code: def test_state_dict(self):
      step_count = 3
      model0 = SimpleLinear()
      sparsifier0 = ImplementedSparsifier(test=3)
      sparsifier0.prepare(model0, [{"tensor_fqn": "linear1.weight"}])
      mask = model0.linear1.parametrizations["weight"][0].mask
      mask.data = torch.arange(mask.shape[0] * mask.shape[1]).reshape(
          mask.shape
      )
      for step in range(step_count):
          sparsifier0.step()
      state_dict = sparsifier0.state_dict()
  
      # Check the expected keys in the state_dict
      assert "state" in state_dict
      assert "step_count" in state_dict["state"]["linear1.weight"]
      assert state_dict["state"]["linear1.weight"]["step_count"] == 3
      assert "groups" in state_dict
      assert "test" in state_dict["groups"][0]
      assert "tensor_fqn" in state_dict["groups"][0]
      assert state_dict["groups"][0]["tensor_fqn"] == "linear1.weight"
  
      # Check loading static_dict creates an equivalent model
      model1 = SimpleLinear()
      sparsifier1 = ImplementedSparsifier()
      sparsifier1.prepare(model1, None)
  
      assert sparsifier0.state != sparsifier1.state
  
      # Make sure the masks are different in the beginning
      for mg in sparsifier0.groups:
          if mg["tensor_fqn"] == "linear1.weight":
              mask0 = mg["module"].parametrizations.weight[0].mask
      for mg in sparsifier1.groups:
          if mg["tensor_fqn"] == "linear1.weight":
              mask1 = mg["module"].parametrizations.weight[0].mask
      self.assertNotEqual(mask0, mask1)
  
      sparsifier1.load_state_dict(state_dict)
  
      # Make sure the states are loaded, and are correct
      assert sparsifier0.state == sparsifier1.state
  
      # Make sure the masks (and all dicts) are the same after loading
      assert len(sparsifier0.groups) == len(sparsifier1.groups)
      for idx in range(len(sparsifier0.groups)):
          mg0 = sparsifier0.groups[idx]
          mg1 = sparsifier1.groups[idx]
          for key in mg0.keys():
              assert key in mg1
              if key == "module":
                  # We cannot compare modules as they are different
                  param0 = mg0[key].parametrizations.weight[0]
                  param1 = mg1[key].parametrizations.weight[0]
                  assert hasattr(param0, "mask")
                  assert hasattr(param1, "mask")
                  self.assertEqual(param0.__dict__, param1.__dict__)
              else:
                  assert mg0[key] == mg1[key]
  
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 4 for calling the TestBaseSparsifier obj's underlying member method code with example usage: obj.test_state_dict() without return types