code: def test_lr_scheduler_record_batch_step(
      self, classifier_module, classifier_data
  ):
      X, y = classifier_data
      batch_size = 128
  
      scheduler = LRScheduler(
          CyclicLR,
          base_lr=1,
          max_lr=5,
          step_size_up=4,
          step_every="batch",
      )
      net = NeuralNetClassifier(
          classifier_module,
          max_epochs=1,
          lr=123.0,
          batch_size=batch_size,
          callbacks=[("scheduler", scheduler)],
      )
      net.fit(X, y)
      new_lrs = scheduler.simulate(
          net.history[-1, "train_batch_count"],
          initial_lr=123.0,
      )
      assert np.all(
          net.history[-1, "batches", :, "event_lr"] == new_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 10 for calling the TestLRCallbacks obj's underlying member method code with example usage: obj.test_lr_scheduler_record_batch_step(classifier_module, classifier_data) without return types