class_docstr: An extension of BaseDynamicPruningMethod for scheduled pruners
  where the pruning amount is changed externally over the
  course of the training.
code: def _remove_impl(
      self, module: _torch.nn.Module, fuse_pruning_mask: bool
  ) -> None:
      super()._remove_impl(module, fuse_pruning_mask)
      del module._buffers[
          self._tensor_name + "_" + self.scheduled_value_name
      ]
      if self.sync_scheduled_value_post_hook_handle is not None:
          self.sync_scheduled_value_post_hook_handle.remove()
          self.sync_scheduled_value_post_hook_handle = None
  
imports: import logging
  import types
  from typing import Any
  from typing import Dict
  from typing import NamedTuple
  from typing import Optional
  from typing import cast
  import numpy
  import torch
  import torch.nn.utils.prune
  import torch.utils.hooks
  from coremltools.optimize.torch._typing import ParamsDict
  from coremltools.optimize.torch._utils.state_dict_utils import LoadStateDictPostHook
  from coremltools.optimize.torch.pruning._utils import block2_sparsity
  from coremltools.optimize.torch.pruning._utils import structured_sparsity
  from coremltools.optimize.torch.pruning._utils import unstructured_sparsity
label_desc: Use image node_id 4 for calling the ScheduledBaseDynamicPruningMethod obj's underlying member method code with example usage: obj._remove_impl(module, fuse_pruning_mask) without return types