code: def test_prepare(self):
      model = SimpleLinear()
      sparsifier = NearlyDiagonalSparsifier(nearliness=1)
      sparsifier.prepare(model, config=None)
      for g in sparsifier.groups:
          module = g["module"]
          # Check mask exists
          assert hasattr(module.parametrizations["weight"][0], "mask")
          # Check parametrization exists and is correct
          assert is_parametrized(module, "weight")
          assert type(module.parametrizations.weight[0]) == FakeSparsity
  
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 TestNearlyDiagonalSparsifier obj's underlying member method code with example usage: obj.test_prepare() without return types