code: def test_reduce_lr_monitor_passes_monitored_loss(
      self, classifier_data, classifier_module, mode
  ):
      X, y = classifier_data
      net = NeuralNetClassifier(
          classifier_module,
          callbacks=[
              (
                  "scheduler",
                  LRScheduler(
                      ReduceLROnPlateau, monitor="valid_loss", mode=mode
                  ),
              ),
          ],
          max_epochs=1,
      )
      net.fit(X, y)
  
      expected = net.history_[-1, "valid_loss"]
      policy = dict(net.callbacks_)["scheduler"].lr_scheduler_
      assert policy.best == pytest.approx(expected)
  
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 4 for calling the TestReduceLROnPlateau obj's underlying member method code with example usage: obj.test_reduce_lr_monitor_passes_monitored_loss(classifier_data, classifier_module, mode) without return types