code: def test_lr_scheduler_record_epoch_step(
      self, classifier_module, classifier_data, policy, kwargs
  ):
      epochs = 3
      scheduler = LRScheduler(policy, **kwargs)
      lrs = scheduler.simulate(epochs, initial_lr=123.0)
      net = NeuralNetClassifier(
          classifier_module,
          max_epochs=epochs,
          lr=123.0,
          callbacks=[("scheduler", scheduler)],
      )
      net.fit(*classifier_data)
      assert np.all(net.history[:, "event_lr"] == lrs)
  
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 9 for calling the TestLRCallbacks obj's underlying member method code with example usage: obj.test_lr_scheduler_record_epoch_step(classifier_module, classifier_data, policy, kwargs) without return types