class_docstr: An extension of BaseDynamicPruningMethod for scheduled pruners
  where the pruning amount is changed externally over the
  course of the training.
code: def __init__(
      self,
      scheduled_value: _Any,
      scheduled_value_name: str,
      **kwargs: _ParamsDict,
  ):
      super().__init__()
      self.scheduled_value_name = scheduled_value_name
      setattr(self, scheduled_value_name, scheduled_value)
      self.sync_scheduled_value_post_hook_handle: _Optional[
          _hooks.RemovableHandle
      ] = 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 1 to create a new ScheduledBaseDynamicPruningMethod object from inherited base classes: BaseDynamicPruningMethod with example: obj = ScheduledBaseDynamicPruningMethod(scheduled_value, scheduled_value_name)