code: def test_loss_print(self, iteration_log):
      log_stream = StringIO()
      log_handler = logging.StreamHandler(log_stream)
      log_handler.setLevel(logging.INFO)
      key_to_handler = "test_logging"
      key_to_print = "myLoss"
  
      # set up engine
      def _train_func(engine, batch):
          return [torch.tensor(0.0)]
  
      engine = Engine(_train_func)
  
      # set up testing handler
      logger = logging.getLogger(key_to_handler)
      logger.setLevel(logging.INFO)
      logger.addHandler(log_handler)
      stats_handler = StatsHandler(
          iteration_log=iteration_log,
          epoch_log=False,
          name=key_to_handler,
          tag_name=key_to_print,
      )
      stats_handler.attach(engine)
  
      num_iters = 3
      max_epochs = 2
      engine.run(range(num_iters), max_epochs=max_epochs)
  
      # check logging output
      output_str = log_stream.getvalue()
      log_handler.close()
      has_key_word = re.compile(f".*{key_to_print}.*")
      content_count = 0
      for line in output_str.split("\n"):
          if has_key_word.match(line):
              content_count += 1
      if iteration_log is True:
          self.assertTrue(content_count == num_iters * max_epochs)
      else:
          self.assertTrue(content_count == 2)
  
imports: from __future__ import annotations
  import logging
  import os
  import re
  import tempfile
  import unittest
  from io import StringIO
  import torch
  from ignite.engine import Engine, Events
  from parameterized import parameterized
  from monai.handlers import StatsHandler
label_desc: Use image node_id 2 for calling the TestHandlerStats obj's underlying member method code with example usage: obj.test_loss_print(iteration_log) and returns: list