class_docstr: The BaseDataScheduler is the abstract scheduler class specifically for the
  BaseDataSparsifier class. This class controls a specific hyperparameter of
  the sparsifier class and varies it across the training process (or across time).
  
  Args:
      data_sparsifier (instance of BaseDataSparsifier)
          Implemented class data sparsifier class wherein the update_mask is implemented
      schedule_param (str)
          A specific hyperparameter of the passed sparsifier that needs to be scheduled/varied
      last_epoch (int, default=-1)
          This is specifically is passed when training needs to be resumed from a particular
          point.
      verbose (bool, default=False)
          Verbosity of the BaseDataScheduler
  
  The *get_hyperparam()* function needs to be implemented by the user.
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docstr: Loads the schedulers state.
  
  Note:
      Remember to restore the state of the data_sparsifier before the scheduler.
  
  Args:
      state_dict (dict): scheduler state. Should be an object returned
          from a call to :meth:`state_dict`.
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code: def load_state_dict(self, state_dict):
      """Loads the schedulers state.
  
      Note:
          Remember to restore the state of the data_sparsifier before the scheduler.
  
      Args:
          state_dict (dict): scheduler state. Should be an object returned
              from a call to :meth:`state_dict`.
      """
      self.__dict__.update(state_dict)
  
file_path: pytorch/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py
imports: from functools import wraps
  import weakref
  import abc
  import warnings
  from ..data_sparsifier import BaseDataSparsifier
label_desc: Use image node_id 5 for calling the BaseDataScheduler obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types