docstr: Load an optimizer state dict.
  
  In general we should prefer the configuration of the existing optimizer instance
  (e.g., learning rate) over that found in the state_dict. This allows us to
  resume training from a checkpoint using a new set of optimizer args.
code: def load_state_dict(self, state_dict):
      """
      Load an optimizer state dict.
  
      In general we should prefer the configuration of the existing optimizer instance
      (e.g., learning rate) over that found in the state_dict. This allows us to
      resume training from a checkpoint using a new set of optimizer args.
      """
      if (
          "loss_scaler" in state_dict
          and self.scaler is not None
          and isinstance(state_dict["loss_scaler"], float)
      ):
          self.scaler.loss_scale = state_dict["loss_scaler"]
      self.optimizer.load_state_dict(state_dict)
  
imports: import math
  from itertools import chain
  from typing import Optional
  import parlai.utils.logging
label_desc: Use image node_id 5 for calling the SafeFP16Optimizer obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types