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.
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code: def load_state_dict(self, state_dict, optimizer_overrides=None):
      """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.
      """
      self.optimizer.load_state_dict(state_dict)
  
      if (
          optimizer_overrides is not None
          and len(optimizer_overrides) > 0
      ):
          # override learning rate, momentum, etc. with latest values
          for group in self.param_groups:
              group.update(optimizer_overrides)
  
imports: import torch
  from fairseq import utils
  from fairseq.dataclass.utils import gen_parser_from_dataclass
  from collections import defaultdict
label_desc: Use image node_id 12 for calling the FairseqOptimizer obj's underlying member method code with example usage: obj.load_state_dict(state_dict, optimizer_overrides) without return types