code: def test_parameters_passed_correctly_to_torch_cgn(
      self, grad_clip_cls_and_mock
  ):
      grad_norm_clip_cls, cgn = grad_clip_cls_and_mock
  
      clipping = grad_norm_clip_cls(
          gradient_clip_value=55, gradient_clip_norm_type=99
      )
      named_parameters = [("p1", 1), ("p2", 2), ("p3", 3)]
      parameter_values = [p for _, p in named_parameters]
      clipping.on_grad_computed(None, named_parameters=named_parameters)
  
      # Clip norm must receive values, not (name, value) pairs.
      assert list(cgn.call_args_list[0][0][0]) == parameter_values
      assert cgn.call_args_list[0][1]["max_norm"] == 55
      assert cgn.call_args_list[0][1]["norm_type"] == 99
  
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 2 for calling the TestGradientNormClipping obj's underlying member method code with example usage: obj.test_parameters_passed_correctly_to_torch_cgn(grad_clip_cls_and_mock) without return types