code: def test_step(self):
      model = SimpleLinear()
      sparsifier = WeightNormSparsifier(sparsity_level=0.5)
      sparsifier.prepare(
          model, config=[{"tensor_fqn": "linear1.weight"}]
      )
      for g in sparsifier.groups:
          # Before step
          module = g["module"]
          assert (
              1.0 - module.parametrizations["weight"][0].mask.mean()
          ) == 0  # checking sparsity level is 0
      sparsifier.enable_mask_update = True
      sparsifier.step()
      self.assertAlmostEqual(
          model.linear1.parametrizations["weight"][0]
          .mask.mean()
          .item(),
          0.5,
          places=2,
      )
      for g in sparsifier.groups:
          # After step
          module = g["module"]
          assert (
              1.0 - module.parametrizations["weight"][0].mask.mean()
          ) > 0  # checking sparsity level has increased
      # Test if the mask collapses to all zeros if the weights are randomized
      iters_before_collapse = 1000
      for _ in range(iters_before_collapse):
          model.linear1.weight.data = torch.randn(
              model.linear1.weight.shape
          )
          sparsifier.step()
      for g in sparsifier.groups:
          # After step
          module = g["module"]
          assert (
              1.0 - module.parametrizations["weight"][0].mask.mean()
          ) > 0
  
imports: import itertools
  import logging
  import re
  import torch
  from torch import nn
  from torch.ao.pruning import BaseSparsifier, WeightNormSparsifier, FakeSparsity, NearlyDiagonalSparsifier
  from torch.nn.utils.parametrize import is_parametrized
  from torch.testing._internal.common_utils import TestCase
  from torch.testing._internal.common_pruning import SimpleLinear, MockSparseLinear, ImplementedSparsifier
label_desc: Use image node_id 2 for calling the TestWeightNormSparsifier obj's underlying member method code with example usage: obj.test_step() without return types