class_docstr: Wrapper for a PyTorch LRScheduler.
  
  This wrapper fulfills two main functions:
  
  1. Save and restore the learning rate when a trial is paused, preempted, etc.
  2. Step the learning rate scheduler at the configured frequency
     (e.g., every batch or every epoch).
code: def load_state_dict(self, state_dict: Dict[Any, Any]) -> None:
      self._scheduler.load_state_dict(state_dict)
  
file_path: determined/harness/determined/pytorch/_lr_scheduler.py
imports: import enum
  from typing import Any, Dict, List
  import torch
label_desc: Use image node_id 4 for calling the LRScheduler obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types