code: def test_lr_callback_init_policies(
      self,
      classifier_module,
      classifier_data,
      policy,
      instance,
      kwargs,
  ):
      X, y = classifier_data
      lr_policy = LRScheduler(policy, **kwargs)
      net = NeuralNetClassifier(
          classifier_module, max_epochs=2, callbacks=[lr_policy]
      )
      net.fit(X, y)
      assert any(
          list(
              map(
                  lambda x: isinstance(
                      getattr(x[1], "lr_scheduler_", None), instance
                  ),
                  net.callbacks_,
              )
          )
      )
  
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 TestLRCallbacks obj's underlying member method code with example usage: obj.test_lr_callback_init_policies(classifier_module, classifier_data, policy, instance, kwargs) without return types