code: def test_train(self):
      train_configs = [
          pathjoin(_data_dir, "config_fl_train.json"),
          pathjoin(_data_dir, "multi_gpu_train.json"),
      ]
      eval_configs = [
          pathjoin(_data_dir, "config_fl_train.json"),
          pathjoin(_data_dir, "config_fl_evaluate.json"),
          pathjoin(_data_dir, "multi_gpu_evaluate.json"),
      ]
      train_workflow = ConfigWorkflow(
          config_file=train_configs,
          workflow_type="train",
          logging_file=_logging_file,
      )
      # simulate the case that this application has specific requirements for a bundle workflow
      train_workflow.add_property(
          name="loader",
          required=True,
          config_id="train#training_transforms#0",
          desc="NA",
      )
  
      # initialize algo
      algo = MonaiAlgo(
          bundle_root=_data_dir,
          train_workflow=ConfigWorkflow(
              config_file=train_configs,
              workflow_type="train",
              logging_file=_logging_file,
          ),
          eval_workflow=ConfigWorkflow(
              config_file=eval_configs,
              workflow_type="train",
              logging_file=_logging_file,
          ),
          config_filters_filename=pathjoin(
              _root_dir, "testing_data", "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(train_configs)
      parser.parse()
      network = parser.get_parsed_content("network")
      data = ExchangeObject(weights=get_state_dict(network))
      # test train
      for i in range(2):
          print(f"Testing round {i+1} of {2}...")
          # test evaluate
          metric_eo = algo.evaluate(data=data, extra={})
          self.assertIsInstance(metric_eo, ExchangeObject)
          metric = metric_eo.metrics
          self.assertIsInstance(metric["accuracy"], float)
  
          # test train
          algo.train(data=data, extra={})
          weights_eo = algo.get_weights()
          self.assertIsInstance(weights_eo, ExchangeObject)
          self.assertTrue(weights_eo.is_valid_weights())
          self.assertIsInstance(weights_eo.weights, dict)
          self.assertTrue(len(weights_eo.weights) > 0)
  
file_path: MONAI/tests/test_fl_monai_algo_dist.py
imports: from __future__ import annotations
  import os
  import unittest
  from os.path import join
  import torch.distributed
  from monai.bundle import ConfigParser, ConfigWorkflow
  from monai.fl.client.monai_algo import MonaiAlgo
  from monai.fl.utils.constants import ExtraItems
  from monai.fl.utils.exchange_object import ExchangeObject
  from monai.networks import get_state_dict
  from tests.utils import DistCall, DistTestCase, SkipIfBeforePyTorchVersion, SkipIfNoModule, skip_if_no_cuda
label_desc: Use image node_id 1 for calling the TestFLMonaiAlgo obj's underlying member method code with example usage: obj.test_train() without return types