code: def test_simulate_lrs_batch_step(self, policy):
      lr_sch = LRScheduler(
          policy,
          base_lr=1,
          max_lr=5,
          step_size_up=4,
          step_every="batch",
      )
      lrs = lr_sch.simulate(11, 1)
      expected = np.array([1, 2, 3, 4, 5, 4, 3, 2, 1, 2, 3])
      assert np.allclose(expected, 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 2 for calling the TestLRCallbacks obj's underlying member method code with example usage: obj.test_simulate_lrs_batch_step(policy) without return types