code: def update_mask(self, name, data, **kwargs):
      mask = self.get_mask(name)
      mask[0] = 0
      linear_state = self.state[name]
      linear_state["step_count"] = linear_state.get("step_count", 0) + 1
  
imports: import logging
  import torch
  from torch.nn.utils.parametrize import is_parametrized
  from torch.testing._internal.common_utils import TestCase
  from typing import Tuple
  from torch import nn
  import itertools
  import math
  import copy
  from torch.ao.pruning._experimental.data_sparsifier import BaseDataSparsifier, DataNormSparsifier
  from torch.ao.pruning._experimental.data_sparsifier.quantization_utils import post_training_sparse_quantize
label_desc: Use image node_id 2 for calling the ImplementedSparsifier obj's underlying member method code with example usage: obj.update_mask(name, data) without return types