code: def test_reduce_lr_monitor_with_callable(
      self, classifier_data, classifier_module
  ):
      # step should always be called with the return value from the
      # callable, 55
      _, mock_step = self.get_net_with_mock(
          classifier_data, classifier_module, monitor=lambda x: 55
      )
      score = mock_step.call_args_list[0][0][0]
      assert score == 55
  
imports: from unittest.mock import Mock
  import numpy
  import pytest
  from sklearn.base import clone
  from torch.optim import SGD
  from torch.optim.lr_scheduler import CosineAnnealingLR
  from torch.optim.lr_scheduler import ExponentialLR
  from torch.optim.lr_scheduler import LambdaLR
  from torch.optim.lr_scheduler import MultiStepLR
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.optim.lr_scheduler import StepLR
  from torch.optim.lr_scheduler import CyclicLR
  from skorch import NeuralNetClassifier
  from skorch.callbacks.lr_scheduler import WarmRestartLR, LRScheduler
label_desc: Use image node_id 3 for calling the TestReduceLROnPlateau obj's underlying member method code with example usage: obj.test_reduce_lr_monitor_with_callable(classifier_data, classifier_module) without return types