code: def test_sparsity_levels(self):
      nearliness_levels = list(range(-1, 100))
      model = nn.Sequential()
  
      p = re.compile(r"[-\.\s]")
      for nearliness in nearliness_levels:
          sparsifier = NearlyDiagonalSparsifier(nearliness=1)
          layer_name = f"{nearliness}"
          layer_name = p.sub("_", layer_name)
  
          layer = nn.Linear(32, 32, bias=False)
          layer.weight = nn.Parameter(torch.ones(32, 32))
          width, height = layer.weight.shape
          model.add_module(layer_name, layer)
          config = {
              "tensor_fqn": layer_name + ".weight",
              "nearliness": nearliness,
          }
  
          sparsifier.prepare(model, [config])
          # should raise a ValueError when nearliness arg is illegal
          if (nearliness > 0 and nearliness % 2 == 0) or (
              nearliness // 2 >= min(width, height)
          ):
              with self.assertRaises(ValueError):
                  sparsifier.step()
          else:
              sparsifier.step()
              sparsifier.squash_mask()
              model.eval()
  
              layer = getattr(model, layer_name)
              # verify that mask created corresponds to the nearliness
              self._verify_nearliness(layer.weight, nearliness)
  
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 5 for calling the TestNearlyDiagonalSparsifier obj's underlying member method code with example usage: obj.test_sparsity_levels() without return types