code: def test_step(self):
      model = SimpleLinear()
      sparsifier = ImplementedSparsifier(test=3)
      sparsifier.enable_mask_update = True
      sparsifier.prepare(model, [{"tensor_fqn": "linear1.weight"}])
      sparsifier.step()
      assert torch.all(
          model.linear1.parametrizations.weight[0].mask[0] == 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 3 for calling the TestBaseSparsifier obj's underlying member method code with example usage: obj.test_step() without return types