}

static TranslateToMLIRRegistration GraphdefToMlirTranslate(
    "graphdef-to-mlir", "graphdef-to-mlir", GraphdefToMlirTranslateFunction);

static OwningOpRef<mlir::ModuleOp> GraphdefToSplattedMlirTranslateFunction(
    llvm::StringRef input, MLIRContext* context) {
  tensorflow::GraphdefToMlirOptions options{
      debug_info_file,        xla_compile_device_type,
      prune_unused_nodes,     convert_legacy_fed_inputs,

// Similar as the above function, but replaces all constant tensors
// with randomly generated splat values.
absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>>
GraphdefToSplattedMlirTranslateFunction(
    llvm::StringRef input, const std::vector<std::string>& input_arrays,
    const std::vector<std::string>& input_dtypes,
    const std::vector<std::vector<int>>& input_shapes,

  if (!module_or.status().ok()) {
    LOG(ERROR) << "SavedModel V1 import failed: " << module_or.status();
  }
  return module_or;
}

absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>>
GraphdefToSplattedMlirTranslateFunction(
    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,

      /*enable_shape_inference=*/false,
      /*unconditionally_use_set_output_shapes=*/true,
      /*enable_soft_placement=*/false};

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