code: def test_content(self):
      data = [0] * 8
      test_lr = 0.1
      gamma = 0.1
  
      # set up engine
      def _train_func(engine, batch):
          pass
  
      val_engine = Engine(_train_func)
      train_engine = Engine(_train_func)
  
      @train_engine.on(Events.EPOCH_COMPLETED)
      def run_validation(engine):
          val_engine.run(data)
          val_engine.state.metrics["val_loss"] = 1
  
      # set up testing handler
      net = torch.nn.PReLU()
  
      def _reduce_lr_on_plateau():
          optimizer = torch.optim.SGD(net.parameters(), test_lr)
          lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
              optimizer, patience=1
          )
          handler = LrScheduleHandler(
              lr_scheduler,
              step_transform=lambda x: val_engine.state.metrics[
                  "val_loss"
              ],
          )
          handler.attach(train_engine)
          return handler
  
      with tempfile.TemporaryDirectory() as tempdir:
          key_to_handler = "test_log_lr"
          key_to_print = "Current learning rate"
          filename = os.path.join(tempdir, "test_lr.log")
          # test with additional logging handler
          file_saver = logging.FileHandler(filename, mode="w")
          file_saver.setLevel(logging.INFO)
          logger = logging.getLogger(key_to_handler)
          logger.setLevel(logging.INFO)
          logger.addHandler(file_saver)
  
          def _reduce_on_step():
              optimizer = torch.optim.SGD(net.parameters(), test_lr)
              lr_scheduler = torch.optim.lr_scheduler.StepLR(
                  optimizer, step_size=2, gamma=gamma
              )
              handler = LrScheduleHandler(
                  lr_scheduler, name=key_to_handler
              )
              handler.attach(train_engine)
              return handler
  
          schedulers = _reduce_lr_on_plateau(), _reduce_on_step()
  
          train_engine.run(data, max_epochs=5)
          file_saver.close()
          logger.removeHandler(file_saver)
  
          with open(filename) as f:
              output_str = f.read()
              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
              self.assertTrue(content_count > 0)
  
      for scheduler in schedulers:
          np.testing.assert_allclose(
              scheduler.lr_scheduler._last_lr[0], 0.001
          )
  
file_path: MONAI/tests/test_handler_lr_scheduler.py
imports: from __future__ import annotations
  import logging
  import os
  import re
  import tempfile
  import unittest
  import numpy
  import torch
  from ignite.engine import Engine, Events
  from monai.handlers import LrScheduleHandler
label_desc: Use image node_id 1 for calling the TestHandlerLrSchedule obj's underlying member method code with example usage: obj.test_content() and returns: handler, handler