// Do not create any new function for the operations on the localhost.
    if (IsOnLocalHost(host)) continue;

    llvm::SmallVector<mlir::Type, 4> input_types;
    llvm::SmallVector<mlir::Type, 4> result_types;
    for (Value input : metadata.inputs) {
      input_types.push_back(input.getType());
    }
    for (Value result : metadata.results) {
      result_types.push_back(result.getType());
    }

  // 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_);

    auto* context = shape_refiner_->GetContext(&node);
    DataType dtype = node.input_type(idx);
    return ConvertDataTypeAndShape(dtype, context->input(idx),
                                   context->input_handle_shapes_and_types(idx),
                                   context, builder);
  }
  DataType dtype = node.properties()->input_types[idx];
  mlir::Type element_type;

  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;

// attaching a to the initializer function's type so that it is identifiable.
Location CreateInitOpLoc(MLIRContext* ctx, func::FuncOp init_func_ops) {
  const std::string init_type = GetInitializerType(init_func_ops);
  const std::string name =
      absl::StrCat(init_type, "_", init_func_ops.getName().str());
  return NameLoc::get(StringAttr::get(ctx, name));
}

void MergeInitializerFunctionOpsToMainPass::runOnOperation() {

      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();
}

absl::StatusOr<ElementsAttr> ConvertTensor(const Tensor& input_tensor,
                                           Builder* builder) {
  const auto& input_dtype = input_tensor.dtype();
  const auto& input_shape = input_tensor.shape();
  Type elt_type;
  TF_RETURN_IF_ERROR(ConvertDataType(input_dtype, *builder, &elt_type));
  SmallVector<int64_t, 4> shape;
  ConvertToMlirShape(input_shape, &shape);

    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);

    return GraphdefToSplattedMlirTranslateFunction(
        file->getBuffer(), input_arrays, input_dtypes, input_shapes,
        output_arrays, control_output_arrays, graphdef_conversion_options,
        context);
  }
  return GraphdefToMlirTranslateFunction(file->getBuffer(), input_arrays,
                                         input_dtypes, input_shapes,
                                         output_arrays, control_output_arrays,

  // Build the function type. Region arguments and extern values together
  // become the function arguments, with region arguments going first.
  auto input_types = llvm::to_vector<4>(entry.getArgumentTypes());
  for (auto input : extern_values) input_types.push_back(input.getType());

  // Terminator operands and pass through extern values (if enabled) together
  // become the function return values.