return errors::NotFound("No variable found at path ", variable_path);
  }

  *variable = variables_iter->second.get();
  return Status();
}

SavedModelV2Bundle* TFSavedModelAPI::GetBundle() { return &this->bundle_; }

TFSavedModelAPI::TFSavedModelAPI(const std::string& directory,
                                 SavedModelV2Bundle bundle,
                                 RevivedObjects revived_objects)
    : directory_(directory),

    status->status = tensorflow::errors::Unimplemented(
        "TFRT SavedModel implementation will be added in the future");
  } else {
    std::unique_ptr<tensorflow::TFSavedModelAPI> saved_model;
    status->status = tensorflow::TFSavedModelAPI::Load(
        dirname, absl::nullopt,
        tensorflow::down_cast<tensorflow::EagerContext*>(
            tensorflow::unwrap(ctx)),
        &saved_model);

      ImmediateExecutionContext* context,
      std::unique_ptr<TFSavedModelAPI>* out);

  ~TFSavedModelAPI() override = default;

  Status GetVariable(const std::string& variable_path, Variable** variable);

  SavedModelV2Bundle* GetBundle() override;

 private:
  TFSavedModelAPI(const std::string& directory, SavedModelV2Bundle bundle,
                  RevivedObjects revived_objects);

  TF_SavedModel* saved_model =
      TF_LoadSavedModel(model_dir.c_str(), ctx, status);
  EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

  tensorflow::TFSavedModelAPI* model_api =
      tensorflow::down_cast<tensorflow::TFSavedModelAPI*>(
          tensorflow::unwrap(saved_model));
  tensorflow::Variable* uninitialized_variable;
  ASSERT_EQ(absl::OkStatus(), model_api->GetVariable("uninitialized_variable",