#undef CASE

}  // namespace
}  // namespace tensorflow

namespace {
const tensorflow::AttrValue* GetAttrValue(TF_OpKernelConstruction* ctx,
                                          const char* attr_name,
                                          TF_Status* status) {
  auto* cc_ctx = reinterpret_cast<::tensorflow::OpKernelConstruction*>(ctx);
  const tensorflow::AttrValue* attr =
      ::tensorflow::AttrSlice(cc_ctx->def()).Find(attr_name);

               PatternRewriter& rewriter) const override {
    auto transpose_op = cast<TransposeOp>(op.getOperand(0).getDefiningOp());

    const auto result_type = mlir::cast<TensorType>(op.getResult(0).getType());
    const SmallVector<int64_t> new_result_shape =
        Permute<int64_t>(result_type.getShape(), kNchwToNhwcPermutation);

    const TensorType new_result_type =

// quantization API
TfLiteStatus QuantizeWeights(
    flatbuffers::FlatBufferBuilder* builder, const tflite::Model* input_model,
    const tflite::TensorType& inference_type,
    const absl::flat_hash_set<std::string>& denylisted_ops,
    const CustomOpMap& custom_op_map, int64_t minimum_elements_for_weights,
    bool disable_per_channel, bool weight_only_quantization,
    bool legacy_float_scale) {

// the values with the TOCO quantizer.
TfLiteStatus QuantizeModel(
    absl::string_view model_buffer, const tflite::TensorType &input_type,
    const tflite::TensorType &output_type,
    const tflite::TensorType &inference_type,
    const std::unordered_set<std::string> &operator_names,
    bool disable_per_channel, bool fully_quantize, std::string &output_buffer,

  void Element(size_t i, flatbuffers::ElementaryType /*type*/,
               const flatbuffers::TypeTable* /*type_table*/,
               const uint8_t* /*val*/) override {
    if (i) s += ", ";
  }
};

void ToString(const std::string& serialized_model) {
  IndentedToStringVisitor visitor(/*delimiter=*/"\n", /*indent=*/"  ");
  IterateFlatBuffer(reinterpret_cast<const uint8_t*>(serialized_model.c_str()),

// TODO(krzysd) Handle function calls
StatusOr<Operation*> ConvertOp(
    const tflite::OperatorT& op, const std::vector<Value>& vals_map,
    const std::vector<mlir::TensorType>& intermediate_types,
    Value optional_arg_marker,
    const std::vector<std::unique_ptr<tflite::OperatorCodeT>>& op_codes,
    const std::vector<std::string>& func_names,
    const std::vector<std::unique_ptr<tflite::TensorT>>& tensors, Location loc,

}

Status PropagateShapes(Graph* graph,
                       const std::map<int, InferredShape>& arg_shapes,
                       const std::vector<BackEdgeHelper::BackEdge>& back_edges,
                       ShapeRefiner* shape_refiner) {
  std::map<const Node*, const Node*> merge_to_next_iteration;
  for (const auto& e : back_edges) {
    if (e.src->IsNextIteration() && e.dst->IsMerge()) {

                        QuantizationUnit quant_op) const {
    auto [quantized_op, weight_idx] = quant_op;
    const bool is_narrow_range = true;
    const bool is_legacy_float = quant_specs_.legacy_float_scale;
    const bool is_signed = quant_specs_.IsSignedInferenceType();
    const int bit_width = quant_specs_.GetQuantizationTypeWidth();

Attribute DefaultOrNullAttr(OpBuilder& builder, const Attribute& attr) {
  if (attr) return attr;
  return builder.getStringAttr(kNullAttributeValue);
}

// Checks whether the value of a constant equals the given float, regardless
// of the tensor dimension.
bool FloatValueEquals(const Attribute& attr, const double value) {
  const auto fp_attr = mlir::dyn_cast_or_null<DenseFPElementsAttr>(attr);

absl::Status CalibrationComponent::ExportToSavedModel(
    ModuleOp module_op, absl::string_view calibration_data_dir,
    const bool force_regenerate_calibration_data,
    const absl::string_view dst_saved_model_path) {
  TF_ASSIGN_OR_RETURN(const std::string checkpoint_dir, GetLocalTmpFileName());

  // Clone ModuleOp and function aliases so changes in this pipeline won't