File input = getInput().get().getAsFile();
        InstrumentationInputType inputType = getInputType(input);
        switch (inputType) {
            case DEPENDENCY_ANALYSIS_DATA:
                doMergeAndOutputAnalysis(input, outputs);
                return;
            case ORIGINAL_ARTIFACT:

  for (auto input : llvm::zip(getOperandTypes(), getInputSpecs())) {
    Type inputType = std::get<0>(input);
    Attribute inputSpec = std::get<1>(input);
    if (!isValidQuantizationSpec(inputSpec, inputType)) {
      return emitOpError() << "has incompatible specification " << inputSpec
                           << " and input type " << inputType;
    }
  }

  for (auto result : llvm::zip(getResultTypes(), getOutputSpecs())) {

  for (auto input : llvm::zip(getOperandTypes(), getInputSpecs())) {
    Type inputType = std::get<0>(input);
    Attribute inputSpec = std::get<1>(input);
    if (!isValidQuantizationSpec(inputSpec, inputType)) {
      return emitOpError() << "has incompatible specification " << inputSpec
                           << " and input type " << inputType;
    }
  }

  for (auto result : llvm::zip(getResultTypes(), getOutputSpecs())) {

absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>>
GraphdefToMlirTranslateFunction(
    llvm::StringRef input, const std::vector<std::string>& input_arrays,
    const std::vector<std::string>& input_dtypes,
    const std::vector<std::optional<std::vector<int>>>& input_shapes,
    const std::vector<std::string>& output_arrays,
    const std::vector<std::string>& control_output_arrays,

}

Type InferReductionOpType(Value input, Value reduction_indices,
                          BoolAttr keep_dims) {
  Type input_ty = input.getType();
  Type element_ty = getElementTypeOrSelf(input_ty);

  // Output type is unranked if input type is not ranked.
  auto ranked_ty = mlir::dyn_cast<RankedTensorType>(input_ty);
  if (!ranked_ty) return UnrankedTensorType::get(element_ty);
  int64_t rank = ranked_ty.getRank();

    static {
        Map<Type, String> map = new HashMap<>();
        map.put(Type.BYTE_TYPE, "0");
        map.put(Type.SHORT_TYPE, "0");
        map.put(Type.INT_TYPE, "0");
        map.put(Type.LONG_TYPE, "0L");
        map.put(Type.FLOAT_TYPE, "0.0f");
        map.put(Type.DOUBLE_TYPE, "0.0");
        map.put(Type.CHAR_TYPE, "'\\u0000'");
        map.put(Type.BOOLEAN_TYPE, "false");

  // within the subgraph and are referenced outside the subgraph.
  llvm::SmallVector<Type> input_types =
      TypesFromValues(subgraph.FuncArguments());
  llvm::SmallVector<Type> return_types =
      TypesFromValues(subgraph.FuncOutputs());

  FunctionType function_type =
      builder.getFunctionType(input_types, return_types);

  std::string function_name = absl::StrCat("func_", subgraph.subgraph_id_);

  mlir::MLIRContext* context = tfr_module_.getContext();
  llvm::SmallVector<mlir::Type, 4> input_tys, output_tys;
  mlir::Builder builder(context);
  for (auto ty : input_dtys) {
    mlir::Type elt_ty;
    TF_RETURN_IF_ERROR(ConvertDataType(ty, builder, &elt_ty));
    mlir::TensorType mlir_ty = mlir::UnrankedTensorType::get(elt_ty);
    input_tys.push_back(mlir_ty);
  }
  for (auto ty : output_dtys) {
    mlir::Type elt_ty;

      enable_shape_inference, unconditionally_use_set_output_shapes,
      enable_soft_placement,  set_original_tf_func_name};

  auto module_or = tensorflow::GraphdefToMlirTranslateFunction(
      input, input_arrays, input_dtypes, input_shapes, output_arrays,
      control_output_arrays, options, context);
  if (!module_or.status().ok()) return nullptr;
  return std::move(module_or).value();
}

    bool use_splatted_constant, const std::vector<std::string>& extra_tf_opdefs,
    const GraphImportConfig& specs, absl::string_view debug_info_file,
    absl::string_view input_arrays, absl::string_view input_dtypes,
    absl::string_view input_shapes, absl::string_view output_arrays,
    absl::string_view control_output_arrays, llvm::SourceMgr* source_mgr,
    mlir::MLIRContext* context);