def METHOD_NAME(self):
    config_file = [
        pathjoin(_data_dir, "config_fl_train.json"),
        pathjoin(_data_dir, "config_fl_evaluate.json"),
        pathjoin(_data_dir, "multi_gpu_evaluate.json"),
    ]
    # initialize algo
    algo = MonaiAlgo(
        bundle_root=_data_dir,
        config_train_filename=None,
        eval_workflow=ConfigWorkflow(config_file=config_file, workflow_type="train", logging_file=_logging_file),
        config_filters_filename=pathjoin(_data_dir, "config_fl_filters.json"),
    )
    algo.initialize(extra={ExtraItems.CLIENT_NAME: "test_fl"})
    self.assertTrue(dist.get_rank() in (0, 1))
    # initialize model
    parser = ConfigParser()
    parser.read_config(
        [pathjoin(_data_dir, "config_fl_train.json"), pathjoin(_data_dir, "config_fl_evaluate.json")]
    )
    parser.parse()
    network = parser.get_parsed_content("network")
    data = ExchangeObject(weights=get_state_dict(network))
    # test evaluate
    metric_eo = algo.evaluate(data=data, extra={})
    self.assertIsInstance(metric_eo, ExchangeObject)
    metric = metric_eo.metrics
    self.assertIsInstance(metric["accuracy"], float)