, no size field).
-  FBT_VECTOR_UINT2 = 17,
-  FBT_VECTOR_FLOAT2 = 18,
-  FBT_VECTOR_INT3 = 19,  // Typed triple (no type table, no size field).
-  FBT_VECTOR_UINT3 = 20,
-  FBT_VECTOR_FLOAT3 = 21,
-  FBT_VECTOR_INT4 = 22,  // Typed quad (no type table, no size field).
-  FBT_VECTOR_UINT4 = 23,
-  FBT_VECTOR_FLOAT4 = 24,
-  FBT_BLOB = 25,
-  FBT_BOOL = 26,
-  FBT_VECTOR_BOOL =
-      36,  // To Allow the same type of conversion of type to vector type
-
-  FBT_MAX_TYPE = 37
-};
-
-inline bool IsInline(Type t) { return t <= FBT_FLOAT || t == FBT_BOOL; }
-
-inline bool IsTypedVectorElementType(Type t) {
-  return (t >= FBT_INT && t <= FBT_STRING) || t == FBT_BOOL;
-}
-
-inline bool IsTypedVector(Type t) {
-  return (t >= FBT_VECTOR_INT && t <= FBT_VECTOR_STRING_DEPRECATED) ||
-         t == FBT_VECTOR_BOOL;
-}
-
-inline bool IsFixedTypedVector(Type t) {
-  return t >= FBT_VECTOR_INT2 && t <= FBT_VECTOR_FLOAT4;
-}
-
-inline Type ToTypedVector(Type t, size_t fixed_len = 0) {
-  FLATBUFFERS_ASSERT(IsTypedVectorElementType(t));
-  switch (fixed_len) {
-    case 0: return static_cast<Type>(t - FBT_INT + FBT_VECTOR_INT);
-    case 2: return static_cast<Type>(t - FBT_INT + FBT_VECTOR_INT2);
-    case 3: return static_cast<Type>(t - FBT_INT + FBT_VECTOR_INT3);
-    case 4: return static_cast<Type>(t - FBT_INT + FBT_VECTOR_INT4);
-    default: FLATBUFFERS_ASSERT(0); return FBT_NULL;
-  }
-}
-
-inline Type ToTypedVectorElementType(Type t) {
-  FLATBUFFERS_ASSERT(IsTypedVector(t));
-  return static_cast<Type>(t - FBT_VECTOR_INT + FBT_INT);
-}
-
-inline Type ToFixedTypedVectorElementType(Type t, uint8_t *len) {
-  FLATBUFFERS_ASSERT(IsFixedTypedVector(t));
-  auto fixed_type = t - FBT_VECTOR_INT2;
-  *len = static_cast<uint8_t>(fixed_type / 3 +
-                              2);  // 3 types each, starting from length 2.
-  return static_cast<Type>(fixed_type % 3 + FBT_INT);
-}
-
-// TODO: implement proper support for 8/16bit floats, or decide not to
-// support them.
-typedef int16_t half;
-typedef int8_t quarter;
-
-// TODO: can we do this without conditionals using intrinsics or inline asm
-// on some platforms? Given branch prediction the method below should be
-// decently quick, but it is the most frequently executed function.
-// We could do an (unaligned) 64-bit read if we ifdef out the platforms for
-// which that doesn't work (or where we'd read into un-owned memory).
-template<typename R, typename T1, typename T2, typename T4, typename T8>
-R ReadSizedScalar(const uint8_t *data, uint8_t byte_width) {
-  return byte_width < 4
-             ? (byte_width < 2
-                    ? static_cast<R>(flatbuffers::ReadScalar<T1>(data))
-                    : static_cast<R>(flatbuffers::ReadScalar<T2>(data)))
-             : (byte_width < 8
-                    ? static_cast<R>(flatbuffers::ReadScalar<T4>(data))
-                    : static_cast<R>(flatbuffers::ReadScalar<T8>(data)));
-}
-
-inline int64_t ReadInt64(const uint8_t *data, uint8_t byte_width) {
-  return ReadSizedScalar<int64_t, int8_t, int16_t, int32_t, int64_t>(
-      data, byte_width);
-}
-
-inline uint64_t ReadUInt64(const uint8_t *data, uint8_t byte_width) {
-  // This is the "hottest" function (all offset lookups use this), so worth
-  // optimizing if possible.
-  // TODO: GCC apparently replaces memcpy by a rep movsb, but only if count is a
-  // constant, which here it isn't. Test if memcpy is still faster than
-  // the conditionals in ReadSizedScalar. Can also use inline asm.
-
-  // clang-format off
-  #if defined(_MSC_VER) && defined(_M_X64) && !defined(_M_ARM64EC)
-  // This is 64-bit Windows only, __movsb does not work on 32-bit Windows.
-    uint64_t u = 0;
-    __movsb(reinterpret_cast<uint8_t *>(&u),
-            reinterpret_cast<const uint8_t *>(data), byte_width);
-    return flatbuffers::EndianScalar(u);
-  #else
-    return ReadSizedScalar<uint64_t, uint8_t, uint16_t, uint32_t, uint64_t>(
-             data, byte_width);
-  #endif
-  // clang-format on
-}
-
-inline double ReadDouble(const uint8_t *data, uint8_t byte_width) {
-  return ReadSizedScalar<double, quarter, half, float, double>(data,
-                                                               byte_width);
-}
-
-inline const uint8_t *Indirect(const uint8_t *offset, uint8_t byte_width) {
-  return offset - ReadUInt64(offset, byte_width);
-}
-
-template<typename T> const uint8_t *Indirect(const uint8_t *offset) {
-  return offset - flatbuffers::ReadScalar<T>(offset);
-}
-
-inline BitWidth WidthU(uint64_t u) {
-#define FLATBUFFERS_GET_FIELD_BIT_WIDTH(value, width)                   \
-  {                                                                     \
-    if (!((u) & ~((1ULL << (width)) - 1ULL))) return BIT_WIDTH_##width; \
-  }
-  FLATBUFFERS_GET_FIELD_BIT_WIDTH(u, 8);
-  FLATBUFFERS_GET_FIELD_BIT_WIDTH(u, 16);
-  FLATBUFFERS_GET_FIELD_BIT_WIDTH(u, 32);
-#undef FLATBUFFERS_GET_FIELD_BIT_WIDTH
-  return BIT_WIDTH_64;
-}
-
-inline BitWidth WidthI(int64_t i) {
-  auto u = static_cast<uint64_t>(i) << 1;
-  return WidthU(i >= 0 ? u : ~u);
-}
-
-inline BitWidth WidthF(double f) {
-  return static_cast<double>(static_cast<float>(f)) == f ? BIT_WIDTH_32
-                                                         : BIT_WIDTH_64;
-}
-
-// Base class of all types below.
-// Points into the data buffer and allows access to one type.
-class Object {
- public:
-  Object(const uint8_t *data, uint8_t byte_width)
-      : data_(data), byte_width_(byte_width) {}
-
- protected:
-  const uint8_t *data_;
-  uint8_t byte_width_;
-};
-
-// Object that has a size, obtained either from size prefix, or elsewhere.
-class Sized : public Object {
- public:
-  // Size prefix.
-  Sized(const uint8_t *data, uint8_t byte_width)
-      : Object(data, byte_width), size_(read_size()) {}
-  // Manual size.
-  Sized(const uint8_t *data, uint8_t byte_width, size_t sz)
-      : Object(data, byte_width), size_(sz) {}
-  size_t size() const { return size_; }
-  // Access size stored in `byte_width_` bytes before data_ pointer.
-  size_t read_size() const {
-    return static_cast<size_t>(ReadUInt64(data_ - byte_width_, byte_width_));
-  }
-
- protected:
-  size_t size_;
-};
-
-class String : public Sized {
- public:
-  // Size prefix.
-  String(const uint8_t *data, uint8_t byte_width) : Sized(data, byte_width) {}
-  // Manual size.
-  String(const uint8_t *data, uint8_t byte_width, size_t sz)
-      : Sized(data, byte_width, sz) {}
-
-  size_t length() const { return size(); }
-  const char *c_str() const { return reinterpret_cast<const char *>(data_); }
-  std::string str() const { return std::string(c_str(), size()); }
-
-  static String EmptyString() {
-    static const char *empty_string = "";
-    return String(reinterpret_cast<const uint8_t *>(empty_string), 1, 0);
-  }
-  bool IsTheEmptyString() const { return data_ == EmptyString().data_; }
-};
-
-class Blob : public Sized {
- public:
-  Blob(const uint8_t *data_buf, uint8_t byte_width)
-      : Sized(data_buf, byte_width) {}
-
-  static Blob EmptyBlob() {
-    static const uint8_t empty_blob[] = { 0 /*len*/ };
-    return Blob(empty_blob + 1, 1);
-  }
-  bool IsTheEmptyBlob() const { return data_ == EmptyBlob().data_; }
-  const uint8_t *data() const { return data_; }
-};
-
-class Vector : public Sized {
- public:
-  Vector(const uint8_t *data, uint8_t byte_width) : Sized(data, byte_width) {}
-
-  Reference operator[](size_t i) const;
-
-  static Vector EmptyVector() {
-    static const uint8_t empty_vector[] = { 0 /*len*/ };
-    return Vector(empty_vector + 1, 1);
-  }
-  bool IsTheEmptyVector() const { return data_ == EmptyVector().data_; }
-};
-
-class TypedVector : public Sized {
- public:
-  TypedVector(const uint8_t *data, uint8_t byte_width, Type element_type)
-      : Sized(data, byte_width), type_(element_type) {}
-
-  Reference operator[](size_t i) const;
-
-  static TypedVector EmptyTypedVector() {
-    static const uint8_t empty_typed_vector[] = { 0 /*len*/ };
-    return TypedVector(empty_typed_vector + 1, 1, FBT_INT);
-  }
-  bool IsTheEmptyVector() const {
-    return data_ == TypedVector::EmptyTypedVector().data_;
-  }
-
-  Type ElementType() { return type_; }
-
-  friend Reference;
-
- private:
-  Type type_;
-
-  friend Map;
-};
-
-class FixedTypedVector : public Object {
- public:
-  FixedTypedVector(const uint8_t *data, uint8_t byte_width, Type element_type,
-                   uint8_t len)
-      : Object(data, byte_width), type_(element_type), len_(len) {}
-
-  Reference operator[](size_t i) const;
-
-  static FixedTypedVector EmptyFixedTypedVector() {
-    static const uint8_t fixed_empty_vector[] = { 0 /* unused */ };
-    return FixedTypedVector(fixed_empty_vector, 1, FBT_INT, 0);
-  }
-  bool IsTheEmptyFixedTypedVector() const {
-    return data_ == FixedTypedVector::EmptyFixedTypedVector().data_;
-  }
-
-  Type ElementType() const { return type_; }
-  uint8_t size() const { return len_; }
-
- private:
-  Type type_;
-  uint8_t len_;
-};
-
-class Map : public Vector {
- public:
-  Map(const uint8_t *data, uint8_t byte_width) : Vector(data, byte_width) {}
-
-  Reference operator[](const char *key) const;
-  Reference operator[](const std::string &key) const;
-
-  Vector Values() const { return Vector(data_, byte_width_); }
-
-  TypedVector Keys() const {
-    const size_t num_prefixed_fields = 3;
-    auto keys_offset = data_ - byte_width_ * num_prefixed_fields;
-    return TypedVector(Indirect(keys_offset, byte_width_),
-                       static_cast<uint8_t>(
-                           ReadUInt64(keys_offset + byte_width_, byte_width_)),
-                       FBT_KEY);
-  }
-
-  static Map EmptyMap() {
-    static const uint8_t empty_map[] = {
-      0 /*keys_len*/, 0 /*keys_offset*/, 1 /*keys_width*/, 0 /*len*/
-    };
-    return Map(empty_map + 4, 1);
-  }
-
-  bool IsTheEmptyMap() const { return data_ == EmptyMap().data_; }
-};
-
-template<typename T>
-void AppendToString(std::string &s, T &&v, bool keys_quoted) {
-  s += "[ ";
-  for (size_t i = 0; i < v.size(); i++) {
-    if (i) s += ", ";
-    v[i].ToString(true, keys_quoted, s);
-  }
-  s += " ]";
-}
-
-class Reference {
- public:
-  Reference()
-      : data_(nullptr), parent_width_(0), byte_width_(0), type_(FBT_NULL) {}
-
-  Reference(const uint8_t *data, uint8_t parent_width, uint8_t byte_width,
-            Type type)
-      : data_(data),
-        parent_width_(parent_width),
-        byte_width_(byte_width),
-        type_(type) {}
-
-  Reference(const uint8_t *data, uint8_t parent_width, uint8_t packed_type)
-      : data_(data), parent_width_(parent_width) {
-    byte_width_ = 1U << static_cast<BitWidth>(packed_type & 3);
-    type_ = static_cast<Type>(packed_type >> 2);
-  }
-
-  Type GetType() const { return type_; }
-
-  bool IsNull() const { return type_ == FBT_NULL; }
-  bool IsBool() const { return type_ == FBT_BOOL; }
-  bool IsInt() const { return type_ == FBT_INT || type_ == FBT_INDIRECT_INT; }
-  bool IsUInt() const {
-    return type_ == FBT_UINT || type_ == FBT_INDIRECT_UINT;
-  }
-  bool IsIntOrUint() const { return IsInt() || IsUInt(); }
-  bool IsFloat() const {
-    return type_ == FBT_FLOAT || type_ == FBT_INDIRECT_FLOAT;
-  }
-  bool IsNumeric() const { return IsIntOrUint() || IsFloat(); }
-  bool IsString() const { return type_ == FBT_STRING; }
-  bool IsKey() const { return type_ == FBT_KEY; }
-  bool IsVector() const { return type_ == FBT_VECTOR || type_ == FBT_MAP; }
-  bool IsUntypedVector() const { return type_ == FBT_VECTOR; }
-  bool IsTypedVector() const { return flexbuffers::IsTypedVector(type_); }
-  bool IsFixedTypedVector() const {
-    return flexbuffers::IsFixedTypedVector(type_);
-  }
-  bool IsAnyVector() const {
-    return (IsTypedVector() || IsFixedTypedVector() || IsVector());
-  }
-  bool IsMap() const { return type_ == FBT_MAP; }
-  bool IsBlob() const { return type_ == FBT_BLOB; }
-  bool AsBool() const {
-    return (type_ == FBT_BOOL ? ReadUInt64(data_, parent_width_)
-                              : AsUInt64()) != 0;
-  }
-
-  // Reads any type as a int64_t. Never fails, does most sensible conversion.
-  // Truncates floats, strings are attempted to be parsed for a number,
-  // vectors/maps return their size. Returns 0 if all else fails.
-  int64_t AsInt64() const {
-    if (type_ == FBT_INT) {
-      // A fast path for the common case.
-      return ReadInt64(data_, parent_width_);
-    } else
-      switch (type_) {
-        case FBT_INDIRECT_INT: return ReadInt64(Indirect(), byte_width_);
-        case FBT_UINT: return ReadUInt64(data_, parent_width_);
-        case FBT_INDIRECT_UINT: return ReadUInt64(Indirect(), byte_width_);
-        case FBT_FLOAT:
-          return static_cast<int64_t>(ReadDouble(data_, parent_width_));
-        case FBT_INDIRECT_FLOAT:
-          return static_cast<int64_t>(ReadDouble(Indirect(), byte_width_));
-        case FBT_NULL: return 0;
-        case FBT_STRING: return flatbuffers::StringToInt(AsString().c_str());
-        case FBT_VECTOR: return static_cast<int64_t>(AsVector().size());
-        case FBT_BOOL: return ReadInt64(data_, parent_width_);
-        default:
-          // Convert other things to int.
-          return 0;
-      }
-  }
-
-  // TODO: could specialize these to not use AsInt64() if that saves
-  // extension ops in generated code, and use a faster op than ReadInt64.
-  int32_t AsInt32() const { return static_cast<int32_t>(AsInt64()); }
-  int16_t AsInt16() const { return static_cast<int16_t>(AsInt64()); }
-  int8_t AsInt8() const { return static_cast<int8_t>(AsInt64()); }
-
-  uint64_t AsUInt64() const {
-    if (type_ == FBT_UINT) {
-      // A fast path for the common case.
-      return ReadUInt64(data_, parent_width_);
-    } else
-      switch (type_) {
-        case FBT_INDIRECT_UINT: return ReadUInt64(Indirect(), byte_width_);
-        case FBT_INT: return ReadInt64(data_, parent_width_);
-        case FBT_INDIRECT_INT: return ReadInt64(Indirect(), byte_width_);
-        case FBT_FLOAT:
-          return static_cast<uint64_t>(ReadDouble(data_, parent_width_));
-        case FBT_INDIRECT_FLOAT:
-          return static_cast<uint64_t>(ReadDouble(Indirect(), byte_width_));
-        case FBT_NULL: return 0;
-        case FBT_STRING: return flatbuffers::StringToUInt(AsString().c_str());
-        case FBT_VECTOR: return static_cast<uint64_t>(AsVector().size());
-        case FBT_BOOL: return ReadUInt64(data_, parent_width_);
-        default:
-          // Convert other things to uint.
-          return 0;
-      }
-  }
-
-  uint32_t AsUInt32() const { return static_cast<uint32_t>(AsUInt64()); }
-  uint16_t AsUInt16() const { return static_cast<uint16_t>(AsUInt64()); }
-  uint8_t AsUInt8() const { return static_cast<uint8_t>(AsUInt64()); }
-
-  double AsDouble() const {
-    if (type_ == FBT_FLOAT) {
-      // A fast path for the common case.
-      return ReadDouble(data_, parent_width_);
-    } else
-      switch (type_) {
-        case FBT_INDIRECT_FLOAT: return ReadDouble(Indirect(), byte_width_);
-        case FBT_INT:
-          return static_cast<double>(ReadInt64(data_, parent_width_));
-        case FBT_UINT:
-          return static_cast<double>(ReadUInt64(data_, parent_width_));
-        case FBT_INDIRECT_INT:
-          return static_cast<double>(ReadInt64(Indirect(), byte_width_));
-        case FBT_INDIRECT_UINT:
-          return static_cast<double>(ReadUInt64(Indirect(), byte_width_));
-        case FBT_NULL: return 0.0;
-        case FBT_STRING: {
-#if 1
-#if !defined( _MSC_VER)
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wnull-dereference"
-#endif
-          // See b/173239141 for additional context. Patched via
-          // micro/tools/make/flexbuffers_download.sh
-          // Introduce a segfault for an unsupported code path for TFLM.
-          return *(static_cast<double*>(nullptr));
-#if !defined( _MSC_VER)
-#pragma GCC diagnostic pop
-#endif
-#else
-          // This is the original code
-          double d;
-          flatbuffers::StringToNumber(AsString().c_str(), &d);
-          return d;
-#endif
-        }
-        case FBT_VECTOR: return static_cast<double>(AsVector().size());
-        case FBT_BOOL:
-          return static_cast<double>(ReadUInt64(data_, parent_width_));
-        default:
-          // Convert strings and other things to float.
-          return 0;
-      }
-  }
-
-  float AsFloat() const { return static_cast<float>(AsDouble()); }
-
-  const char *AsKey() const {
-    if (type_ == FBT_KEY || type_ == FBT_STRING) {
-      return reinterpret_cast<const char *>(Indirect());
-    } else {
-      return "";
-    }
-  }
-
-  // This function returns the empty string if you try to read something that
-  // is not a string or key.
-  String AsString() const {
-    if (type_ == FBT_STRING) {
-      return String(Indirect(), byte_width_);
-    } else if (type_ == FBT_KEY) {
-      auto key = Indirect();
-      return String(key, byte_width_,
-                    strlen(reinterpret_cast<const char *>(key)));
-    } else {
-      return String::EmptyString();
-    }
-  }
-
-  // Unlike AsString(), this will convert any type to a std::string.
-  std::string ToString() const {
-    std::string s;
-    ToString(false, false, s);
-    return s;
-  }
-
-  // Convert any type to a JSON-like string. strings_quoted determines if
-  // string values at the top level receive "" quotes (inside other values
-  // they always do). keys_quoted determines if keys are quoted, at any level.
-  // TODO(wvo): add further options to have indentation/newlines.
-  void ToString(bool strings_quoted, bool keys_quoted, std::string &s) const {
-    if (type_ == FBT_STRING) {
-      String str(Indirect(), byte_width_);
-      if (strings_quoted) {
-        flatbuffers::EscapeString(str.c_str(), str.length(), &s, true, false);
-      } else {
-        s.append(str.c_str(), str.length());
-      }
-    } else if (IsKey()) {
-      auto str = AsKey();
-      if (keys_quoted) {
-        flatbuffers::EscapeString(str, strlen(str), &s, true, false);
-      } else {
-        s += str;
-      }
-    } else if (IsInt()) {
-      s += flatbuffers::NumToString(AsInt64());
-    } else if (IsUInt()) {
-      s += flatbuffers::NumToString(AsUInt64());
-    } else if (IsFloat()) {
-      s += flatbuffers::NumToString(AsDouble());
-    } else if (IsNull()) {
-      s += "null";
-    } else if (IsBool()) {
-      s += AsBool() ? "true" : "false";
-    } else if (IsMap()) {
-      s += "{ ";
-      auto m = AsMap();
-      auto keys = m.Keys();
-      auto vals = m.Values();
-      for (size_t i = 0; i < keys.size(); i++) {
-        bool kq = keys_quoted;
-        if (!kq) {
-          // FlexBuffers keys may contain arbitrary characters, only allow
-          // unquoted if it looks like an "identifier":
-          const char *p = keys[i].AsKey();
-          if (!flatbuffers::is_alpha(*p) && *p != '_') {
-            kq = true;
-          } else {
-            while (*++p) {
-              if (!flatbuffers::is_alnum(*p) && *p != '_') {
-                kq = true;
-                break;
-              }
-            }
-          }
-        }
-        keys[i].ToString(true, kq, s);
-        s += ": ";
-        vals[i].ToString(true, keys_quoted, s);
-        if (i < keys.size() - 1) s += ", ";
-      }
-      s += " }";
-    } else if (IsVector()) {
-      AppendToString<Vector>(s, AsVector(), keys_quoted);
-    } else if (IsTypedVector()) {
-      AppendToString<TypedVector>(s, AsTypedVector(), keys_quoted);
-    } else if (IsFixedTypedVector()) {
-      AppendToString<FixedTypedVector>(s, AsFixedTypedVector(), keys_quoted);
-    } else if (IsBlob()) {
-      auto blob = AsBlob();
-      flatbuffers::EscapeString(reinterpret_cast<const char *>(blob.data()),
-                                blob.size(), &s, true, false);
-    } else {
-      s += "(?)";
-    }
-  }
-
-  // This function returns the empty blob if you try to read a not-blob.
-  // Strings can be viewed as blobs too.
-  Blob AsBlob() const {
-    if (type_ == FBT_BLOB || type_ == FBT_STRING) {
-      return Blob(Indirect(), byte_width_);
-    } else {
-      return Blob::EmptyBlob();
-    }
-  }
-
-  // This function returns the empty vector if you try to read a not-vector.
-  // Maps can be viewed as vectors too.
-  Vector AsVector() const {
-    if (type_ == FBT_VECTOR || type_ == FBT_MAP) {
-      return Vector(Indirect(), byte_width_);
-    } else {
-      return Vector::EmptyVector();
-    }
-  }
-
-  TypedVector AsTypedVector() const {
-    if (IsTypedVector()) {
-      auto tv =
-          TypedVector(Indirect(), byte_width_, ToTypedVectorElementType(type_));
-      if (tv.type_ == FBT_STRING) {
-        // These can't be accessed as strings, since we don't know the bit-width
-        // of the size field, see the declaration of
-        // FBT_VECTOR_STRING_DEPRECATED above for details.
-        // We change the type here to be keys, which are a subtype of strings,
-        // and will ignore the size field. This will truncate strings with
-        // embedded nulls.
-        tv.type_ = FBT_KEY;
-      }
-      return tv;
-    } else {
-      return TypedVector::EmptyTypedVector();
-    }
-  }
-
-  FixedTypedVector AsFixedTypedVector() const {
-    if (IsFixedTypedVector()) {
-      uint8_t len = 0;
-      auto vtype = ToFixedTypedVectorElementType(type_, &len);
-      return FixedTypedVector(Indirect(), byte_width_, vtype, len);
-    } else {
-      return FixedTypedVector::EmptyFixedTypedVector();
-    }
-  }
-
-  Map AsMap() const {
-    if (type_ == FBT_MAP) {
-      return Map(Indirect(), byte_width_);
-    } else {
-      return Map::EmptyMap();
-    }
-  }
-
-  template<typename T> T As() const;
-
-  // Experimental: Mutation functions.
-  // These allow scalars in an already created buffer to be updated in-place.
-  // Since by default scalars are stored in the smallest possible space,
-  // the new value may not fit, in which case these functions return false.
-  // To avoid this, you can construct the values you intend to mutate using
-  // Builder::ForceMinimumBitWidth.
-  bool MutateInt(int64_t i) {
-    if (type_ == FBT_INT) {
-      return Mutate(data_, i, parent_width_, WidthI(i));
-    } else if (type_ == FBT_INDIRECT_INT) {
-      return Mutate(Indirect(), i, byte_width_, WidthI(i));
-    } else if (type_ == FBT_UINT) {
-      auto u = static_cast<uint64_t>(i);
-      return Mutate(data_, u, parent_width_, WidthU(u));
-    } else if (type_ == FBT_INDIRECT_UINT) {
-      auto u = static_cast<uint64_t>(i);
-      return Mutate(Indirect(), u, byte_width_, WidthU(u));
-    } else {
-      return false;
-    }
-  }
-
-  bool MutateBool(bool b) {
-    return type_ == FBT_BOOL && Mutate(data_, b, parent_width_, BIT_WIDTH_8);
-  }
-
-  bool MutateUInt(uint64_t u) {
-    if (type_ == FBT_UINT) {
-      return Mutate(data_, u, parent_width_, WidthU(u));
-    } else if (type_ == FBT_INDIRECT_UINT) {
-      return Mutate(Indirect(), u, byte_width_, WidthU(u));
-    } else if (type_ == FBT_INT) {
-      auto i = static_cast<int64_t>(u);
-      return Mutate(data_, i, parent_width_, WidthI(i));
-    } else if (type_ == FBT_INDIRECT_INT) {
-      auto i = static_cast<int64_t>(u);
-      return Mutate(Indirect(), i, byte_width_, WidthI(i));
-    } else {
-      return false;
-    }
-  }
-
-  bool MutateFloat(float f) {
-    if (type_ == FBT_FLOAT) {
-      return MutateF(data_, f, parent_width_, BIT_WIDTH_32);
-    } else if (type_ == FBT_INDIRECT_FLOAT) {
-      return MutateF(Indirect(), f, byte_width_, BIT_WIDTH_32);
-    } else {
-      return false;
-    }
-  }
-
-  bool MutateFloat(double d) {
-    if (type_ == FBT_FLOAT) {
-      return MutateF(data_, d, parent_width_, WidthF(d));
-    } else if (type_ == FBT_INDIRECT_FLOAT) {
-      return MutateF(Indirect(), d, byte_width_, WidthF(d));
-    } else {
-      return false;
-    }
-  }
-
-  bool MutateString(const char *str, size_t len) {
-    auto s = AsString();
-    if (s.IsTheEmptyString()) return false;
-    // This is very strict, could allow shorter strings, but that creates
-    // garbage.
-    if (s.length() != len) return false;
-    memcpy(const_cast<char *>(s.c_str()), str, len);
-    return true;
-  }
-  bool MutateString(const char *str) { return MutateString(str, strlen(str)); }
-  bool MutateString(const std::string &str) {
-    return MutateString(str.data(), str.length());
-  }
-
- private:
-  const uint8_t *Indirect() const {
-    return flexbuffers::Indirect(data_, parent_width_);
-  }
-
-  template<typename T>
-  bool Mutate(const uint8_t *dest, T t, size_t byte_width,
-              BitWidth value_width) {
-    auto fits = static_cast<size_t>(static_cast<size_t>(1U) << value_width) <=
-                byte_width;
-    if (fits) {
-      t = flatbuffers::EndianScalar(t);
-      memcpy(const_cast<uint8_t *>(dest), &t, byte_width);
-    }
-    return fits;
-  }
-
-  template<typename T>
-  bool MutateF(const uint8_t *dest, T t, size_t byte_width,
-               BitWidth value_width) {
-    if (byte_width == sizeof(double))
-      return Mutate(dest, static_cast<double>(t), byte_width, value_width);
-    if (byte_width == sizeof(float))
-      return Mutate(dest, static_cast<float>(t), byte_width, value_width);
-    FLATBUFFERS_ASSERT(false);
-    return false;
-  }
-
-  friend class Verifier;
-
-  const uint8_t *data_;
-  uint8_t parent_width_;
-  uint8_t byte_width_;
-  Type type_;
-};
-
-// Template specialization for As().
-template<> inline bool Reference::As<bool>() const { return AsBool(); }
-
-template<> inline int8_t Reference::As<int8_t>() const { return AsInt8(); }
-template<> inline int16_t Reference::As<int16_t>() const { return AsInt16(); }
-template<> inline int32_t Reference::As<int32_t>() const { return AsInt32(); }
-template<> inline int64_t Reference::As<int64_t>() const { return AsInt64(); }
-
-template<> inline uint8_t Reference::As<uint8_t>() const { return AsUInt8(); }
-template<> inline uint16_t Reference::As<uint16_t>() const {
-  return AsUInt16();
-}
-template<> inline uint32_t Reference::As<uint32_t>() const {
-  return AsUInt32();
-}
-template<> inline uint64_t Reference::As<uint64_t>() const {
-  return AsUInt64();
-}
-
-template<> inline double Reference::As<double>() const { return AsDouble(); }
-template<> inline float Reference::As<float>() const { return AsFloat(); }
-
-template<> inline String Reference::As<String>() const { return AsString(); }
-template<> inline std::string Reference::As<std::string>() const {
-  return AsString().str();
-}
-
-template<> inline Blob Reference::As<Blob>() const { return AsBlob(); }
-template<> inline Vector Reference::As<Vector>() const { return AsVector(); }
-template<> inline TypedVector Reference::As<TypedVector>() const {
-  return AsTypedVector();
-}
-template<> inline FixedTypedVector Reference::As<FixedTypedVector>() const {
-  return AsFixedTypedVector();
-}
-template<> inline Map Reference::As<Map>() const { return AsMap(); }
-
-inline uint8_t PackedType(BitWidth bit_width, Type type) {
-  return static_cast<uint8_t>(bit_width | (type << 2));
-}
-
-inline uint8_t NullPackedType() { return PackedType(BIT_WIDTH_8, FBT_NULL); }
-
-// Vector accessors.
-// Note: if you try to access outside of bounds, you get a Null value back
-// instead. Normally this would be an assert, but since this is "dynamically
-// typed" data, you may not want that (someone sends you a 2d vector and you
-// wanted 3d).
-// The Null converts seamlessly into a default value for any other type.
-// TODO(wvo): Could introduce an #ifdef that makes this into an assert?
-inline Reference Vector::operator[](size_t i) const {
-  auto len = size();
-  if (i >= len) return Reference(nullptr, 1, NullPackedType());
-  auto packed_type = (data_ + len * byte_width_)[i];
-  auto elem = data_ + i * byte_width_;
-  return Reference(elem, byte_width_, packed_type);
-}
-
-inline Reference TypedVector::operator[](size_t i) const {
-  auto len = size();
-  if (i >= len) return Reference(nullptr, 1, NullPackedType());
-  auto elem = data_ + i * byte_width_;
-  return Reference(elem, byte_width_, 1, type_);
-}
-
-inline Reference FixedTypedVector::operator[](size_t i) const {
-  if (i >= len_) return Reference(nullptr, 1, NullPackedType());
-  auto elem = data_ + i * byte_width_;
-  return Reference(elem, byte_width_, 1, type_);
-}
-
-template<typename T> int KeyCompare(const void *key, const void *elem) {
-  auto str_elem = reinterpret_cast<const char *>(
-      Indirect<T>(reinterpret_cast<const uint8_t *>(elem)));
-  auto skey = reinterpret_cast<const char *>(key);
-  return strcmp(skey, str_elem);
-}
-
-inline Reference Map::operator[](const char *key) const {
-  auto keys = Keys();
-  // We can't pass keys.byte_width_ to the comparison function, so we have
-  // to pick the right one ahead of time.
-  int (*comp)(const void *, const void *) = nullptr;
-  switch (keys.byte_width_) {
-    case 1: comp = KeyCompare<uint8_t>; break;
-    case 2: comp = KeyCompare<uint16_t>; break;
-    case 4: comp = KeyCompare<uint32_t>; break;
-    case 8: comp = KeyCompare<uint64_t>; break;
-    default: FLATBUFFERS_ASSERT(false); return Reference();
-  }
-  auto res = std::bsearch(key, keys.data_, keys.size(), keys.byte_width_, comp);
-  if (!res) return Reference(nullptr, 1, NullPackedType());
-  auto i = (reinterpret_cast<uint8_t *>(res) - keys.data_) / keys.byte_width_;
-  return (*static_cast<const Vector *>(this))[i];
-}
-
-inline Reference Map::operator[](const std::string &key) const {
-  return (*this)[key.c_str()];
-}
-
-inline Reference GetRoot(const uint8_t *buffer, size_t size) {
-  // See Finish() below for the serialization counterpart of this.
-  // The root starts at the end of the buffer, so we parse backwards from there.
-  auto end = buffer + size;
-  auto byte_width = *--end;
-  auto packed_type = *--end;
-  end -= byte_width;  // The root data item.
-  return Reference(end, byte_width, packed_type);
-}
-
-inline Reference GetRoot(const std::vector<uint8_t> &buffer) {
-  return GetRoot(buffer.data(), buffer.size());
-}
-
-// Flags that configure how the Builder behaves.
-// The "Share" flags determine if the Builder automatically tries to pool
-// this type. Pooling can reduce the size of serialized data if there are
-// multiple maps of the same kind, at the expense of slightly slower
-// serialization (the cost of lookups) and more memory use (std::set).
-// By default this is on for keys, but off for strings.
-// Turn keys off if you have e.g. only one map.
-// Turn strings on if you expect many non-unique string values.
-// Additionally, sharing key vectors can save space if you have maps with
-// identical field populations.
-enum BuilderFlag {
-  BUILDER_FLAG_NONE = 0,
-  BUILDER_FLAG_SHARE_KEYS = 1,
-  BUILDER_FLAG_SHARE_STRINGS = 2,
-  BUILDER_FLAG_SHARE_KEYS_AND_STRINGS = 3,
-  BUILDER_FLAG_SHARE_KEY_VECTORS = 4,
-  BUILDER_FLAG_SHARE_ALL = 7,
-};
-
-class Builder FLATBUFFERS_FINAL_CLASS {
- public:
-  Builder(size_t initial_size = 256,
-          BuilderFlag flags = BUILDER_FLAG_SHARE_KEYS)
-      : buf_(initial_size),
-        finished_(false),
-        has_duplicate_keys_(false),
-        flags_(flags),
-        force_min_bit_width_(BIT_WIDTH_8),
-        key_pool(KeyOffsetCompare(buf_)),
-        string_pool(StringOffsetCompare(buf_)) {
-    buf_.clear();
-  }
-
-#ifdef FLATBUFFERS_DEFAULT_DECLARATION
-  Builder(Builder &&) = default;
-  Builder &operator=(Builder &&) = default;
-#endif
-
-  /// @brief Get the serialized buffer (after you call `Finish()`).
-  /// @return Returns a vector owned by this class.
-  const std::vector<uint8_t> &GetBuffer() const {
-    Finished();
-    return buf_;
-  }
-
-  // Size of the buffer. Does not include unfinished values.
-  size_t GetSize() const { return buf_.size(); }
-
-  // Reset all state so we can re-use the buffer.
-  void Clear() {
-    buf_.clear();
-    stack_.clear();
-    finished_ = false;
-    // flags_ remains as-is;
-    force_min_bit_width_ = BIT_WIDTH_8;
-    key_pool.clear();
-    string_pool.clear();
-  }
-
-  // All value constructing functions below have two versions: one that
-  // takes a key (for placement inside a map) and one that doesn't (for inside
-  // vectors and elsewhere).
-
-  void Null() { stack_.push_back(Value()); }
-  void Null(const char *key) {
-    Key(key);
-    Null();
-  }
-
-  void Int(int64_t i) { stack_.push_back(Value(i, FBT_INT, WidthI(i))); }
-  void Int(const char *key, int64_t i) {
-    Key(key);
-    Int(i);
-  }
-
-  void UInt(uint64_t u) { stack_.push_back(Value(u, FBT_UINT, WidthU(u))); }
-  void UInt(const char *key, uint64_t u) {
-    Key(key);
-    UInt(u);
-  }
-
-  void Float(float f) { stack_.push_back(Value(f)); }
-  void Float(const char *key, float f) {
-    Key(key);
-    Float(f);
-  }
-
-  void Double(double f) { stack_.push_back(Value(f)); }
-  void Double(const char *key, double d) {
-    Key(key);
-    Double(d);
-  }
-
-  void Bool(bool b) { stack_.push_back(Value(b)); }
-  void Bool(const char *key, bool b) {
-    Key(key);
-    Bool(b);
-  }
-
-  void IndirectInt(int64_t i) { PushIndirect(i, FBT_INDIRECT_INT, WidthI(i)); }
-  void IndirectInt(const char *key, int64_t i) {
-    Key(key);
-    IndirectInt(i);
-  }
-
-  void IndirectUInt(uint64_t u) {
-    PushIndirect(u, FBT_INDIRECT_UINT, WidthU(u));
-  }
-  void IndirectUInt(const char *key, uint64_t u) {
-    Key(key);
-    IndirectUInt(u);
-  }
-
-  void IndirectFloat(float f) {
-    PushIndirect(f, FBT_INDIRECT_FLOAT, BIT_WIDTH_32);
-  }
-  void IndirectFloat(const char *key, float f) {
-    Key(key);
-    IndirectFloat(f);
-  }
-
-  void IndirectDouble(double f) {
-    PushIndirect(f, FBT_INDIRECT_FLOAT, WidthF(f));
-  }
-  void IndirectDouble(const char *key, double d) {
-    Key(key);
-    IndirectDouble(d);
-  }
-
-  size_t Key(const char *str, size_t len) {
-    auto sloc = buf_.size();
-    WriteBytes(str, len + 1);
-    if (flags_ & BUILDER_FLAG_SHARE_KEYS) {
-      auto it = key_pool.find(sloc);
-      if (it != key_pool.end()) {
-        // Already in the buffer. Remove key we just serialized, and use
-        // existing offset instead.
-        buf_.resize(sloc);
-        sloc = *it;
-      } else {
-        key_pool.insert(sloc);
-      }
-    }
-    stack_.push_back(Value(static_cast<uint64_t>(sloc), FBT_KEY, BIT_WIDTH_8));
-    return sloc;
-  }
-
-  size_t Key(const char *str) { return Key(str, strlen(str)); }
-  size_t Key(const std::string &str) { return Key(str.c_str(), str.size()); }
-
-  size_t String(const char *str, size_t len) {
-    auto reset_to = buf_.size();
-    auto sloc = CreateBlob(str, len, 1, FBT_STRING);
-    if (flags_ & BUILDER_FLAG_SHARE_STRINGS) {
-      StringOffset so(sloc, len);
-      auto it = string_pool.find(so);
-      if (it != string_pool.end()) {
-        // Already in the buffer. Remove string we just serialized, and use
-        // existing offset instead.
-        buf_.resize(reset_to);
-        sloc = it->first;
-        stack_.back().u_ = sloc;
-      } else {
-        string_pool.insert(so);
-      }
-    }
-    return sloc;
-  }
-  size_t String(const char *str) { return String(str, strlen(str)); }
-  size_t String(const std::string &str) {
-    return String(str.c_str(), str.size());
-  }
-  void String(const flexbuffers::String &str) {
-    String(str.c_str(), str.length());
-  }
-
-  void String(const char *key, const char *str) {
-    Key(key);
-    String(str);
-  }
-  void String(const char *key, const std::string &str) {
-    Key(key);
-    String(str);
-  }
-  void String(const char *key, const flexbuffers::String &str) {
-    Key(key);
-    String(str);
-  }
-
-  size_t Blob(const void *data, size_t len) {
-    return CreateBlob(data, len, 0, FBT_BLOB);
-  }
-  size_t Blob(const std::vector<uint8_t> &v) {
-    return CreateBlob(v.data(), v.size(), 0, FBT_BLOB);
-  }
-
-  void Blob(const char *key, const void *data, size_t len) {
-    Key(key);
-    Blob(data, len);
-  }
-  void Blob(const char *key, const std::vector<uint8_t> &v) {
-    Key(key);
-    Blob(v);
-  }
-
-  // TODO(wvo): support all the FlexBuffer types (like flexbuffers::String),
-  // e.g. Vector etc. Also in overloaded versions.
-  // Also some FlatBuffers types?
-
-  size_t StartVector() { return stack_.size(); }
-  size_t StartVector(const char *key) {
-    Key(key);
-    return stack_.size();
-  }
-  size_t StartMap() { return stack_.size(); }
-  size_t StartMap(const char *key) {
-    Key(key);
-    return stack_.size();
-  }
-
-  // TODO(wvo): allow this to specify an alignment greater than the natural
-  // alignment.
-  size_t EndVector(size_t start, bool typed, bool fixed) {
-    auto vec = CreateVector(start, stack_.size() - start, 1, typed, fixed);
-    // Remove temp elements and return vector.
-    stack_.resize(start);
-    stack_.push_back(vec);
-    return static_cast<size_t>(vec.u_);
-  }
-
-  size_t EndMap(size_t start) {
-    // We should have interleaved keys and values on the stack.
-    // Make sure it is an even number:
-    auto len = stack_.size() - start;
-    FLATBUFFERS_ASSERT(!(len & 1));
-    len /= 2;
-    // Make sure keys are all strings:
-    for (auto key = start; key < stack_.size(); key += 2) {
-      FLATBUFFERS_ASSERT(stack_[key].type_ == FBT_KEY);
-    }
-    // Now sort values, so later we can do a binary search lookup.
-    // We want to sort 2 array elements at a time.
-    struct TwoValue {
-      Value key;
-      Value val;
-    };
-    // TODO(wvo): strict aliasing?
-    // TODO(wvo): allow the caller to indicate the data is already sorted
-    // for maximum efficiency? With an assert to check sortedness to make sure
-    // we're not breaking binary search.
-    // Or, we can track if the map is sorted as keys are added which would be
-    // be quite cheap (cheaper than checking it here), so we can skip this
-    // step automatically when appliccable, and encourage people to write in
-    // sorted fashion.
-    // std::sort is typically already a lot faster on sorted data though.
-    auto dict = reinterpret_cast<TwoValue *>(stack_.data() + start);
-    std::sort(
-        dict, dict + len, [&](const TwoValue &a, const TwoValue &b) -> bool {
-          auto as = reinterpret_cast<const char *>(buf_.data() + a.key.u_);
-          auto bs = reinterpret_cast<const char *>(buf_.data() + b.key.u_);
-          auto comp = strcmp(as, bs);
-          // We want to disallow duplicate keys, since this results in a
-          // map where values cannot be found.
-          // But we can't assert here (since we don't want to fail on
-          // random JSON input) or have an error mechanism.
-          // Instead, we set has_duplicate_keys_ in the builder to
-          // signal this.
-          // TODO: Have to check for pointer equality, as some sort
-          // implementation apparently call this function with the same
-          // element?? Why?
-          if (!comp && &a != &b) has_duplicate_keys_ = true;
-          return comp < 0;
-        });
-    // First create a vector out of all keys.
-    // TODO(wvo): if kBuilderFlagShareKeyVectors is true, see if we can share
-    // the first vector.
-    auto keys = CreateVector(start, len, 2, true, false);
-    auto vec = CreateVector(start + 1, len, 2, false, false, &keys);
-    // Remove temp elements and return map.
-    stack_.resize(start);
-    stack_.push_back(vec);
-    return static_cast<size_t>(vec.u_);
-  }
-
-  // Call this after EndMap to see if the map had any duplicate keys.
-  // Any map with such keys won't be able to retrieve all values.
-  bool HasDuplicateKeys() const { return has_duplicate_keys_; }
-
-  template<typename F> size_t Vector(F f) {
-    auto start = StartVector();
-    f();
-    return EndVector(start, false, false);
-  }
-  template<typename F, typename T> size_t Vector(F f, T &state) {
-    auto start = StartVector();
-    f(state);
-    return EndVector(start, false, false);
-  }
-  template<typename F> size_t Vector(const char *key, F f) {
-    auto start = StartVector(key);
-    f();
-    return EndVector(start, false, false);
-  }
-  template<typename F, typename T>
-  size_t Vector(const char *key, F f, T &state) {
-    auto start = StartVector(key);
-    f(state);
-    return EndVector(start, false, false);
-  }
-
-  template<typename T> void Vector(const T *elems, size_t len) {
-    if (flatbuffers::is_scalar<T>::value) {
-      // This path should be a lot quicker and use less space.
-      ScalarVector(elems, len, false);
-    } else {
-      auto start = StartVector();
-      for (size_t i = 0; i < len; i++) Add(elems[i]);
-      EndVector(start, false, false);
-    }
-  }
-  template<typename T>
-  void Vector(const char *key, const T *elems, size_t len) {
-    Key(key);
-    Vector(elems, len);
-  }
-  template<typename T> void Vector(const std::vector<T> &vec) {
-    Vector(vec.data(), vec.size());
-  }
-
-  template<typename F> size_t TypedVector(F f) {
-    auto start = StartVector();
-    f();
-    return EndVector(start, true, false);
-  }
-  template<typename F, typename T> size_t TypedVector(F f, T &state) {
-    auto start = StartVector();
-    f(state);
-    return EndVector(start, true, false);
-  }
-  template<typename F> size_t TypedVector(const char *key, F f) {
-    auto start = StartVector(key);
-    f();
-    return EndVector(start, true, false);
-  }
-  template<typename F, typename T>
-  size_t TypedVector(const char *key, F f, T &state) {
-    auto start = StartVector(key);
-    f(state);
-    return EndVector(start, true, false);
-  }
-
-  template<typename T> size_t FixedTypedVector(const T *elems, size_t len) {
-    // We only support a few fixed vector lengths. Anything bigger use a
-    // regular typed vector.
-    FLATBUFFERS_ASSERT(len >= 2 && len <= 4);
-    // And only scalar values.
-    static_assert(flatbuffers::is_scalar<T>::value, "Unrelated types");
-    return ScalarVector(elems, len, true);
-  }
-
-  template<typename T>
-  size_t FixedTypedVector(const char *key, const T *elems, size_t len) {
-    Key(key);
-    return FixedTypedVector(elems, len);
-  }
-
-  template<typename F> size_t Map(F f) {
-    auto start = StartMap();
-    f();
-    return EndMap(start);
-  }
-  template<typename F, typename T> size_t Map(F f, T &state) {
-    auto start = StartMap();
-    f(state);
-    return EndMap(start);
-  }
-  template<typename F> size_t Map(const char *key, F f) {
-    auto start = StartMap(key);
-    f();
-    return EndMap(start);
-  }
-  template<typename F, typename T> size_t Map(const char *key, F f, T &state) {
-    auto start = StartMap(key);
-    f(state);
-    return EndMap(start);
-  }
-  template<typename T> void Map(const std::map<std::string, T> &map) {
-    auto start = StartMap();
-    for (auto it = map.begin(); it != map.end(); ++it)
-      Add(it->first.c_str(), it->second);
-    EndMap(start);
-  }
-
-  // If you wish to share a value explicitly (a value not shared automatically
-  // through one of the BUILDER_FLAG_SHARE_* flags) you can do so with these
-  // functions. Or if you wish to turn those flags off for performance reasons
-  // and still do some explicit sharing. For example:
-  // builder.IndirectDouble(M_PI);
-  // auto id = builder.LastValue();  // Remember where we stored it.
-  // .. more code goes here ..
-  // builder.ReuseValue(id);  // Refers to same double by offset.
-  // LastValue works regardless of whether the value has a key or not.
-  // Works on any data type.
-  struct Value;
-  Value LastValue() { return stack_.back(); }
-  void ReuseValue(Value v) { stack_.push_back(v); }
-  void ReuseValue(const char *key, Value v) {
-    Key(key);
-    ReuseValue(v);
-  }
-
-  // Overloaded Add that tries to call the correct function above.
-  void Add(int8_t i) { Int(i); }
-  void Add(int16_t i) { Int(i); }
-  void Add(int32_t i) { Int(i); }
-  void Add(int64_t i) { Int(i); }
-  void Add(uint8_t u) { UInt(u); }
-  void Add(uint16_t u) { UInt(u); }
-  void Add(uint32_t u) { UInt(u); }
-  void Add(uint64_t u) { UInt(u); }
-  void Add(float f) { Float(f); }
-  void Add(double d) { Double(d); }
-  void Add(bool b) { Bool(b); }
-  void Add(const char *str) { String(str); }
-  void Add(const std::string &str) { String(str); }
-  void Add(const flexbuffers::String &str) { String(str); }
-
-  template<typename T> void Add(const std::vector<T> &vec) { Vector(vec); }
-
-  template<typename T> void Add(const char *key, const T &t) {
-    Key(key);
-    Add(t);
-  }
-
-  template<typename T> void Add(const std::map<std::string, T> &map) {
-    Map(map);
-  }
-
-  template<typename T> void operator+=(const T &t) { Add(t); }
-
-  // This function is useful in combination with the Mutate* functions above.
-  // It forces elements of vectors and maps to have a minimum size, such that
-  // they can later be updated without failing.
-  // Call with no arguments to reset.
-  void ForceMinimumBitWidth(BitWidth bw = BIT_WIDTH_8) {
-    force_min_bit_width_ = bw;
-  }
-
-  void Finish() {
-    // If you hit this assert, you likely have objects that were never included
-    // in a parent. You need to have exactly one root to finish a buffer.
-    // Check your Start/End calls are matched, and all objects are inside
-    // some other object.
-    FLATBUFFERS_ASSERT(stack_.size() == 1);
-
-    // Write root value.
-    auto byte_width = Align(stack_[0].ElemWidth(buf_.size(), 0));
-    WriteAny(stack_[0], byte_width);
-    // Write root type.
-    Write(stack_[0].StoredPackedType(), 1);
-    // Write root size. Normally determined by parent, but root has no parent :)
-    Write(byte_width, 1);
-
-    finished_ = true;
-  }
-
- private:
-  void Finished() const {
-    // If you get this assert, you're attempting to get access a buffer
-    // which hasn't been finished yet. Be sure to call
-    // Builder::Finish with your root object.
-    FLATBUFFERS_ASSERT(finished_);
-  }
-
-  // Align to prepare for writing a scalar with a certain size.
-  uint8_t Align(BitWidth alignment) {
-    auto byte_width = 1U << alignment;
-    buf_.insert(buf_.end(), flatbuffers::PaddingBytes(buf_.size(), byte_width),
-                0);
-    return static_cast<uint8_t>(byte_width);
-  }
-
-  void WriteBytes(const void *val, size_t size) {
-    buf_.insert(buf_.end(), reinterpret_cast<const uint8_t *>(val),
-                reinterpret_cast<const uint8_t *>(val) + size);
-  }
-
-  template<typename T> void Write(T val, size_t byte_width) {
-    FLATBUFFERS_ASSERT(sizeof(T) >= byte_width);
-    val = flatbuffers::EndianScalar(val);
-    WriteBytes(&val, byte_width);
-  }
-
-  void WriteDouble(double f, uint8_t byte_width) {
-    switch (byte_width) {
-      case 8: Write(f, byte_width); break;
-      case 4: Write(static_cast<float>(f), byte_width); break;
-      // case 2: Write(static_cast<half>(f), byte_width); break;
-      // case 1: Write(static_cast<quarter>(f), byte_width); break;
-      default: FLATBUFFERS_ASSERT(0);
-    }
-  }
-
-  void WriteOffset(uint64_t o, uint8_t byte_width) {
-    auto reloff = buf_.size() - o;
-    FLATBUFFERS_ASSERT(byte_width == 8 || reloff < 1ULL << (byte_width * 8));
-    Write(reloff, byte_width);
-  }
-
-  template<typename T> void PushIndirect(T val, Type type, BitWidth bit_width) {
-    auto byte_width = Align(bit_width);
-    auto iloc = buf_.size();
-    Write(val, byte_width);
-    stack_.push_back(Value(static_cast<uint64_t>(iloc), type, bit_width));
-  }
-
-  static BitWidth WidthB(size_t byte_width) {
-    switch (byte_width) {
-      case 1: return BIT_WIDTH_8;
-      case 2: return BIT_WIDTH_16;
-      case 4: return BIT_WIDTH_32;
-      case 8: return BIT_WIDTH_64;
-      default: FLATBUFFERS_ASSERT(false); return BIT_WIDTH_64;
-    }
-  }
-
-  template<typename T> static Type GetScalarType() {
-    static_assert(flatbuffers::is_scalar<T>::value, "Unrelated types");
-    return flatbuffers::is_floating_point<T>::value
-               ? FBT_FLOAT
-               : flatbuffers::is_same<T, bool>::value
-                     ? FBT_BOOL
-                     : (flatbuffers::is_unsigned<T>::value ? FBT_UINT
-                                                           : FBT_INT);
-  }
-
- public:
-  // This was really intended to be private, except for LastValue/ReuseValue.
-  struct Value {
-    union {
-      int64_t i_;
-      uint64_t u_;
-      double f_;
-    };
-
-    Type type_;
-
-    // For scalars: of itself, for vector: of its elements, for string: length.
-    BitWidth min_bit_width_;
-
-    Value() : i_(0), type_(FBT_NULL), min_bit_width_(BIT_WIDTH_8) {}
-
-    Value(bool b)
-        : u_(static_cast<uint64_t>(b)),
-          type_(FBT_BOOL),
-          min_bit_width_(BIT_WIDTH_8) {}
-
-    Value(int64_t i, Type t, BitWidth bw)
-        : i_(i), type_(t), min_bit_width_(bw) {}
-    Value(uint64_t u, Type t, BitWidth bw)
-        : u_(u), type_(t), min_bit_width_(bw) {}
-
-    Value(float f)
-        : f_(static_cast<double>(f)),
-          type_(FBT_FLOAT),
-          min_bit_width_(BIT_WIDTH_32) {}
-    Value(double f) : f_(f), type_(FBT_FLOAT), min_bit_width_(WidthF(f)) {}
-
-    uint8_t StoredPackedType(BitWidth parent_bit_width_ = BIT_WIDTH_8) const {
-      return PackedType(StoredWidth(parent_bit_width_), type_);
-    }
-
-    BitWidth ElemWidth(size_t buf_size, size_t elem_index) const {
-      if (IsInline(type_)) {
-        return min_bit_width_;
-      } else {
-        // We have an absolute offset, but want to store a relative offset
-        // elem_index elements beyond the current buffer end. Since whether
-        // the relative offset fits in a certain byte_width depends on
-        // the size of the elements before it (and their alignment), we have
-        // to test for each size in turn.
-        for (size_t byte_width = 1;
-             byte_width <= sizeof(flatbuffers::largest_scalar_t);
-             byte_width *= 2) {
-          // Where are we going to write this offset?
-          auto offset_loc = buf_size +
-                            flatbuffers::PaddingBytes(buf_size, byte_width) +
-                            elem_index * byte_width;
-          // Compute relative offset.
-          auto offset = offset_loc - u_;
-          // Does it fit?
-          auto bit_width = WidthU(offset);
-          if (static_cast<size_t>(static_cast<size_t>(1U) << bit_width) ==
-              byte_width)
-            return bit_width;
-        }
-        FLATBUFFERS_ASSERT(false);  // Must match one of the sizes above.
-        return BIT_WIDTH_64;
-      }
-    }
-
-    BitWidth StoredWidth(BitWidth parent_bit_width_ = BIT_WIDTH_8) const {
-      if (IsInline(type_)) {
-        return (std::max)(min_bit_width_, parent_bit_width_);
-      } else {
-        return min_bit_width_;
-      }
-    }
-  };
-
- private:
-  void WriteAny(const Value &val, uint8_t byte_width) {
-    switch (val.type_) {
-      case FBT_NULL:
-      case FBT_INT: Write(val.i_, byte_width); break;
-      case FBT_BOOL:
-      case FBT_UINT: Write(val.u_, byte_width); break;
-      case FBT_FLOAT: WriteDouble(val.f_, byte_width); break;
-      default: WriteOffset(val.u_, byte_width); break;
-    }
-  }
-
-  size_t CreateBlob(const void *data, size_t len, size_t trailing, Type type) {
-    auto bit_width = WidthU(len);
-    auto byte_width = Align(bit_width);
-    Write<uint64_t>(len, byte_width);
-    auto sloc = buf_.size();
-    WriteBytes(data, len + trailing);
-    stack_.push_back(Value(static_cast<uint64_t>(sloc), type, bit_width));
-    return sloc;
-  }
-
-  template<typename T>
-  size_t ScalarVector(const T *elems, size_t len, bool fixed) {
-    auto vector_type = GetScalarType<T>();
-    auto byte_width = sizeof(T);
-    auto bit_width = WidthB(byte_width);
-    // If you get this assert, you're trying to write a vector with a size
-    // field that is bigger than the scalars you're trying to write (e.g. a
-    // byte vector > 255 elements). For such types, write a "blob" instead.
-    // TODO: instead of asserting, could write vector with larger elements
-    // instead, though that would be wasteful.
-    FLATBUFFERS_ASSERT(WidthU(len) <= bit_width);
-    Align(bit_width);
-    if (!fixed) Write<uint64_t>(len, byte_width);
-    auto vloc = buf_.size();
-    for (size_t i = 0; i < len; i++) Write(elems[i], byte_width);
-    stack_.push_back(Value(static_cast<uint64_t>(vloc),
-                           ToTypedVector(vector_type, fixed ? len : 0),
-                           bit_width));
-    return vloc;
-  }
-
-  Value CreateVector(size_t start, size_t vec_len, size_t step, bool typed,
-                     bool fixed, const Value *keys = nullptr) {
-    FLATBUFFERS_ASSERT(
-        !fixed ||
-        typed);  // typed=false, fixed=true combination is not supported.
-    // Figure out smallest bit width we can store this vector with.
-    auto bit_width = (std::max)(force_min_bit_width_, WidthU(vec_len));
-    auto prefix_elems = 1;
-    if (keys) {
-      // If this vector is part of a map, we will pre-fix an offset to the keys
-      // to this vector.
-      bit_width = (std::max)(bit_width, keys->ElemWidth(buf_.size(), 0));
-      prefix_elems += 2;
-    }
-    Type vector_type = FBT_KEY;
-    // Check bit widths and types for all elements.
-    for (size_t i = start; i < stack_.size(); i += step) {
-      auto elem_width =
-          stack_[i].ElemWidth(buf_.size(), i - start + prefix_elems);
-      bit_width = (std::max)(bit_width, elem_width);
-      if (typed) {
-        if (i == start) {
-          vector_type = stack_[i].type_;
-        } else {
-          // If you get this assert, you are writing a typed vector with
-          // elements that are not all the same type.
-          FLATBUFFERS_ASSERT(vector_type == stack_[i].type_);
-        }
-      }
-    }
-    // If you get this assert, your typed types are not one of:
-    // Int / UInt / Float / Key.
-    FLATBUFFERS_ASSERT(!typed || IsTypedVectorElementType(vector_type));
-    auto byte_width = Align(bit_width);
-    // Write vector. First the keys width/offset if available, and size.
-    if (keys) {
-      WriteOffset(keys->u_, byte_width);
-      Write<uint64_t>(1ULL << keys->min_bit_width_, byte_width);
-    }
-    if (!fixed) Write<uint64_t>(vec_len, byte_width);
-    // Then the actual data.
-    auto vloc = buf_.size();
-    for (size_t i = start; i < stack_.size(); i += step) {
-      WriteAny(stack_[i], byte_width);
-    }
-    // Then the types.
-    if (!typed) {
-      for (size_t i = start; i < stack_.size(); i += step) {
-        buf_.push_back(stack_[i].StoredPackedType(bit_width));
-      }
-    }
-    return Value(static_cast<uint64_t>(vloc),
-                 keys ? FBT_MAP
-                      : (typed ? ToTypedVector(vector_type, fixed ? vec_len : 0)
-                               : FBT_VECTOR),
-                 bit_width);
-  }
-
-  // You shouldn't really be copying instances of this class.
-  Builder(const Builder &);
-  Builder &operator=(const Builder &);
-
-  std::vector<uint8_t> buf_;
-  std::vector<Value> stack_;
-
-  bool finished_;
-  bool has_duplicate_keys_;
-
-  BuilderFlag flags_;
-
-  BitWidth force_min_bit_width_;
-
-  struct KeyOffsetCompare {
-    explicit KeyOffsetCompare(const std::vector<uint8_t> &buf) : buf_(&buf) {}
-    bool operator()(size_t a, size_t b) const {
-      auto stra = reinterpret_cast<const char *>(buf_->data() + a);
-      auto strb = reinterpret_cast<const char *>(buf_->data() + b);
-      return strcmp(stra, strb) < 0;
-    }
-    const std::vector<uint8_t> *buf_;
-  };
-
-  typedef std::pair<size_t, size_t> StringOffset;
-  struct StringOffsetCompare {
-    explicit StringOffsetCompare(const std::vector<uint8_t> &buf)
-        : buf_(&buf) {}
-    bool operator()(const StringOffset &a, const StringOffset &b) const {
-      auto stra = buf_->data() + a.first;
-      auto strb = buf_->data() + b.first;
-      auto cr = memcmp(stra, strb, (std::min)(a.second, b.second) + 1);
-      return cr < 0 || (cr == 0 && a.second < b.second);
-    }
-    const std::vector<uint8_t> *buf_;
-  };
-
-  typedef std::set<size_t, KeyOffsetCompare> KeyOffsetMap;
-  typedef std::set<StringOffset, StringOffsetCompare> StringOffsetMap;
-
-  KeyOffsetMap key_pool;
-  StringOffsetMap string_pool;
-
-  friend class Verifier;
-};
-
-// Helper class to verify the integrity of a FlexBuffer
-class Verifier FLATBUFFERS_FINAL_CLASS {
- public:
-  Verifier(const uint8_t *buf, size_t buf_len,
-           // Supplying this vector likely results in faster verification
-           // of larger buffers with many shared keys/strings, but
-           // comes at the cost of using additional memory the same size of
-           // the buffer being verified, so it is by default off.
-           std::vector<uint8_t> *reuse_tracker = nullptr,
-           bool _check_alignment = true, size_t max_depth = 64)
-      : buf_(buf),
-        size_(buf_len),
-        depth_(0),
-        max_depth_(max_depth),
-        num_vectors_(0),
-        max_vectors_(buf_len),
-        check_alignment_(_check_alignment),
-        reuse_tracker_(reuse_tracker) {
-    FLATBUFFERS_ASSERT(size_ < FLATBUFFERS_MAX_BUFFER_SIZE);
-    if (reuse_tracker_) {
-      reuse_tracker_->clear();
-      reuse_tracker_->resize(size_, PackedType(BIT_WIDTH_8, FBT_NULL));
-    }
-  }
-
- private:
-  // Central location where any verification failures register.
-  bool Check(bool ok) const {
-    // clang-format off
-    #ifdef FLATBUFFERS_DEBUG_VERIFICATION_FAILURE
-      FLATBUFFERS_ASSERT(ok);
-    #endif
-    // clang-format on
-    return ok;
-  }
-
-  // Verify any range within the buffer.
-  bool VerifyFrom(size_t elem, size_t elem_len) const {
-    return Check(elem_len < size_ && elem <= size_ - elem_len);
-  }
-  bool VerifyBefore(size_t elem, size_t elem_len) const {
-    return Check(elem_len <= elem);
-  }
-
-  bool VerifyFromPointer(const uint8_t *p, size_t len) {
-    auto o = static_cast<size_t>(p - buf_);
-    return VerifyFrom(o, len);
-  }
-  bool VerifyBeforePointer(const uint8_t *p, size_t len) {
-    auto o = static_cast<size_t>(p - buf_);
-    return VerifyBefore(o, len);
-  }
-
-  bool VerifyByteWidth(size_t width) {
-    return Check(width == 1 || width == 2 || width == 4 || width == 8);
-  }
-
-  bool VerifyType(int type) { return Check(type >= 0 && type < FBT_MAX_TYPE); }
-
-  bool VerifyOffset(uint64_t off, const uint8_t *p) {
-    return Check(off <= static_cast<uint64_t>(size_)) &&
-           off <= static_cast<uint64_t>(p - buf_);
-  }
-
-  bool VerifyAlignment(const uint8_t *p, size_t size) const {
-    auto o = static_cast<size_t>(p - buf_);
-    return Check((o & (size - 1)) == 0 || !check_alignment_);
-  }
-
-// Macro, since we want to escape from parent function & use lazy args.
-#define FLEX_CHECK_VERIFIED(P, PACKED_TYPE)                     \
-  if (reuse_tracker_) {                                         \
-    auto packed_type = PACKED_TYPE;                             \
-    auto existing = (*reuse_tracker_)[P - buf_];                \
-    if (existing == packed_type) return true;                   \
-    /* Fail verification if already set with different type! */ \
-    if (!Check(existing == 0)) return false;                    \
-    (*reuse_tracker_)[P - buf_] = packed_type;                  \
-  }
-
-  bool VerifyVector(Reference r, const uint8_t *p, Type elem_type) {
-    // Any kind of nesting goes thru this function, so guard against that
-    // here, both with simple nesting checks, and the reuse tracker if on.
-    depth_++;
-    num_vectors_++;
-    if (!Check(depth_ <= max_depth_ && num_vectors_ <= max_vectors_))
-      return false;
-    auto size_byte_width = r.byte_width_;
-    if (!VerifyBeforePointer(p, size_byte_width)) return false;
-    FLEX_CHECK_VERIFIED(p - size_byte_width,
-                        PackedType(Builder::WidthB(size_byte_width), r.type_));
-    auto sized = Sized(p, size_byte_width);
-    auto num_elems = sized.size();
-    auto elem_byte_width = r.type_ == FBT_STRING || r.type_ == FBT_BLOB
-                               ? uint8_t(1)
-                               : r.byte_width_;
-    auto max_elems = SIZE_MAX / elem_byte_width;
-    if (!Check(num_elems < max_elems))
-      return false;  // Protect against byte_size overflowing.
-    auto byte_size = num_elems * elem_byte_width;
-    if (!VerifyFromPointer(p, byte_size)) return false;
-    if (elem_type == FBT_NULL) {
-      // Verify type bytes after the vector.
-      if (!VerifyFromPointer(p + byte_size, num_elems)) return false;
-      auto v = Vector(p, size_byte_width);
-      for (size_t i = 0; i < num_elems; i++)
-        if (!VerifyRef(v[i])) return false;
-    } else if (elem_type == FBT_KEY) {
-      auto v = TypedVector(p, elem_byte_width, FBT_KEY);
-      for (size_t i = 0; i < num_elems; i++)
-        if (!VerifyRef(v[i])) return false;
-    } else {
-      FLATBUFFERS_ASSERT(IsInline(elem_type));
-    }
-    depth_--;
-    return true;
-  }
-
-  bool VerifyKeys(const uint8_t *p, uint8_t byte_width) {
-    // The vector part of the map has already been verified.
-    const size_t num_prefixed_fields = 3;
-    if (!VerifyBeforePointer(p, byte_width * num_prefixed_fields)) return false;
-    p -= byte_width * num_prefixed_fields;
-    auto off = ReadUInt64(p, byte_width);
-    if (!VerifyOffset(off, p)) return false;
-    auto key_byte_with =
-        static_cast<uint8_t>(ReadUInt64(p + byte_width, byte_width));
-    if (!VerifyByteWidth(key_byte_with)) return false;
-    return VerifyVector(Reference(p, byte_width, key_byte_with, FBT_VECTOR_KEY),
-                        p - off, FBT_KEY);
-  }
-
-  bool VerifyKey(const uint8_t *p) {
-    FLEX_CHECK_VERIFIED(p, PackedType(BIT_WIDTH_8, FBT_KEY));
-    while (p < buf_ + size_)
-      if (*p++) return true;
-    return false;
-  }
-
-#undef FLEX_CHECK_VERIFIED
-
-  bool VerifyTerminator(const String &s) {
-    return VerifyFromPointer(reinterpret_cast<const uint8_t *>(s.c_str()),
-                             s.size() + 1);
-  }
-
-  bool VerifyRef(Reference r) {
-    // r.parent_width_ and r.data_ already verified.
-    if (!VerifyByteWidth(r.byte_width_) || !VerifyType(r.type_)) {
-      return false;
-    }
-    if (IsInline(r.type_)) {
-      // Inline scalars, don't require further verification.
-      return true;
-    }
-    // All remaining types are an offset.
-    auto off = ReadUInt64(r.data_, r.parent_width_);
-    if (!VerifyOffset(off, r.data_)) return false;
-    auto p = r.Indirect();
-    if (!VerifyAlignment(p, r.byte_width_)) return false;
-    switch (r.type_) {
-      case FBT_INDIRECT_INT:
-      case FBT_INDIRECT_UINT:
-      case FBT_INDIRECT_FLOAT: return VerifyFromPointer(p, r.byte_width_);
-      case FBT_KEY: return VerifyKey(p);
-      case FBT_MAP:
-        return VerifyVector(r, p, FBT_NULL) && VerifyKeys(p, r.byte_width_);
-      case FBT_VECTOR: return VerifyVector(r, p, FBT_NULL);
-      case FBT_VECTOR_INT: return VerifyVector(r, p, FBT_INT);
-      case FBT_VECTOR_BOOL:
-      case FBT_VECTOR_UINT: return VerifyVector(r, p, FBT_UINT);
-      case FBT_VECTOR_FLOAT: return VerifyVector(r, p, FBT_FLOAT);
-      case FBT_VECTOR_KEY: return VerifyVector(r, p, FBT_KEY);
-      case FBT_VECTOR_STRING_DEPRECATED:
-        // Use of FBT_KEY here intentional, see elsewhere.
-        return VerifyVector(r, p, FBT_KEY);
-      case FBT_BLOB: return VerifyVector(r, p, FBT_UINT);
-      case FBT_STRING:
-        return VerifyVector(r, p, FBT_UINT) &&
-               VerifyTerminator(String(p, r.byte_width_));
-      case FBT_VECTOR_INT2:
-      case FBT_VECTOR_UINT2:
-      case FBT_VECTOR_FLOAT2:
-      case FBT_VECTOR_INT3:
-      case FBT_VECTOR_UINT3:
-      case FBT_VECTOR_FLOAT3:
-      case FBT_VECTOR_INT4:
-      case FBT_VECTOR_UINT4:
-      case FBT_VECTOR_FLOAT4: {
-        uint8_t len = 0;
-        auto vtype = ToFixedTypedVectorElementType(r.type_, &len);
-        if (!VerifyType(vtype)) return false;
-        return VerifyFromPointer(p, r.byte_width_ * len);
-      }
-      default: return false;
-    }
-  }
-
- public:
-  bool VerifyBuffer() {
-    if (!Check(size_ >= 3)) return false;
-    auto end = buf_ + size_;
-    auto byte_width = *--end;
-    auto packed_type = *--end;
-    return VerifyByteWidth(byte_width) && Check(end - buf_ >= byte_width) &&
-           VerifyRef(Reference(end - byte_width, byte_width, packed_type));
-  }
-
- private:
-  const uint8_t *buf_;
-  size_t size_;
-  size_t depth_;
-  const size_t max_depth_;
-  size_t num_vectors_;
-  const size_t max_vectors_;
-  bool check_alignment_;
-  std::vector<uint8_t> *reuse_tracker_;
-};
-
-// Utility function that contructs the Verifier for you, see above for
-// parameters.
-inline bool VerifyBuffer(const uint8_t *buf, size_t buf_len,
-                         std::vector<uint8_t> *reuse_tracker = nullptr) {
-  Verifier verifier(buf, buf_len, reuse_tracker);
-  return verifier.VerifyBuffer();
-}
-
-}  // namespace flexbuffers
-
-#if defined(_MSC_VER)
-#  pragma warning(pop)
-#endif
-
-#endif  // FLATBUFFERS_FLEXBUFFERS_H_

code/components/tflite-lib/third_party/flatbuffers/include/flatbuffers/stl_emulation.h

@@ -1,509 +0,0 @@
-/*
- * Copyright 2017 Google Inc. All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef FLATBUFFERS_STL_EMULATION_H_
-#define FLATBUFFERS_STL_EMULATION_H_
-
-// clang-format off
-#include "flatbuffers/base.h"
-
-#include <string>
-#include <type_traits>
-#include <vector>
-#include <memory>
-#include <limits>
-
-#ifndef FLATBUFFERS_USE_STD_OPTIONAL
-  // Detect C++17 compatible compiler.
-  // __cplusplus >= 201703L - a compiler has support of 'static inline' variables.
-  #if (defined(__cplusplus) && __cplusplus >= 201703L) \
-      || (defined(_MSVC_LANG) && _MSVC_LANG >= 201703L)
-    #define FLATBUFFERS_USE_STD_OPTIONAL 1
-  #else
-    #define FLATBUFFERS_USE_STD_OPTIONAL 0
-  #endif // (defined(__cplusplus) && __cplusplus >= 201703L) ...
-#endif // FLATBUFFERS_USE_STD_OPTIONAL
-
-#if FLATBUFFERS_USE_STD_OPTIONAL
-  #include <optional>
-#endif
-
-// The __cpp_lib_span is the predefined feature macro.
-#if defined(FLATBUFFERS_USE_STD_SPAN)
-    #include <span>
-#elif defined(__cpp_lib_span) && defined(__has_include)
-  #if __has_include(<span>)
-    #include <span>
-    #define FLATBUFFERS_USE_STD_SPAN
-  #endif
-#else
-  // Disable non-trivial ctors if FLATBUFFERS_SPAN_MINIMAL defined.
-  #if !defined(FLATBUFFERS_TEMPLATES_ALIASES)
-    #define FLATBUFFERS_SPAN_MINIMAL
-  #else
-    // Enable implicit construction of a span<T,N> from a std::array<T,N>.
-    #include <array>
-  #endif
-#endif // defined(FLATBUFFERS_USE_STD_SPAN)
-
-// This header provides backwards compatibility for older versions of the STL.
-namespace flatbuffers {
-
-#if defined(FLATBUFFERS_TEMPLATES_ALIASES)
-  template <typename T>
-  using numeric_limits = std::numeric_limits<T>;
-#else
-  template <typename T> class numeric_limits :
-    public std::numeric_limits<T> {};
-#endif  // defined(FLATBUFFERS_TEMPLATES_ALIASES)
-
-#if defined(FLATBUFFERS_TEMPLATES_ALIASES)
-  template <typename T> using is_scalar = std::is_scalar<T>;
-  template <typename T, typename U> using is_same = std::is_same<T,U>;
-  template <typename T> using is_floating_point = std::is_floating_point<T>;
-  template <typename T> using is_unsigned = std::is_unsigned<T>;
-  template <typename T> using is_enum = std::is_enum<T>;
-  template <typename T> using make_unsigned = std::make_unsigned<T>;
-  template<bool B, class T, class F>
-  using conditional = std::conditional<B, T, F>;
-  template<class T, T v>
-  using integral_constant = std::integral_constant<T, v>;
-  template <bool B>
-  using bool_constant = integral_constant<bool, B>;
-  using true_type  = std::true_type;
-  using false_type = std::false_type;
-#else
-  // MSVC 2010 doesn't support C++11 aliases.
-  template <typename T> struct is_scalar : public std::is_scalar<T> {};
-  template <typename T, typename U> struct is_same : public std::is_same<T,U> {};
-  template <typename T> struct is_floating_point :
-        public std::is_floating_point<T> {};
-  template <typename T> struct is_unsigned : public std::is_unsigned<T> {};
-  template <typename T> struct is_enum : public std::is_enum<T> {};
-  template <typename T> struct make_unsigned : public std::make_unsigned<T> {};
-  template<bool B, class T, class F>
-  struct conditional : public std::conditional<B, T, F> {};
-  template<class T, T v>
-  struct integral_constant : public std::integral_constant<T, v> {};
-  template <bool B>
-  struct bool_constant : public integral_constant<bool, B> {};
-  typedef bool_constant<true>  true_type;
-  typedef bool_constant<false> false_type;
-#endif  // defined(FLATBUFFERS_TEMPLATES_ALIASES)
-
-#if defined(FLATBUFFERS_TEMPLATES_ALIASES)
-  template <class T> using unique_ptr = std::unique_ptr<T>;
-#else
-  // MSVC 2010 doesn't support C++11 aliases.
-  // We're manually "aliasing" the class here as we want to bring unique_ptr
-  // into the flatbuffers namespace.  We have unique_ptr in the flatbuffers
-  // namespace we have a completely independent implementation (see below)
-  // for C++98 STL implementations.
-  template <class T> class unique_ptr : public std::unique_ptr<T> {
-    public:
-    unique_ptr() {}
-    explicit unique_ptr(T* p) : std::unique_ptr<T>(p) {}
-    unique_ptr(std::unique_ptr<T>&& u) { *this = std::move(u); }
-    unique_ptr(unique_ptr&& u) { *this = std::move(u); }
-    unique_ptr& operator=(std::unique_ptr<T>&& u) {
-      std::unique_ptr<T>::reset(u.release());
-      return *this;
-    }
-    unique_ptr& operator=(unique_ptr&& u) {
-      std::unique_ptr<T>::reset(u.release());
-      return *this;
-    }
-    unique_ptr& operator=(T* p) {
-      return std::unique_ptr<T>::operator=(p);
-    }
-  };
-#endif  // defined(FLATBUFFERS_TEMPLATES_ALIASES)
-
-#if FLATBUFFERS_USE_STD_OPTIONAL
-template<class T>
-using Optional = std::optional<T>;
-using nullopt_t = std::nullopt_t;
-inline constexpr nullopt_t nullopt = std::nullopt;
-
-#else
-// Limited implementation of Optional<T> type for a scalar T.
-// This implementation limited by trivial types compatible with
-// std::is_arithmetic<T> or std::is_enum<T> type traits.
-
-// A tag to indicate an empty flatbuffers::optional<T>.
-struct nullopt_t {
-  explicit FLATBUFFERS_CONSTEXPR_CPP11 nullopt_t(int) {}
-};
-
-#if defined(FLATBUFFERS_CONSTEXPR_DEFINED)
-  namespace internal {
-    template <class> struct nullopt_holder {
-      static constexpr nullopt_t instance_ = nullopt_t(0);
-    };
-    template<class Dummy>
-    constexpr nullopt_t nullopt_holder<Dummy>::instance_;
-  }
-  static constexpr const nullopt_t &nullopt = internal::nullopt_holder<void>::instance_;
-
-#else
-  namespace internal {
-    template <class> struct nullopt_holder {
-      static const nullopt_t instance_;
-    };
-    template<class Dummy>
-    const nullopt_t nullopt_holder<Dummy>::instance_  = nullopt_t(0);
-  }
-  static const nullopt_t &nullopt = internal::nullopt_holder<void>::instance_;
-
-#endif
-
-template<class T>
-class Optional FLATBUFFERS_FINAL_CLASS {
-  // Non-scalar 'T' would extremely complicated Optional<T>.
-  // Use is_scalar<T> checking because flatbuffers flatbuffers::is_arithmetic<T>
-  // isn't implemented.
-  static_assert(flatbuffers::is_scalar<T>::value, "unexpected type T");
-
- public:
-  ~Optional() {}
-
-  FLATBUFFERS_CONSTEXPR_CPP11 Optional() FLATBUFFERS_NOEXCEPT
-    : value_(), has_value_(false) {}
-
-  FLATBUFFERS_CONSTEXPR_CPP11 Optional(nullopt_t) FLATBUFFERS_NOEXCEPT
-    : value_(), has_value_(false) {}
-
-  FLATBUFFERS_CONSTEXPR_CPP11 Optional(T val) FLATBUFFERS_NOEXCEPT
-    : value_(val), has_value_(true) {}
-
-  FLATBUFFERS_CONSTEXPR_CPP11 Optional(const Optional &other) FLATBUFFERS_NOEXCEPT
-    : value_(other.value_), has_value_(other.has_value_) {}
-
-  FLATBUFFERS_CONSTEXPR_CPP14 Optional &operator=(const Optional &other) FLATBUFFERS_NOEXCEPT {
-    value_ = other.value_;
-    has_value_ = other.has_value_;
-    return *this;
-  }
-
-  FLATBUFFERS_CONSTEXPR_CPP14 Optional &operator=(nullopt_t) FLATBUFFERS_NOEXCEPT {
-    value_ = T();
-    has_value_ = false;
-    return *this;
-  }
-
-  FLATBUFFERS_CONSTEXPR_CPP14 Optional &operator=(T val) FLATBUFFERS_NOEXCEPT {
-    value_ = val;
-    has_value_ = true;
-    return *this;
-  }
-
-  void reset() FLATBUFFERS_NOEXCEPT {
-    *this = nullopt;
-  }
-
-  void swap(Optional &other) FLATBUFFERS_NOEXCEPT {
-    std::swap(value_, other.value_);
-    std::swap(has_value_, other.has_value_);
-  }
-
-  FLATBUFFERS_CONSTEXPR_CPP11 FLATBUFFERS_EXPLICIT_CPP11 operator bool() const FLATBUFFERS_NOEXCEPT {
-    return has_value_;
-  }
-
-  FLATBUFFERS_CONSTEXPR_CPP11 bool has_value() const FLATBUFFERS_NOEXCEPT {
-    return has_value_;
-  }
-
-  FLATBUFFERS_CONSTEXPR_CPP11 const T& operator*() const FLATBUFFERS_NOEXCEPT {
-    return value_;
-  }
-
-  const T& value() const {
-    FLATBUFFERS_ASSERT(has_value());
-    return value_;
-  }
-
-  T value_or(T default_value) const FLATBUFFERS_NOEXCEPT {
-    return has_value() ? value_ : default_value;
-  }
-
- private:
-  T value_;
-  bool has_value_;
-};
-
-template<class T>
-FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(const Optional<T>& opt, nullopt_t) FLATBUFFERS_NOEXCEPT {
-  return !opt;
-}
-template<class T>
-FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(nullopt_t, const Optional<T>& opt) FLATBUFFERS_NOEXCEPT {
-  return !opt;
-}
-
-template<class T, class U>
-FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(const Optional<T>& lhs, const U& rhs) FLATBUFFERS_NOEXCEPT {
-  return static_cast<bool>(lhs) && (*lhs == rhs);
-}
-
-template<class T, class U>
-FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(const T& lhs, const Optional<U>& rhs) FLATBUFFERS_NOEXCEPT {
-  return static_cast<bool>(rhs) && (lhs == *rhs);
-}
-
-template<class T, class U>
-FLATBUFFERS_CONSTEXPR_CPP11 bool operator==(const Optional<T>& lhs, const Optional<U>& rhs) FLATBUFFERS_NOEXCEPT {
-  return static_cast<bool>(lhs) != static_cast<bool>(rhs)
-              ? false
-              : !static_cast<bool>(lhs) ? false : (*lhs == *rhs);
-}
-#endif // FLATBUFFERS_USE_STD_OPTIONAL
-
-
-// Very limited and naive partial implementation of C++20 std::span<T,Extent>.
-#if defined(FLATBUFFERS_USE_STD_SPAN)
-  inline constexpr std::size_t dynamic_extent = std::dynamic_extent;
-  template<class T, std::size_t Extent = std::dynamic_extent>
-  using span = std::span<T, Extent>;
-
-#else // !defined(FLATBUFFERS_USE_STD_SPAN)
-FLATBUFFERS_CONSTEXPR std::size_t dynamic_extent = static_cast<std::size_t>(-1);
-
-// Exclude this code if MSVC2010 or non-STL Android is active.
-// The non-STL Android doesn't have `std::is_convertible` required for SFINAE.
-#if !defined(FLATBUFFERS_SPAN_MINIMAL)
-namespace internal {
-  // This is SFINAE helper class for checking of a common condition:
-  // > This overload only participates in overload resolution
-  // > Check whether a pointer to an array of From can be converted
-  // > to a pointer to an array of To.
-  // This helper is used for checking of 'From -> const From'.
-  template<class To, std::size_t Extent, class From, std::size_t N>
-  struct is_span_convertable {
-    using type =
-      typename std::conditional<std::is_convertible<From (*)[], To (*)[]>::value
-                                && (Extent == dynamic_extent || N == Extent),
-                                int, void>::type;
-  };
-
-  template<typename T>
-  struct SpanIterator {
-    // TODO: upgrade to std::random_access_iterator_tag.
-    using iterator_category = std::forward_iterator_tag;
-    using difference_type  = std::ptrdiff_t;
-    using value_type = typename std::remove_cv<T>::type;
-    using reference = T&;
-    using pointer   = T*;
-
-    // Convince MSVC compiler that this iterator is trusted (it is verified).
-    #ifdef _MSC_VER
-      using _Unchecked_type = pointer;
-    #endif // _MSC_VER
-
-    SpanIterator(pointer ptr) : ptr_(ptr) {}
-    reference operator*() const { return *ptr_; }
-    pointer operator->() { return ptr_; }
-    SpanIterator& operator++() { ptr_++; return *this; }  
-    SpanIterator  operator++(int) { auto tmp = *this; ++(*this); return tmp; }
-
-    friend bool operator== (const SpanIterator& lhs, const SpanIterator& rhs) { return lhs.ptr_ == rhs.ptr_; }
-    friend bool operator!= (const SpanIterator& lhs, const SpanIterator& rhs) { return lhs.ptr_ != rhs.ptr_; }
-
-   private:
-    pointer ptr_;
-  };
-}  // namespace internal
-#endif  // !defined(FLATBUFFERS_SPAN_MINIMAL)
-
-// T - element type; must be a complete type that is not an abstract
-// class type.
-// Extent - the number of elements in the sequence, or dynamic.
-template<class T, std::size_t Extent = dynamic_extent>
-class span FLATBUFFERS_FINAL_CLASS {
- public:
-  typedef T element_type;
-  typedef T& reference;
-  typedef const T& const_reference;
-  typedef T* pointer;
-  typedef const T* const_pointer;
-  typedef std::size_t size_type;
-
-  static FLATBUFFERS_CONSTEXPR size_type extent = Extent;
-
-  // Returns the number of elements in the span.
-  FLATBUFFERS_CONSTEXPR_CPP11 size_type size() const FLATBUFFERS_NOEXCEPT {
-    return count_;
-  }
-
-  // Returns the size of the sequence in bytes.
-  FLATBUFFERS_CONSTEXPR_CPP11
-  size_type size_bytes() const FLATBUFFERS_NOEXCEPT {
-    return size() * sizeof(element_type);
-  }
-
-  // Checks if the span is empty.
-  FLATBUFFERS_CONSTEXPR_CPP11 bool empty() const FLATBUFFERS_NOEXCEPT {
-    return size() == 0;
-  }
-
-  // Returns a pointer to the beginning of the sequence.
-  FLATBUFFERS_CONSTEXPR_CPP11 pointer data() const FLATBUFFERS_NOEXCEPT {
-    return data_;
-  }
-
-  #if !defined(FLATBUFFERS_SPAN_MINIMAL)
-    using Iterator = internal::SpanIterator<T>;
-
-    Iterator begin() const { return Iterator(data()); }
-    Iterator end() const   { return Iterator(data() + size()); }
-  #endif
-
-  // Returns a reference to the idx-th element of the sequence.
-  // The behavior is undefined if the idx is greater than or equal to size().
-  FLATBUFFERS_CONSTEXPR_CPP11 reference operator[](size_type idx) const {
-    return data()[idx];
-  }
-
-  FLATBUFFERS_CONSTEXPR_CPP11 span(const span &other) FLATBUFFERS_NOEXCEPT
-      : data_(other.data_), count_(other.count_) {}
-
-  FLATBUFFERS_CONSTEXPR_CPP14 span &operator=(const span &other)
-      FLATBUFFERS_NOEXCEPT {
-    data_ = other.data_;
-    count_ = other.count_;
-  }
-
-  // Limited implementation of
-  // `template <class It> constexpr std::span(It first, size_type count);`.
-  //
-  // Constructs a span that is a view over the range [first, first + count);
-  // the resulting span has: data() == first and size() == count.
-  // The behavior is undefined if [first, first + count) is not a valid range,
-  // or if (extent != flatbuffers::dynamic_extent && count != extent).
-  FLATBUFFERS_CONSTEXPR_CPP11
-  explicit span(pointer first, size_type count) FLATBUFFERS_NOEXCEPT
-    : data_ (Extent == dynamic_extent ? first : (Extent == count ? first : nullptr)),
-      count_(Extent == dynamic_extent ? count : (Extent == count ? Extent : 0)) {
-      // Make span empty if the count argument is incompatible with span<T,N>.
-  }
-
-  // Exclude this code if MSVC2010 is active. The MSVC2010 isn't C++11
-  // compliant, it doesn't support default template arguments for functions.
-  #if defined(FLATBUFFERS_SPAN_MINIMAL)
-  FLATBUFFERS_CONSTEXPR_CPP11 span() FLATBUFFERS_NOEXCEPT : data_(nullptr),
-                                                            count_(0) {
-    static_assert(extent == 0 || extent == dynamic_extent, "invalid span");
-  }
-
-  #else
-  // Constructs an empty span whose data() == nullptr and size() == 0.
-  // This overload only participates in overload resolution if
-  // extent == 0 || extent == flatbuffers::dynamic_extent.
-  // A dummy template argument N is need dependency for SFINAE.
-  template<std::size_t N = 0,
-    typename internal::is_span_convertable<element_type, Extent, element_type, (N - N)>::type = 0>
-  FLATBUFFERS_CONSTEXPR_CPP11 span() FLATBUFFERS_NOEXCEPT : data_(nullptr),
-                                                            count_(0) {
-    static_assert(extent == 0 || extent == dynamic_extent, "invalid span");
-  }
-
-  // Constructs a span that is a view over the array arr; the resulting span
-  // has size() == N and data() == std::data(arr). These overloads only
-  // participate in overload resolution if
-  // extent == std::dynamic_extent || N == extent is true and
-  // std::remove_pointer_t<decltype(std::data(arr))>(*)[]
-  // is convertible to element_type (*)[].
-  template<std::size_t N,
-    typename internal::is_span_convertable<element_type, Extent, element_type, N>::type = 0>
-  FLATBUFFERS_CONSTEXPR_CPP11 span(element_type (&arr)[N]) FLATBUFFERS_NOEXCEPT
-      : data_(arr), count_(N) {}
-
-  template<class U, std::size_t N,
-    typename internal::is_span_convertable<element_type, Extent, U, N>::type = 0>
-  FLATBUFFERS_CONSTEXPR_CPP11 span(std::array<U, N> &arr) FLATBUFFERS_NOEXCEPT
-     : data_(arr.data()), count_(N) {}
-
-  //template<class U, std::size_t N,
-  //  int = 0>
-  //FLATBUFFERS_CONSTEXPR_CPP11 span(std::array<U, N> &arr) FLATBUFFERS_NOEXCEPT
-  //   : data_(arr.data()), count_(N) {}
-
-  template<class U, std::size_t N,
-    typename internal::is_span_convertable<element_type, Extent, U, N>::type = 0>
-  FLATBUFFERS_CONSTEXPR_CPP11 span(const std::array<U, N> &arr) FLATBUFFERS_NOEXCEPT
-    : data_(arr.data()), count_(N) {}
-
-  // Converting constructor from another span s;
-  // the resulting span has size() == s.size() and data() == s.data().
-  // This overload only participates in overload resolution
-  // if extent == std::dynamic_extent || N == extent is true and U (*)[]
-  // is convertible to element_type (*)[].
-  template<class U, std::size_t N,
-    typename internal::is_span_convertable<element_type, Extent, U, N>::type = 0>
-  FLATBUFFERS_CONSTEXPR_CPP11 span(const flatbuffers::span<U, N> &s) FLATBUFFERS_NOEXCEPT
-      : span(s.data(), s.size()) {
-  }
-
-  #endif  // !defined(FLATBUFFERS_SPAN_MINIMAL)
-
- private:
-  // This is a naive implementation with 'count_' member even if (Extent != dynamic_extent).
-  pointer const data_;
-  size_type count_;
-};
-#endif  // defined(FLATBUFFERS_USE_STD_SPAN)
-
-#if !defined(FLATBUFFERS_SPAN_MINIMAL)
-template<class ElementType, std::size_t Extent>
-FLATBUFFERS_CONSTEXPR_CPP11
-flatbuffers::span<ElementType, Extent> make_span(ElementType(&arr)[Extent]) FLATBUFFERS_NOEXCEPT {
-  return span<ElementType, Extent>(arr);
-}
-
-template<class ElementType, std::size_t Extent>
-FLATBUFFERS_CONSTEXPR_CPP11
-flatbuffers::span<const ElementType, Extent> make_span(const ElementType(&arr)[Extent]) FLATBUFFERS_NOEXCEPT {
-  return span<const ElementType, Extent>(arr);
-}
-
-template<class ElementType, std::size_t Extent>
-FLATBUFFERS_CONSTEXPR_CPP11
-flatbuffers::span<ElementType, Extent> make_span(std::array<ElementType, Extent> &arr) FLATBUFFERS_NOEXCEPT {
-  return span<ElementType, Extent>(arr);
-}
-
-template<class ElementType, std::size_t Extent>
-FLATBUFFERS_CONSTEXPR_CPP11
-flatbuffers::span<const ElementType, Extent> make_span(const std::array<ElementType, Extent> &arr) FLATBUFFERS_NOEXCEPT {
-  return span<const ElementType, Extent>(arr);
-}
-
-template<class ElementType, std::size_t Extent>
-FLATBUFFERS_CONSTEXPR_CPP11
-flatbuffers::span<ElementType, dynamic_extent> make_span(ElementType *first, std::size_t count) FLATBUFFERS_NOEXCEPT {
-  return span<ElementType, dynamic_extent>(first, count);
-}
-
-template<class ElementType, std::size_t Extent>
-FLATBUFFERS_CONSTEXPR_CPP11
-flatbuffers::span<const ElementType, dynamic_extent> make_span(const ElementType *first, std::size_t count) FLATBUFFERS_NOEXCEPT {
-  return span<const ElementType, dynamic_extent>(first, count);
-}
-#endif // !defined(FLATBUFFERS_SPAN_MINIMAL)
-
-}  // namespace flatbuffers
-
-#endif  // FLATBUFFERS_STL_EMULATION_H_

code/components/tflite-lib/third_party/flatbuffers/include/flatbuffers/table.h

@@ -1,168 +0,0 @@
-/*
- * Copyright 2021 Google Inc. All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef FLATBUFFERS_TABLE_H_
-#define FLATBUFFERS_TABLE_H_
-
-#include "flatbuffers/base.h"
-#include "flatbuffers/verifier.h"
-
-namespace flatbuffers {
-
-// "tables" use an offset table (possibly shared) that allows fields to be
-// omitted and added at will, but uses an extra indirection to read.
-class Table {
- public:
-  const uint8_t *GetVTable() const {
-    return data_ - ReadScalar<soffset_t>(data_);
-  }
-
-  // This gets the field offset for any of the functions below it, or 0
-  // if the field was not present.
-  voffset_t GetOptionalFieldOffset(voffset_t field) const {
-    // The vtable offset is always at the start.
-    auto vtable = GetVTable();
-    // The first element is the size of the vtable (fields + type id + itself).
-    auto vtsize = ReadScalar<voffset_t>(vtable);
-    // If the field we're accessing is outside the vtable, we're reading older
-    // data, so it's the same as if the offset was 0 (not present).
-    return field < vtsize ? ReadScalar<voffset_t>(vtable + field) : 0;
-  }
-
-  template<typename T> T GetField(voffset_t field, T defaultval) const {
-    auto field_offset = GetOptionalFieldOffset(field);
-    return field_offset ? ReadScalar<T>(data_ + field_offset) : defaultval;
-  }
-
-  template<typename P> P GetPointer(voffset_t field) {
-    auto field_offset = GetOptionalFieldOffset(field);
-    auto p = data_ + field_offset;
-    return field_offset ? reinterpret_cast<P>(p + ReadScalar<uoffset_t>(p))
-                        : nullptr;
-  }
-  template<typename P> P GetPointer(voffset_t field) const {
-    return const_cast<Table *>(this)->GetPointer<P>(field);
-  }
-
-  template<typename P> P GetStruct(voffset_t field) const {
-    auto field_offset = GetOptionalFieldOffset(field);
-    auto p = const_cast<uint8_t *>(data_ + field_offset);
-    return field_offset ? reinterpret_cast<P>(p) : nullptr;
-  }
-
-  template<typename Raw, typename Face>
-  flatbuffers::Optional<Face> GetOptional(voffset_t field) const {
-    auto field_offset = GetOptionalFieldOffset(field);
-    auto p = data_ + field_offset;
-    return field_offset ? Optional<Face>(static_cast<Face>(ReadScalar<Raw>(p)))
-                        : Optional<Face>();
-  }
-
-  template<typename T> bool SetField(voffset_t field, T val, T def) {
-    auto field_offset = GetOptionalFieldOffset(field);
-    if (!field_offset) return IsTheSameAs(val, def);
-    WriteScalar(data_ + field_offset, val);
-    return true;
-  }
-  template<typename T> bool SetField(voffset_t field, T val) {
-    auto field_offset = GetOptionalFieldOffset(field);
-    if (!field_offset) return false;
-    WriteScalar(data_ + field_offset, val);
-    return true;
-  }
-
-  bool SetPointer(voffset_t field, const uint8_t *val) {
-    auto field_offset = GetOptionalFieldOffset(field);
-    if (!field_offset) return false;
-    WriteScalar(data_ + field_offset,
-                static_cast<uoffset_t>(val - (data_ + field_offset)));
-    return true;
-  }
-
-  uint8_t *GetAddressOf(voffset_t field) {
-    auto field_offset = GetOptionalFieldOffset(field);
-    return field_offset ? data_ + field_offset : nullptr;
-  }
-  const uint8_t *GetAddressOf(voffset_t field) const {
-    return const_cast<Table *>(this)->GetAddressOf(field);
-  }
-
-  bool CheckField(voffset_t field) const {
-    return GetOptionalFieldOffset(field) != 0;
-  }
-
-  // Verify the vtable of this table.
-  // Call this once per table, followed by VerifyField once per field.
-  bool VerifyTableStart(Verifier &verifier) const {
-    return verifier.VerifyTableStart(data_);
-  }
-
-  // Verify a particular field.
-  template<typename T>
-  bool VerifyField(const Verifier &verifier, voffset_t field,
-                   size_t align) const {
-    // Calling GetOptionalFieldOffset should be safe now thanks to
-    // VerifyTable().
-    auto field_offset = GetOptionalFieldOffset(field);
-    // Check the actual field.
-    return !field_offset || verifier.VerifyField<T>(data_, field_offset, align);
-  }
-
-  // VerifyField for required fields.
-  template<typename T>
-  bool VerifyFieldRequired(const Verifier &verifier, voffset_t field,
-                           size_t align) const {
-    auto field_offset = GetOptionalFieldOffset(field);
-    return verifier.Check(field_offset != 0) &&
-           verifier.VerifyField<T>(data_, field_offset, align);
-  }
-
-  // Versions for offsets.
-  bool VerifyOffset(const Verifier &verifier, voffset_t field) const {
-    auto field_offset = GetOptionalFieldOffset(field);
-    return !field_offset || verifier.VerifyOffset(data_, field_offset);
-  }
-
-  bool VerifyOffsetRequired(const Verifier &verifier, voffset_t field) const {
-    auto field_offset = GetOptionalFieldOffset(field);
-    return verifier.Check(field_offset != 0) &&
-           verifier.VerifyOffset(data_, field_offset);
-  }
-
- private:
-  // private constructor & copy constructor: you obtain instances of this
-  // class by pointing to existing data only
-  Table();
-  Table(const Table &other);
-  Table &operator=(const Table &);
-
-  uint8_t data_[1];
-};
-
-// This specialization allows avoiding warnings like:
-// MSVC C4800: type: forcing value to bool 'true' or 'false'.
-template<>
-inline flatbuffers::Optional<bool> Table::GetOptional<uint8_t, bool>(
-    voffset_t field) const {
-  auto field_offset = GetOptionalFieldOffset(field);
-  auto p = data_ + field_offset;
-  return field_offset ? Optional<bool>(ReadScalar<uint8_t>(p) != 0)
-                      : Optional<bool>();
-}
-
-}  // namespace flatbuffers
-
-#endif  // FLATBUFFERS_TABLE_H_

code/components/tflite-lib/third_party/flatbuffers/include/flatbuffers/util.h

@@ -1,725 +0,0 @@
-/*
- * Copyright 2014 Google Inc. All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef FLATBUFFERS_UTIL_H_
-#define FLATBUFFERS_UTIL_H_
-
-#include <ctype.h>
-#include <errno.h>
-
-#include "flatbuffers/base.h"
-#include "flatbuffers/stl_emulation.h"
-
-// For TFLM we always want to use FLATBUFFERS_PREFER_PRINTF=1. See
-// http://b/211811553 for more context.
-#ifndef FLATBUFFERS_PREFER_PRINTF
-#define FLATBUFFERS_PREFER_PRINTF 1
-#endif
-
-#ifndef FLATBUFFERS_PREFER_PRINTF
-#  include <iomanip>
-#  include <sstream>
-#else  // FLATBUFFERS_PREFER_PRINTF
-#  include <float.h>
-#  include <stdio.h>
-#endif  // FLATBUFFERS_PREFER_PRINTF
-
-#include <string>
-
-namespace flatbuffers {
-
-// @locale-independent functions for ASCII characters set.
-
-// Fast checking that character lies in closed range: [a <= x <= b]
-// using one compare (conditional branch) operator.
-inline bool check_ascii_range(char x, char a, char b) {
-  FLATBUFFERS_ASSERT(a <= b);
-  // (Hacker's Delight): `a <= x <= b` <=> `(x-a) <={u} (b-a)`.
-  // The x, a, b will be promoted to int and subtracted without overflow.
-  return static_cast<unsigned int>(x - a) <= static_cast<unsigned int>(b - a);
-}
-
-// Case-insensitive isalpha
-inline bool is_alpha(char c) {
-  // ASCII only: alpha to upper case => reset bit 0x20 (~0x20 = 0xDF).
-  return check_ascii_range(c & 0xDF, 'a' & 0xDF, 'z' & 0xDF);
-}
-
-// Check for uppercase alpha
-inline bool is_alpha_upper(char c) { return check_ascii_range(c, 'A', 'Z'); }
-
-// Check (case-insensitive) that `c` is equal to alpha.
-inline bool is_alpha_char(char c, char alpha) {
-  FLATBUFFERS_ASSERT(is_alpha(alpha));
-  // ASCII only: alpha to upper case => reset bit 0x20 (~0x20 = 0xDF).
-  return ((c & 0xDF) == (alpha & 0xDF));
-}
-
-// https://en.cppreference.com/w/cpp/string/byte/isxdigit
-// isdigit and isxdigit are the only standard narrow character classification
-// functions that are not affected by the currently installed C locale. although
-// some implementations (e.g. Microsoft in 1252 codepage) may classify
-// additional single-byte characters as digits.
-inline bool is_digit(char c) { return check_ascii_range(c, '0', '9'); }
-
-inline bool is_xdigit(char c) {
-  // Replace by look-up table.
-  return is_digit(c) || check_ascii_range(c & 0xDF, 'a' & 0xDF, 'f' & 0xDF);
-}
-
-// Case-insensitive isalnum
-inline bool is_alnum(char c) { return is_alpha(c) || is_digit(c); }
-
-inline char CharToUpper(char c) {
-  return static_cast<char>(::toupper(static_cast<unsigned char>(c)));
-}
-
-inline char CharToLower(char c) {
-  return static_cast<char>(::tolower(static_cast<unsigned char>(c)));
-}
-
-// @end-locale-independent functions for ASCII character set
-
-#ifdef FLATBUFFERS_PREFER_PRINTF
-template<typename T> size_t IntToDigitCount(T t) {
-  size_t digit_count = 0;
-  // Count the sign for negative numbers
-  if (t < 0) digit_count++;
-  // Count a single 0 left of the dot for fractional numbers
-  if (-1 < t && t < 1) digit_count++;
-  // Count digits until fractional part
-  T eps = std::numeric_limits<T>::epsilon();
-  while (t <= (-1 + eps) || (1 - eps) <= t) {
-    t /= 10;
-    digit_count++;
-  }
-  return digit_count;
-}
-
-template<typename T> size_t NumToStringWidth(T t, int precision = 0) {
-  size_t string_width = IntToDigitCount(t);
-  // Count the dot for floating point numbers
-  if (precision) string_width += (precision + 1);
-  return string_width;
-}
-
-template<typename T>
-std::string NumToStringImplWrapper(T t, const char *fmt, int precision = 0) {
-  size_t string_width = NumToStringWidth(t, precision);
-  std::string s(string_width, 0x00);
-  // Allow snprintf to use std::string trailing null to detect buffer overflow
-  snprintf(const_cast<char *>(s.data()), (s.size() + 1), fmt, string_width, t);
-  return s;
-}
-#endif  // FLATBUFFERS_PREFER_PRINTF
-
-// Convert an integer or floating point value to a string.
-// In contrast to std::stringstream, "char" values are
-// converted to a string of digits, and we don't use scientific notation.
-template<typename T> std::string NumToString(T t) {
-  // clang-format off
-
-  #ifndef FLATBUFFERS_PREFER_PRINTF
-    std::stringstream ss;
-    ss << t;
-    return ss.str();
-  #else // FLATBUFFERS_PREFER_PRINTF
-    auto v = static_cast<long long>(t);
-    return NumToStringImplWrapper(v, "%.*lld");
-  #endif // FLATBUFFERS_PREFER_PRINTF
-  // clang-format on
-}
-// Avoid char types used as character data.
-template<> inline std::string NumToString<signed char>(signed char t) {
-  return NumToString(static_cast<int>(t));
-}
-template<> inline std::string NumToString<unsigned char>(unsigned char t) {
-  return NumToString(static_cast<int>(t));
-}
-template<> inline std::string NumToString<char>(char t) {
-  return NumToString(static_cast<int>(t));
-}
-
-// Special versions for floats/doubles.
-template<typename T> std::string FloatToString(T t, int precision) {
-  // clang-format off
-
-  #ifndef FLATBUFFERS_PREFER_PRINTF
-    // to_string() prints different numbers of digits for floats depending on
-    // platform and isn't available on Android, so we use stringstream
-    std::stringstream ss;
-    // Use std::fixed to suppress scientific notation.
-    ss << std::fixed;
-    // Default precision is 6, we want that to be higher for doubles.
-    ss << std::setprecision(precision);
-    ss << t;
-    auto s = ss.str();
-  #else // FLATBUFFERS_PREFER_PRINTF
-    auto v = static_cast<double>(t);
-    auto s = NumToStringImplWrapper(v, "%0.*f", precision);
-  #endif // FLATBUFFERS_PREFER_PRINTF
-  // clang-format on
-  // Sadly, std::fixed turns "1" into "1.00000", so here we undo that.
-  auto p = s.find_last_not_of('0');
-  if (p != std::string::npos) {
-    // Strip trailing zeroes. If it is a whole number, keep one zero.
-    s.resize(p + (s[p] == '.' ? 2 : 1));
-  }
-  return s;
-}
-
-template<> inline std::string NumToString<double>(double t) {
-  return FloatToString(t, 12);
-}
-template<> inline std::string NumToString<float>(float t) {
-  return FloatToString(t, 6);
-}
-
-// Convert an integer value to a hexadecimal string.
-// The returned string length is always xdigits long, prefixed by 0 digits.
-// For example, IntToStringHex(0x23, 8) returns the string "00000023".
-inline std::string IntToStringHex(int i, int xdigits) {
-  FLATBUFFERS_ASSERT(i >= 0);
-  // clang-format off
-
-  #ifndef FLATBUFFERS_PREFER_PRINTF
-    std::stringstream ss;
-    ss << std::setw(xdigits) << std::setfill('0') << std::hex << std::uppercase
-       << i;
-    return ss.str();
-  #else // FLATBUFFERS_PREFER_PRINTF
-    return NumToStringImplWrapper(i, "%.*X", xdigits);
-  #endif // FLATBUFFERS_PREFER_PRINTF
-  // clang-format on
-}
-
-// clang-format off
-// Use locale independent functions {strtod_l, strtof_l, strtoll_l, strtoull_l}.
-#if defined(FLATBUFFERS_LOCALE_INDEPENDENT) && (FLATBUFFERS_LOCALE_INDEPENDENT > 0)
-  class ClassicLocale {
-    #ifdef _MSC_VER
-      typedef _locale_t locale_type;
-    #else
-      typedef locale_t locale_type;  // POSIX.1-2008 locale_t type
-    #endif
-    ClassicLocale();
-    ~ClassicLocale();
-    locale_type locale_;
-    static ClassicLocale instance_;
-  public:
-    static locale_type Get() { return instance_.locale_; }
-  };
-
-  #ifdef _MSC_VER
-    #define __strtoull_impl(s, pe, b) _strtoui64_l(s, pe, b, ClassicLocale::Get())
-    #define __strtoll_impl(s, pe, b) _strtoi64_l(s, pe, b, ClassicLocale::Get())
-    #define __strtod_impl(s, pe) _strtod_l(s, pe, ClassicLocale::Get())
-    #define __strtof_impl(s, pe) _strtof_l(s, pe, ClassicLocale::Get())
-  #else
-    #define __strtoull_impl(s, pe, b) strtoull_l(s, pe, b, ClassicLocale::Get())
-    #define __strtoll_impl(s, pe, b) strtoll_l(s, pe, b, ClassicLocale::Get())
-    #define __strtod_impl(s, pe) strtod_l(s, pe, ClassicLocale::Get())
-    #define __strtof_impl(s, pe) strtof_l(s, pe, ClassicLocale::Get())
-  #endif
-#else
-  #define __strtod_impl(s, pe) strtod(s, pe)
-  #define __strtof_impl(s, pe) static_cast<float>(strtod(s, pe))
-  #ifdef _MSC_VER
-    #define __strtoull_impl(s, pe, b) _strtoui64(s, pe, b)
-    #define __strtoll_impl(s, pe, b) _strtoi64(s, pe, b)
-  #else
-    #define __strtoull_impl(s, pe, b) strtoull(s, pe, b)
-    #define __strtoll_impl(s, pe, b) strtoll(s, pe, b)
-  #endif
-#endif
-
-inline void strtoval_impl(int64_t *val, const char *str, char **endptr,
-                                 int base) {
-    *val = __strtoll_impl(str, endptr, base);
-}
-
-inline void strtoval_impl(uint64_t *val, const char *str, char **endptr,
-                                 int base) {
-  *val = __strtoull_impl(str, endptr, base);
-}
-
-inline void strtoval_impl(double *val, const char *str, char **endptr) {
-  *val = __strtod_impl(str, endptr);
-}
-
-// UBSAN: double to float is safe if numeric_limits<float>::is_iec559 is true.
-__supress_ubsan__("float-cast-overflow")
-inline void strtoval_impl(float *val, const char *str, char **endptr) {
-  *val = __strtof_impl(str, endptr);
-}
-#undef __strtoull_impl
-#undef __strtoll_impl
-#undef __strtod_impl
-#undef __strtof_impl
-// clang-format on
-
-// Adaptor for strtoull()/strtoll().
-// Flatbuffers accepts numbers with any count of leading zeros (-009 is -9),
-// while strtoll with base=0 interprets first leading zero as octal prefix.
-// In future, it is possible to add prefixed 0b0101.
-// 1) Checks errno code for overflow condition (out of range).
-// 2) If base <= 0, function try to detect base of number by prefix.
-//
-// Return value (like strtoull and strtoll, but reject partial result):
-// - If successful, an integer value corresponding to the str is returned.
-// - If full string conversion can't be performed, 0 is returned.
-// - If the converted value falls out of range of corresponding return type, a
-// range error occurs. In this case value MAX(T)/MIN(T) is returned.
-template<typename T>
-inline bool StringToIntegerImpl(T *val, const char *const str,
-                                const int base = 0,
-                                const bool check_errno = true) {
-  // T is int64_t or uint64_T
-  FLATBUFFERS_ASSERT(str);
-  if (base <= 0) {
-    auto s = str;
-    while (*s && !is_digit(*s)) s++;
-    if (s[0] == '0' && is_alpha_char(s[1], 'X'))
-      return StringToIntegerImpl(val, str, 16, check_errno);
-    // if a prefix not match, try base=10
-    return StringToIntegerImpl(val, str, 10, check_errno);
-  } else {
-    if (check_errno) errno = 0;  // clear thread-local errno
-    auto endptr = str;
-    strtoval_impl(val, str, const_cast<char **>(&endptr), base);
-    if ((*endptr != '\0') || (endptr == str)) {
-      *val = 0;      // erase partial result
-      return false;  // invalid string
-    }
-    // errno is out-of-range, return MAX/MIN
-    if (check_errno && errno) return false;
-    return true;
-  }
-}
-
-template<typename T>
-inline bool StringToFloatImpl(T *val, const char *const str) {
-  // Type T must be either float or double.
-  FLATBUFFERS_ASSERT(str && val);
-  auto end = str;
-  strtoval_impl(val, str, const_cast<char **>(&end));
-  auto done = (end != str) && (*end == '\0');
-  if (!done) *val = 0;  // erase partial result
-  return done;
-}
-
-// Convert a string to an instance of T.
-// Return value (matched with StringToInteger64Impl and strtod):
-// - If successful, a numeric value corresponding to the str is returned.
-// - If full string conversion can't be performed, 0 is returned.
-// - If the converted value falls out of range of corresponding return type, a
-// range error occurs. In this case value MAX(T)/MIN(T) is returned.
-template<typename T> inline bool StringToNumber(const char *s, T *val) {
-  // Assert on `unsigned long` and `signed long` on LP64.
-  // If it is necessary, it could be solved with flatbuffers::enable_if<B,T>.
-  static_assert(sizeof(T) < sizeof(int64_t), "unexpected type T");
-  FLATBUFFERS_ASSERT(s && val);
-  int64_t i64;
-  // The errno check isn't needed, will return MAX/MIN on overflow.
-  if (StringToIntegerImpl(&i64, s, 0, false)) {
-    const int64_t max = (flatbuffers::numeric_limits<T>::max)();
-    const int64_t min = flatbuffers::numeric_limits<T>::lowest();
-    if (i64 > max) {
-      *val = static_cast<T>(max);
-      return false;
-    }
-    if (i64 < min) {
-      // For unsigned types return max to distinguish from
-      // "no conversion can be performed" when 0 is returned.
-      *val = static_cast<T>(flatbuffers::is_unsigned<T>::value ? max : min);
-      return false;
-    }
-    *val = static_cast<T>(i64);
-    return true;
-  }
-  *val = 0;
-  return false;
-}
-
-template<> inline bool StringToNumber<int64_t>(const char *str, int64_t *val) {
-  return StringToIntegerImpl(val, str);
-}
-
-template<>
-inline bool StringToNumber<uint64_t>(const char *str, uint64_t *val) {
-  if (!StringToIntegerImpl(val, str)) return false;
-  // The strtoull accepts negative numbers:
-  // If the minus sign was part of the input sequence, the numeric value
-  // calculated from the sequence of digits is negated as if by unary minus
-  // in the result type, which applies unsigned integer wraparound rules.
-  // Fix this behaviour (except -0).
-  if (*val) {
-    auto s = str;
-    while (*s && !is_digit(*s)) s++;
-    s = (s > str) ? (s - 1) : s;  // step back to one symbol
-    if (*s == '-') {
-      // For unsigned types return the max to distinguish from
-      // "no conversion can be performed".
-      *val = (flatbuffers::numeric_limits<uint64_t>::max)();
-      return false;
-    }
-  }
-  return true;
-}
-
-template<> inline bool StringToNumber(const char *s, float *val) {
-  return StringToFloatImpl(val, s);
-}
-
-template<> inline bool StringToNumber(const char *s, double *val) {
-  return StringToFloatImpl(val, s);
-}
-
-inline int64_t StringToInt(const char *s, int base = 10) {
-  int64_t val;
-  return StringToIntegerImpl(&val, s, base) ? val : 0;
-}
-
-inline uint64_t StringToUInt(const char *s, int base = 10) {
-  uint64_t val;
-  return StringToIntegerImpl(&val, s, base) ? val : 0;
-}
-
-typedef bool (*LoadFileFunction)(const char *filename, bool binary,
-                                 std::string *dest);
-typedef bool (*FileExistsFunction)(const char *filename);
-
-LoadFileFunction SetLoadFileFunction(LoadFileFunction load_file_function);
-
-FileExistsFunction SetFileExistsFunction(
-    FileExistsFunction file_exists_function);
-
-// Check if file "name" exists.
-bool FileExists(const char *name);
-
-// Check if "name" exists and it is also a directory.
-bool DirExists(const char *name);
-
-// Load file "name" into "buf" returning true if successful
-// false otherwise.  If "binary" is false data is read
-// using ifstream's text mode, otherwise data is read with
-// no transcoding.
-bool LoadFile(const char *name, bool binary, std::string *buf);
-
-// Save data "buf" of length "len" bytes into a file
-// "name" returning true if successful, false otherwise.
-// If "binary" is false data is written using ifstream's
-// text mode, otherwise data is written with no
-// transcoding.
-bool SaveFile(const char *name, const char *buf, size_t len, bool binary);
-
-// Save data "buf" into file "name" returning true if
-// successful, false otherwise.  If "binary" is false
-// data is written using ifstream's text mode, otherwise
-// data is written with no transcoding.
-inline bool SaveFile(const char *name, const std::string &buf, bool binary) {
-  return SaveFile(name, buf.c_str(), buf.size(), binary);
-}
-
-// Functionality for minimalistic portable path handling.
-
-// The functions below behave correctly regardless of whether posix ('/') or
-// Windows ('/' or '\\') separators are used.
-
-// Any new separators inserted are always posix.
-FLATBUFFERS_CONSTEXPR char kPathSeparator = '/';
-
-// Returns the path with the extension, if any, removed.
-std::string StripExtension(const std::string &filepath);
-
-// Returns the extension, if any.
-std::string GetExtension(const std::string &filepath);
-
-// Return the last component of the path, after the last separator.
-std::string StripPath(const std::string &filepath);
-
-// Strip the last component of the path + separator.
-std::string StripFileName(const std::string &filepath);
-
-std::string StripPrefix(const std::string &filepath,
-                        const std::string &prefix_to_remove);
-
-// Concatenates a path with a filename, regardless of whether the path
-// ends in a separator or not.
-std::string ConCatPathFileName(const std::string &path,
-                               const std::string &filename);
-
-// Replaces any '\\' separators with '/'
-std::string PosixPath(const char *path);
-std::string PosixPath(const std::string &path);
-
-// This function ensure a directory exists, by recursively
-// creating dirs for any parts of the path that don't exist yet.
-void EnsureDirExists(const std::string &filepath);
-
-// Obtains the absolute path from any other path.
-// Returns the input path if the absolute path couldn't be resolved.
-std::string AbsolutePath(const std::string &filepath);
-
-// Returns files relative to the --project_root path, prefixed with `//`.
-std::string RelativeToRootPath(const std::string &project,
-                               const std::string &filepath);
-
-// To and from UTF-8 unicode conversion functions
-
-// Convert a unicode code point into a UTF-8 representation by appending it
-// to a string. Returns the number of bytes generated.
-inline int ToUTF8(uint32_t ucc, std::string *out) {
-  FLATBUFFERS_ASSERT(!(ucc & 0x80000000));  // Top bit can't be set.
-  // 6 possible encodings: http://en.wikipedia.org/wiki/UTF-8
-  for (int i = 0; i < 6; i++) {
-    // Max bits this encoding can represent.
-    uint32_t max_bits = 6 + i * 5 + static_cast<int>(!i);
-    if (ucc < (1u << max_bits)) {  // does it fit?
-      // Remaining bits not encoded in the first byte, store 6 bits each
-      uint32_t remain_bits = i * 6;
-      // Store first byte:
-      (*out) += static_cast<char>((0xFE << (max_bits - remain_bits)) |
-                                  (ucc >> remain_bits));
-      // Store remaining bytes:
-      for (int j = i - 1; j >= 0; j--) {
-        (*out) += static_cast<char>(((ucc >> (j * 6)) & 0x3F) | 0x80);
-      }
-      return i + 1;  // Return the number of bytes added.
-    }
-  }
-  FLATBUFFERS_ASSERT(0);  // Impossible to arrive here.
-  return -1;
-}
-
-// Converts whatever prefix of the incoming string corresponds to a valid
-// UTF-8 sequence into a unicode code. The incoming pointer will have been
-// advanced past all bytes parsed.
-// returns -1 upon corrupt UTF-8 encoding (ignore the incoming pointer in
-// this case).
-inline int FromUTF8(const char **in) {
-  int len = 0;
-  // Count leading 1 bits.
-  for (int mask = 0x80; mask >= 0x04; mask >>= 1) {
-    if (**in & mask) {
-      len++;
-    } else {
-      break;
-    }
-  }
-  if ((static_cast<unsigned char>(**in) << len) & 0x80)
-    return -1;  // Bit after leading 1's must be 0.
-  if (!len) return *(*in)++;
-  // UTF-8 encoded values with a length are between 2 and 4 bytes.
-  if (len < 2 || len > 4) { return -1; }
-  // Grab initial bits of the code.
-  int ucc = *(*in)++ & ((1 << (7 - len)) - 1);
-  for (int i = 0; i < len - 1; i++) {
-    if ((**in & 0xC0) != 0x80) return -1;  // Upper bits must 1 0.
-    ucc <<= 6;
-    ucc |= *(*in)++ & 0x3F;  // Grab 6 more bits of the code.
-  }
-  // UTF-8 cannot encode values between 0xD800 and 0xDFFF (reserved for
-  // UTF-16 surrogate pairs).
-  if (ucc >= 0xD800 && ucc <= 0xDFFF) { return -1; }
-  // UTF-8 must represent code points in their shortest possible encoding.
-  switch (len) {
-    case 2:
-      // Two bytes of UTF-8 can represent code points from U+0080 to U+07FF.
-      if (ucc < 0x0080 || ucc > 0x07FF) { return -1; }
-      break;
-    case 3:
-      // Three bytes of UTF-8 can represent code points from U+0800 to U+FFFF.
-      if (ucc < 0x0800 || ucc > 0xFFFF) { return -1; }
-      break;
-    case 4:
-      // Four bytes of UTF-8 can represent code points from U+10000 to U+10FFFF.
-      if (ucc < 0x10000 || ucc > 0x10FFFF) { return -1; }
-      break;
-  }
-  return ucc;
-}
-
-#ifndef FLATBUFFERS_PREFER_PRINTF
-// Wraps a string to a maximum length, inserting new lines where necessary. Any
-// existing whitespace will be collapsed down to a single space. A prefix or
-// suffix can be provided, which will be inserted before or after a wrapped
-// line, respectively.
-inline std::string WordWrap(const std::string in, size_t max_length,
-                            const std::string wrapped_line_prefix,
-                            const std::string wrapped_line_suffix) {
-  std::istringstream in_stream(in);
-  std::string wrapped, line, word;
-
-  in_stream >> word;
-  line = word;
-
-  while (in_stream >> word) {
-    if ((line.length() + 1 + word.length() + wrapped_line_suffix.length()) <
-        max_length) {
-      line += " " + word;
-    } else {
-      wrapped += line + wrapped_line_suffix + "\n";
-      line = wrapped_line_prefix + word;
-    }
-  }
-  wrapped += line;
-
-  return wrapped;
-}
-#endif  // !FLATBUFFERS_PREFER_PRINTF
-
-inline bool EscapeString(const char *s, size_t length, std::string *_text,
-                         bool allow_non_utf8, bool natural_utf8) {
-  std::string &text = *_text;
-  text += "\"";
-  for (uoffset_t i = 0; i < length; i++) {
-    char c = s[i];
-    switch (c) {
-      case '\n': text += "\\n"; break;
-      case '\t': text += "\\t"; break;
-      case '\r': text += "\\r"; break;
-      case '\b': text += "\\b"; break;
-      case '\f': text += "\\f"; break;
-      case '\"': text += "\\\""; break;
-      case '\\': text += "\\\\"; break;
-      default:
-        if (c >= ' ' && c <= '~') {
-          text += c;
-        } else {
-          // Not printable ASCII data. Let's see if it's valid UTF-8 first:
-          const char *utf8 = s + i;
-          int ucc = FromUTF8(&utf8);
-          if (ucc < 0) {
-            if (allow_non_utf8) {
-              text += "\\x";
-              text += IntToStringHex(static_cast<uint8_t>(c), 2);
-            } else {
-              // There are two cases here:
-              //
-              // 1) We reached here by parsing an IDL file. In that case,
-              // we previously checked for non-UTF-8, so we shouldn't reach
-              // here.
-              //
-              // 2) We reached here by someone calling GenerateText()
-              // on a previously-serialized flatbuffer. The data might have
-              // non-UTF-8 Strings, or might be corrupt.
-              //
-              // In both cases, we have to give up and inform the caller
-              // they have no JSON.
-              return false;
-            }
-          } else {
-            if (natural_utf8) {
-              // utf8 points to past all utf-8 bytes parsed
-              text.append(s + i, static_cast<size_t>(utf8 - s - i));
-            } else if (ucc <= 0xFFFF) {
-              // Parses as Unicode within JSON's \uXXXX range, so use that.
-              text += "\\u";
-              text += IntToStringHex(ucc, 4);
-            } else if (ucc <= 0x10FFFF) {
-              // Encode Unicode SMP values to a surrogate pair using two \u
-              // escapes.
-              uint32_t base = ucc - 0x10000;
-              auto high_surrogate = (base >> 10) + 0xD800;
-              auto low_surrogate = (base & 0x03FF) + 0xDC00;
-              text += "\\u";
-              text += IntToStringHex(high_surrogate, 4);
-              text += "\\u";
-              text += IntToStringHex(low_surrogate, 4);
-            }
-            // Skip past characters recognized.
-            i = static_cast<uoffset_t>(utf8 - s - 1);
-          }
-        }
-        break;
-    }
-  }
-  text += "\"";
-  return true;
-}
-
-inline std::string BufferToHexText(const void *buffer, size_t buffer_size,
-                                   size_t max_length,
-                                   const std::string &wrapped_line_prefix,
-                                   const std::string &wrapped_line_suffix) {
-  std::string text = wrapped_line_prefix;
-  size_t start_offset = 0;
-  const char *s = reinterpret_cast<const char *>(buffer);
-  for (size_t i = 0; s && i < buffer_size; i++) {
-    // Last iteration or do we have more?
-    bool have_more = i + 1 < buffer_size;
-    text += "0x";
-    text += IntToStringHex(static_cast<uint8_t>(s[i]), 2);
-    if (have_more) { text += ','; }
-    // If we have more to process and we reached max_length
-    if (have_more &&
-        text.size() + wrapped_line_suffix.size() >= start_offset + max_length) {
-      text += wrapped_line_suffix;
-      text += '\n';
-      start_offset = text.size();
-      text += wrapped_line_prefix;
-    }
-  }
-  text += wrapped_line_suffix;
-  return text;
-}
-
-// Remove paired quotes in a string: "text"|'text' -> text.
-std::string RemoveStringQuotes(const std::string &s);
-
-// Change th global C-locale to locale with name <locale_name>.
-// Returns an actual locale name in <_value>, useful if locale_name is "" or
-// null.
-bool SetGlobalTestLocale(const char *locale_name,
-                         std::string *_value = nullptr);
-
-// Read (or test) a value of environment variable.
-bool ReadEnvironmentVariable(const char *var_name,
-                             std::string *_value = nullptr);
-
-// MSVC specific: Send all assert reports to STDOUT to prevent CI hangs.
-void SetupDefaultCRTReportMode();
-
-enum class Case {
-  kUnknown = 0,
-  // TheQuickBrownFox
-  kUpperCamel = 1,
-  // theQuickBrownFox
-  kLowerCamel = 2,
-  // the_quick_brown_fox
-  kSnake = 3,
-  // THE_QUICK_BROWN_FOX
-  kScreamingSnake = 4,
-  // THEQUICKBROWNFOX
-  kAllUpper = 5,
-  // thequickbrownfox
-  kAllLower = 6,
-  // the-quick-brown-fox
-  kDasher = 7,
-  // THEQuiCKBr_ownFox (or whatever you want, we won't change it)
-  kKeep = 8,
-  // the_quick_brown_fox123 (as opposed to the_quick_brown_fox_123)
-  kSnake2 = 9,
-};
-
-// Convert the `input` string of case `input_case` to the specified `output_case`.
-std::string ConvertCase(const std::string &input, Case output_case,
-                    Case input_case = Case::kSnake);
-
-}  // namespace flatbuffers
-
-#endif  // FLATBUFFERS_UTIL_H_

code/components/tflite-lib/third_party/flatbuffers/include/flatbuffers/vector.h

@@ -1,389 +0,0 @@
-/*
- * Copyright 2021 Google Inc. All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef FLATBUFFERS_VECTOR_H_
-#define FLATBUFFERS_VECTOR_H_
-
-#include "flatbuffers/base.h"
-#include "flatbuffers/buffer.h"
-#include "flatbuffers/stl_emulation.h"
-
-namespace flatbuffers {
-
-struct String;
-
-// An STL compatible iterator implementation for Vector below, effectively
-// calling Get() for every element.
-template<typename T, typename IT> struct VectorIterator {
-  typedef std::random_access_iterator_tag iterator_category;
-  typedef IT value_type;
-  typedef ptrdiff_t difference_type;
-  typedef IT *pointer;
-  typedef IT &reference;
-
-  VectorIterator(const uint8_t *data, uoffset_t i)
-      : data_(data + IndirectHelper<T>::element_stride * i) {}
-  VectorIterator(const VectorIterator &other) : data_(other.data_) {}
-  VectorIterator() : data_(nullptr) {}
-
-  VectorIterator &operator=(const VectorIterator &other) {
-    data_ = other.data_;
-    return *this;
-  }
-
-  VectorIterator &operator=(VectorIterator &&other) {
-    data_ = other.data_;
-    return *this;
-  }
-
-  bool operator==(const VectorIterator &other) const {
-    return data_ == other.data_;
-  }
-
-  bool operator<(const VectorIterator &other) const {
-    return data_ < other.data_;
-  }
-
-  bool operator!=(const VectorIterator &other) const {
-    return data_ != other.data_;
-  }
-
-  difference_type operator-(const VectorIterator &other) const {
-    return (data_ - other.data_) / IndirectHelper<T>::element_stride;
-  }
-
-  // Note: return type is incompatible with the standard
-  // `reference operator*()`.
-  IT operator*() const { return IndirectHelper<T>::Read(data_, 0); }
-
-  // Note: return type is incompatible with the standard
-  // `pointer operator->()`.
-  IT operator->() const { return IndirectHelper<T>::Read(data_, 0); }
-
-  VectorIterator &operator++() {
-    data_ += IndirectHelper<T>::element_stride;
-    return *this;
-  }
-
-  VectorIterator operator++(int) {
-    VectorIterator temp(data_, 0);
-    data_ += IndirectHelper<T>::element_stride;
-    return temp;
-  }
-
-  VectorIterator operator+(const uoffset_t &offset) const {
-    return VectorIterator(data_ + offset * IndirectHelper<T>::element_stride,
-                          0);
-  }
-
-  VectorIterator &operator+=(const uoffset_t &offset) {
-    data_ += offset * IndirectHelper<T>::element_stride;
-    return *this;
-  }
-
-  VectorIterator &operator--() {
-    data_ -= IndirectHelper<T>::element_stride;
-    return *this;
-  }
-
-  VectorIterator operator--(int) {
-    VectorIterator temp(data_, 0);
-    data_ -= IndirectHelper<T>::element_stride;
-    return temp;
-  }
-
-  VectorIterator operator-(const uoffset_t &offset) const {
-    return VectorIterator(data_ - offset * IndirectHelper<T>::element_stride,
-                          0);
-  }
-
-  VectorIterator &operator-=(const uoffset_t &offset) {
-    data_ -= offset * IndirectHelper<T>::element_stride;
-    return *this;
-  }
-
- private:
-  const uint8_t *data_;
-};
-
-template<typename Iterator>
-struct VectorReverseIterator : public std::reverse_iterator<Iterator> {
-  explicit VectorReverseIterator(Iterator iter)
-      : std::reverse_iterator<Iterator>(iter) {}
-
-  // Note: return type is incompatible with the standard
-  // `reference operator*()`.
-  typename Iterator::value_type operator*() const {
-    auto tmp = std::reverse_iterator<Iterator>::current;
-    return *--tmp;
-  }
-
-  // Note: return type is incompatible with the standard
-  // `pointer operator->()`.
-  typename Iterator::value_type operator->() const {
-    auto tmp = std::reverse_iterator<Iterator>::current;
-    return *--tmp;
-  }
-};
-
-// This is used as a helper type for accessing vectors.
-// Vector::data() assumes the vector elements start after the length field.
-template<typename T> class Vector {
- public:
-  typedef VectorIterator<T, typename IndirectHelper<T>::mutable_return_type>
-      iterator;
-  typedef VectorIterator<T, typename IndirectHelper<T>::return_type>
-      const_iterator;
-  typedef VectorReverseIterator<iterator> reverse_iterator;
-  typedef VectorReverseIterator<const_iterator> const_reverse_iterator;
-
-  typedef typename flatbuffers::bool_constant<flatbuffers::is_scalar<T>::value>
-      scalar_tag;
-
-  static FLATBUFFERS_CONSTEXPR bool is_span_observable =
-      scalar_tag::value && (FLATBUFFERS_LITTLEENDIAN || sizeof(T) == 1);
-
-  uoffset_t size() const { return EndianScalar(length_); }
-
-  // Deprecated: use size(). Here for backwards compatibility.
-  FLATBUFFERS_ATTRIBUTE([[deprecated("use size() instead")]])
-  uoffset_t Length() const { return size(); }
-
-  typedef typename IndirectHelper<T>::return_type return_type;
-  typedef typename IndirectHelper<T>::mutable_return_type mutable_return_type;
-  typedef return_type value_type;
-
-  return_type Get(uoffset_t i) const {
-    FLATBUFFERS_ASSERT(i < size());
-    return IndirectHelper<T>::Read(Data(), i);
-  }
-
-  return_type operator[](uoffset_t i) const { return Get(i); }
-
-  // If this is a Vector of enums, T will be its storage type, not the enum
-  // type. This function makes it convenient to retrieve value with enum
-  // type E.
-  template<typename E> E GetEnum(uoffset_t i) const {
-    return static_cast<E>(Get(i));
-  }
-
-  // If this a vector of unions, this does the cast for you. There's no check
-  // to make sure this is the right type!
-  template<typename U> const U *GetAs(uoffset_t i) const {
-    return reinterpret_cast<const U *>(Get(i));
-  }
-
-  // If this a vector of unions, this does the cast for you. There's no check
-  // to make sure this is actually a string!
-  const String *GetAsString(uoffset_t i) const {
-    return reinterpret_cast<const String *>(Get(i));
-  }
-
-  const void *GetStructFromOffset(size_t o) const {
-    return reinterpret_cast<const void *>(Data() + o);
-  }
-
-  iterator begin() { return iterator(Data(), 0); }
-  const_iterator begin() const { return const_iterator(Data(), 0); }
-
-  iterator end() { return iterator(Data(), size()); }
-  const_iterator end() const { return const_iterator(Data(), size()); }
-
-  reverse_iterator rbegin() { return reverse_iterator(end()); }
-  const_reverse_iterator rbegin() const {
-    return const_reverse_iterator(end());
-  }
-
-  reverse_iterator rend() { return reverse_iterator(begin()); }
-  const_reverse_iterator rend() const {
-    return const_reverse_iterator(begin());
-  }
-
-  const_iterator cbegin() const { return begin(); }
-
-  const_iterator cend() const { return end(); }
-
-  const_reverse_iterator crbegin() const { return rbegin(); }
-
-  const_reverse_iterator crend() const { return rend(); }
-
-  // Change elements if you have a non-const pointer to this object.
-  // Scalars only. See reflection.h, and the documentation.
-  void Mutate(uoffset_t i, const T &val) {
-    FLATBUFFERS_ASSERT(i < size());
-    WriteScalar(data() + i, val);
-  }
-
-  // Change an element of a vector of tables (or strings).
-  // "val" points to the new table/string, as you can obtain from
-  // e.g. reflection::AddFlatBuffer().
-  void MutateOffset(uoffset_t i, const uint8_t *val) {
-    FLATBUFFERS_ASSERT(i < size());
-    static_assert(sizeof(T) == sizeof(uoffset_t), "Unrelated types");
-    WriteScalar(data() + i,
-                static_cast<uoffset_t>(val - (Data() + i * sizeof(uoffset_t))));
-  }
-
-  // Get a mutable pointer to tables/strings inside this vector.
-  mutable_return_type GetMutableObject(uoffset_t i) const {
-    FLATBUFFERS_ASSERT(i < size());
-    return const_cast<mutable_return_type>(IndirectHelper<T>::Read(Data(), i));
-  }
-
-  // The raw data in little endian format. Use with care.
-  const uint8_t *Data() const {
-    return reinterpret_cast<const uint8_t *>(&length_ + 1);
-  }
-
-  uint8_t *Data() { return reinterpret_cast<uint8_t *>(&length_ + 1); }
-
-  // Similarly, but typed, much like std::vector::data
-  const T *data() const { return reinterpret_cast<const T *>(Data()); }
-  T *data() { return reinterpret_cast<T *>(Data()); }
-
-  template<typename K> return_type LookupByKey(K key) const {
-    void *search_result = std::bsearch(
-        &key, Data(), size(), IndirectHelper<T>::element_stride, KeyCompare<K>);
-
-    if (!search_result) {
-      return nullptr;  // Key not found.
-    }
-
-    const uint8_t *element = reinterpret_cast<const uint8_t *>(search_result);
-
-    return IndirectHelper<T>::Read(element, 0);
-  }
-
-  template<typename K> mutable_return_type MutableLookupByKey(K key) {
-    return const_cast<mutable_return_type>(LookupByKey(key));
-  }
-
- protected:
-  // This class is only used to access pre-existing data. Don't ever
-  // try to construct these manually.
-  Vector();
-
-  uoffset_t length_;
-
- private:
-  // This class is a pointer. Copying will therefore create an invalid object.
-  // Private and unimplemented copy constructor.
-  Vector(const Vector &);
-  Vector &operator=(const Vector &);
-
-  template<typename K> static int KeyCompare(const void *ap, const void *bp) {
-    const K *key = reinterpret_cast<const K *>(ap);
-    const uint8_t *data = reinterpret_cast<const uint8_t *>(bp);
-    auto table = IndirectHelper<T>::Read(data, 0);
-
-    // std::bsearch compares with the operands transposed, so we negate the
-    // result here.
-    return -table->KeyCompareWithValue(*key);
-  }
-};
-
-template<class U>
-FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span<U> make_span(Vector<U> &vec)
-    FLATBUFFERS_NOEXCEPT {
-  static_assert(Vector<U>::is_span_observable,
-                "wrong type U, only LE-scalar, or byte types are allowed");
-  return span<U>(vec.data(), vec.size());
-}
-
-template<class U>
-FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span<const U> make_span(
-    const Vector<U> &vec) FLATBUFFERS_NOEXCEPT {
-  static_assert(Vector<U>::is_span_observable,
-                "wrong type U, only LE-scalar, or byte types are allowed");
-  return span<const U>(vec.data(), vec.size());
-}
-
-template<class U>
-FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span<uint8_t> make_bytes_span(
-    Vector<U> &vec) FLATBUFFERS_NOEXCEPT {
-  static_assert(Vector<U>::scalar_tag::value,
-                "wrong type U, only LE-scalar, or byte types are allowed");
-  return span<uint8_t>(vec.Data(), vec.size() * sizeof(U));
-}
-
-template<class U>
-FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span<const uint8_t> make_bytes_span(
-    const Vector<U> &vec) FLATBUFFERS_NOEXCEPT {
-  static_assert(Vector<U>::scalar_tag::value,
-                "wrong type U, only LE-scalar, or byte types are allowed");
-  return span<const uint8_t>(vec.Data(), vec.size() * sizeof(U));
-}
-
-// Convenient helper functions to get a span of any vector, regardless
-// of whether it is null or not (the field is not set).
-template<class U>
-FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span<U> make_span(Vector<U> *ptr)
-    FLATBUFFERS_NOEXCEPT {
-  static_assert(Vector<U>::is_span_observable,
-                "wrong type U, only LE-scalar, or byte types are allowed");
-  return ptr ? make_span(*ptr) : span<U>();
-}
-
-template<class U>
-FLATBUFFERS_CONSTEXPR_CPP11 flatbuffers::span<const U> make_span(
-    const Vector<U> *ptr) FLATBUFFERS_NOEXCEPT {
-  static_assert(Vector<U>::is_span_observable,
-                "wrong type U, only LE-scalar, or byte types are allowed");
-  return ptr ? make_span(*ptr) : span<const U>();
-}
-
-// Represent a vector much like the template above, but in this case we
-// don't know what the element types are (used with reflection.h).
-class VectorOfAny {
- public:
-  uoffset_t size() const { return EndianScalar(length_); }
-
-  const uint8_t *Data() const {
-    return reinterpret_cast<const uint8_t *>(&length_ + 1);
-  }
-  uint8_t *Data() { return reinterpret_cast<uint8_t *>(&length_ + 1); }
-
- protected:
-  VectorOfAny();
-
-  uoffset_t length_;
-
- private:
-  VectorOfAny(const VectorOfAny &);
-  VectorOfAny &operator=(const VectorOfAny &);
-};
-
-template<typename T, typename U>
-Vector<Offset<T>> *VectorCast(Vector<Offset<U>> *ptr) {
-  static_assert(std::is_base_of<T, U>::value, "Unrelated types");
-  return reinterpret_cast<Vector<Offset<T>> *>(ptr);
-}
-
-template<typename T, typename U>
-const Vector<Offset<T>> *VectorCast(const Vector<Offset<U>> *ptr) {
-  static_assert(std::is_base_of<T, U>::value, "Unrelated types");
-  return reinterpret_cast<const Vector<Offset<T>> *>(ptr);
-}
-
-// Convenient helper function to get the length of any vector, regardless
-// of whether it is null or not (the field is not set).
-template<typename T> static inline size_t VectorLength(const Vector<T> *v) {
-  return v ? v->size() : 0;
-}
-
-}  // namespace flatbuffers
-
-#endif  // FLATBUFFERS_VERIFIER_H_

code/components/tflite-lib/third_party/flatbuffers/include/flatbuffers/verifier.h

@@ -1,304 +0,0 @@
-/*
- * Copyright 2021 Google Inc. All rights reserved.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef FLATBUFFERS_VERIFIER_H_
-#define FLATBUFFERS_VERIFIER_H_
-
-#include "flatbuffers/base.h"
-#include "flatbuffers/vector.h"
-
-namespace flatbuffers {
-
-// Helper class to verify the integrity of a FlatBuffer
-class Verifier FLATBUFFERS_FINAL_CLASS {
- public:
-  Verifier(const uint8_t *const buf, const size_t buf_len,
-           const uoffset_t _max_depth = 64,
-           const uoffset_t _max_tables = 1000000,
-           const bool _check_alignment = true)
-      : buf_(buf),
-        size_(buf_len),
-        max_depth_(_max_depth),
-        max_tables_(_max_tables),
-        check_alignment_(_check_alignment),
-        upper_bound_(0),
-        depth_(0),
-        num_tables_(0),
-        flex_reuse_tracker_(nullptr) {
-    FLATBUFFERS_ASSERT(size_ < FLATBUFFERS_MAX_BUFFER_SIZE);
-  }
-
-  // Central location where any verification failures register.
-  bool Check(const bool ok) const {
-    // clang-format off
-    #ifdef FLATBUFFERS_DEBUG_VERIFICATION_FAILURE
-      FLATBUFFERS_ASSERT(ok);
-    #endif
-    #ifdef FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE
-      if (!ok)
-        upper_bound_ = 0;
-    #endif
-    // clang-format on
-    return ok;
-  }
-
-  // Verify any range within the buffer.
-  bool Verify(const size_t elem, const size_t elem_len) const {
-    // clang-format off
-    #ifdef FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE
-      auto upper_bound = elem + elem_len;
-      if (upper_bound_ < upper_bound)
-        upper_bound_ =  upper_bound;
-    #endif
-    // clang-format on
-    return Check(elem_len < size_ && elem <= size_ - elem_len);
-  }
-
-  bool VerifyAlignment(const size_t elem, const size_t align) const {
-    return Check((elem & (align - 1)) == 0 || !check_alignment_);
-  }
-
-  // Verify a range indicated by sizeof(T).
-  template<typename T> bool Verify(const size_t elem) const {
-    return VerifyAlignment(elem, sizeof(T)) && Verify(elem, sizeof(T));
-  }
-
-  bool VerifyFromPointer(const uint8_t *const p, const size_t len) {
-    return Verify(static_cast<size_t>(p - buf_), len);
-  }
-
-  // Verify relative to a known-good base pointer.
-  bool VerifyFieldStruct(const uint8_t *const base, const voffset_t elem_off,
-                         const size_t elem_len, const size_t align) const {
-    const auto f = static_cast<size_t>(base - buf_) + elem_off;
-    return VerifyAlignment(f, align) && Verify(f, elem_len);
-  }
-
-  template<typename T>
-  bool VerifyField(const uint8_t *const base, const voffset_t elem_off,
-                   const size_t align) const {
-    const auto f = static_cast<size_t>(base - buf_) + elem_off;
-    return VerifyAlignment(f, align) && Verify(f, sizeof(T));
-  }
-
-  // Verify a pointer (may be NULL) of a table type.
-  template<typename T> bool VerifyTable(const T *const table) {
-    return !table || table->Verify(*this);
-  }
-
-  // Verify a pointer (may be NULL) of any vector type.
-  template<typename T> bool VerifyVector(const Vector<T> *const vec) const {
-    return !vec || VerifyVectorOrString(reinterpret_cast<const uint8_t *>(vec),
-                                        sizeof(T));
-  }
-
-  // Verify a pointer (may be NULL) of a vector to struct.
-  template<typename T>
-  bool VerifyVector(const Vector<const T *> *const vec) const {
-    return VerifyVector(reinterpret_cast<const Vector<T> *>(vec));
-  }
-
-  // Verify a pointer (may be NULL) to string.
-  bool VerifyString(const String *const str) const {
-    size_t end;
-    return !str || (VerifyVectorOrString(reinterpret_cast<const uint8_t *>(str),
-                                         1, &end) &&
-                    Verify(end, 1) &&           // Must have terminator
-                    Check(buf_[end] == '\0'));  // Terminating byte must be 0.
-  }
-
-  // Common code between vectors and strings.
-  bool VerifyVectorOrString(const uint8_t *const vec, const size_t elem_size,
-                            size_t *const end = nullptr) const {
-    const auto veco = static_cast<size_t>(vec - buf_);
-    // Check we can read the size field.
-    if (!Verify<uoffset_t>(veco)) return false;
-    // Check the whole array. If this is a string, the byte past the array
-    // must be 0.
-    const auto size = ReadScalar<uoffset_t>(vec);
-    const auto max_elems = FLATBUFFERS_MAX_BUFFER_SIZE / elem_size;
-    if (!Check(size < max_elems))
-      return false;  // Protect against byte_size overflowing.
-    const auto byte_size = sizeof(size) + elem_size * size;
-    if (end) *end = veco + byte_size;
-    return Verify(veco, byte_size);
-  }
-
-  // Special case for string contents, after the above has been called.
-  bool VerifyVectorOfStrings(const Vector<Offset<String>> *const vec) const {
-    if (vec) {
-      for (uoffset_t i = 0; i < vec->size(); i++) {
-        if (!VerifyString(vec->Get(i))) return false;
-      }
-    }
-    return true;
-  }
-
-  // Special case for table contents, after the above has been called.
-  template<typename T>
-  bool VerifyVectorOfTables(const Vector<Offset<T>> *const vec) {
-    if (vec) {
-      for (uoffset_t i = 0; i < vec->size(); i++) {
-        if (!vec->Get(i)->Verify(*this)) return false;
-      }
-    }
-    return true;
-  }
-
-  __supress_ubsan__("unsigned-integer-overflow") bool VerifyTableStart(
-      const uint8_t *const table) {
-    // Check the vtable offset.
-    const auto tableo = static_cast<size_t>(table - buf_);
-    if (!Verify<soffset_t>(tableo)) return false;
-    // This offset may be signed, but doing the subtraction unsigned always
-    // gives the result we want.
-    const auto vtableo =
-        tableo - static_cast<size_t>(ReadScalar<soffset_t>(table));
-    // Check the vtable size field, then check vtable fits in its entirety.
-    if (!(VerifyComplexity() && Verify<voffset_t>(vtableo) &&
-          VerifyAlignment(ReadScalar<voffset_t>(buf_ + vtableo),
-                          sizeof(voffset_t))))
-      return false;
-    const auto vsize = ReadScalar<voffset_t>(buf_ + vtableo);
-    return Check((vsize & 1) == 0) && Verify(vtableo, vsize);
-  }
-
-  template<typename T>
-  bool VerifyBufferFromStart(const char *const identifier, const size_t start) {
-    // Buffers have to be of some size to be valid. The reason it is a runtime
-    // check instead of static_assert, is that nested flatbuffers go through
-    // this call and their size is determined at runtime.
-    if (!Check(size_ >= FLATBUFFERS_MIN_BUFFER_SIZE)) return false;
-
-    // If an identifier is provided, check that we have a buffer
-    if (identifier && !Check((size_ >= 2 * sizeof(flatbuffers::uoffset_t) &&
-                              BufferHasIdentifier(buf_ + start, identifier)))) {
-      return false;
-    }
-
-    // Call T::Verify, which must be in the generated code for this type.
-    const auto o = VerifyOffset(start);
-    return Check(o != 0) &&
-           reinterpret_cast<const T *>(buf_ + start + o)->Verify(*this)
-    // clang-format off
-    #ifdef FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE
-           && GetComputedSize()
-    #endif
-        ;
-    // clang-format on
-  }
-
-  template<typename T>
-  bool VerifyNestedFlatBuffer(const Vector<uint8_t> *const buf,
-                              const char *const identifier) {
-    // An empty buffer is OK as it indicates not present.
-    if (!buf) return true;
-
-    // If there is a nested buffer, it must be greater than the min size.
-    if(!Check(buf->size() >= FLATBUFFERS_MIN_BUFFER_SIZE)) return false;
-
-    Verifier nested_verifier(buf->data(), buf->size());
-    return nested_verifier.VerifyBuffer<T>(identifier);
-  }
-
-  // Verify this whole buffer, starting with root type T.
-  template<typename T> bool VerifyBuffer() { return VerifyBuffer<T>(nullptr); }
-
-  template<typename T> bool VerifyBuffer(const char *const identifier) {
-    return VerifyBufferFromStart<T>(identifier, 0);
-  }
-
-  template<typename T>
-  bool VerifySizePrefixedBuffer(const char *const identifier) {
-    return Verify<uoffset_t>(0U) &&
-           Check(ReadScalar<uoffset_t>(buf_) == size_ - sizeof(uoffset_t)) &&
-           VerifyBufferFromStart<T>(identifier, sizeof(uoffset_t));
-  }
-
-  uoffset_t VerifyOffset(const size_t start) const {
-    if (!Verify<uoffset_t>(start)) return 0;
-    const auto o = ReadScalar<uoffset_t>(buf_ + start);
-    // May not point to itself.
-    if (!Check(o != 0)) return 0;
-    // Can't wrap around / buffers are max 2GB.
-    if (!Check(static_cast<soffset_t>(o) >= 0)) return 0;
-    // Must be inside the buffer to create a pointer from it (pointer outside
-    // buffer is UB).
-    if (!Verify(start + o, 1)) return 0;
-    return o;
-  }
-
-  uoffset_t VerifyOffset(const uint8_t *const base,
-                         const voffset_t start) const {
-    return VerifyOffset(static_cast<size_t>(base - buf_) + start);
-  }
-
-  // Called at the start of a table to increase counters measuring data
-  // structure depth and amount, and possibly bails out with false if
-  // limits set by the constructor have been hit. Needs to be balanced
-  // with EndTable().
-  bool VerifyComplexity() {
-    depth_++;
-    num_tables_++;
-    return Check(depth_ <= max_depth_ && num_tables_ <= max_tables_);
-  }
-
-  // Called at the end of a table to pop the depth count.
-  bool EndTable() {
-    depth_--;
-    return true;
-  }
-
-  // Returns the message size in bytes
-  size_t GetComputedSize() const {
-    // clang-format off
-    #ifdef FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE
-      uintptr_t size = upper_bound_;
-      // Align the size to uoffset_t
-      size = (size - 1 + sizeof(uoffset_t)) & ~(sizeof(uoffset_t) - 1);
-      return (size > size_) ?  0 : size;
-    #else
-      // Must turn on FLATBUFFERS_TRACK_VERIFIER_BUFFER_SIZE for this to work.
-      (void)upper_bound_;
-      FLATBUFFERS_ASSERT(false);
-      return 0;
-    #endif
-    // clang-format on
-  }
-
-  std::vector<uint8_t> *GetFlexReuseTracker() { return flex_reuse_tracker_; }
-
-  void SetFlexReuseTracker(std::vector<uint8_t> *const rt) {
-    flex_reuse_tracker_ = rt;
-  }
-
- private:
-  const uint8_t *buf_;
-  const size_t size_;
-  const uoffset_t max_depth_;
-  const uoffset_t max_tables_;
-  const bool check_alignment_;
-
-  mutable size_t upper_bound_;
-
-  uoffset_t depth_;
-  uoffset_t num_tables_;
-  std::vector<uint8_t> *flex_reuse_tracker_;
-};
-
-}  // namespace flatbuffers
-
-#endif  // FLATBUFFERS_VERIFIER_H_