for (const auto& output_array : model_flags.output_arrays()) {
    if (function_output_names_set.count(output_array) == 0) {
      return errors::InvalidArgument("output array name (", output_array,
                                     ") does not exist in the given graph");
    }
  }
  return absl::OkStatus();
}

Status ConvertSavedModelToTFLiteFlatBuffer(

    const std::vector<std::string>& output_arrays,
    const std::vector<std::string>& control_output_arrays,
    const GraphdefToMlirOptions& import_options, mlir::MLIRContext* context) {
  auto module_or = GraphdefToMlirImport(
      input, input_arrays, input_dtypes, input_shapes, output_arrays,
      control_output_arrays, import_options, context);
  if (!module_or.status().ok()) {

    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,
    const GraphdefToMlirOptions& import_options, mlir::MLIRContext* context);

ABSL_DEPRECATED(

// source into a MLIR module. If `input_mlir` is true, load from a MLIR source
// file; otherwise, load from a GraphDef.
// Setting prune_unused_nodes to true, would prune unreachable nodes if
// output_arrays is specified.
absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> LoadFromGraphdefOrMlirSource(
    const std::string& input_filename, bool input_mlir,
    bool use_splatted_constant, const std::vector<std::string>& extra_tf_opdefs,

      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();
}

static TranslateToMLIRRegistration GraphdefToMlirTranslate(

        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,

    module = tensorflow::LoadFromGraphdefOrMlirSource(
        input_file_name, input_mlir, use_splatted_constant, custom_opdefs,
        specs, debug_info_file, input_arrays, input_dtypes, input_shapes,
        output_arrays, control_output_arrays, &source_mgr, &context);
  }

  // If errors occur, the library call in the above already logged the error
  // message. So we can just return here.
  if (!module.ok()) return kTrFailure;