code: def test_no_parameter_updates_when_norm_0(
      self, classifier_module, classifier_data
  ):
      from copy import deepcopy
      from skorch import NeuralNetClassifier
      from skorch.callbacks import GradientNormClipping
  
      net = NeuralNetClassifier(
          classifier_module,
          callbacks=[("grad_norm", GradientNormClipping(0))],
          train_split=None,
          warm_start=True,
          max_epochs=1,
      )
      net.initialize()
  
      params_before = deepcopy(list(net.module_.parameters()))
      net.fit(*classifier_data)
      params_after = net.module_.parameters()
      for p0, p1 in zip(params_before, params_after):
          p0, p1 = to_numpy(p0), to_numpy(p1)
          assert np.allclose(p0, p1)
  
file_path: skorch/skorch/tests/callbacks/test_regularization.py
imports: from unittest.mock import patch
  import numpy
  import pytest
  from skorch.utils import to_numpy
label_desc: Use image node_id 3 for calling the TestGradientNormClipping obj's underlying member method code with example usage: obj.test_no_parameter_updates_when_norm_0(classifier_module, classifier_data) without return types