docstr: Return a kwarg dictionary that will be used to override optimizer
  args stored in checkpoints. This allows us to load a checkpoint and
  resume training using a different set of optimizer args, e.g., with a
  different learning rate.
docstr_tok: ["Return","a","kwarg","dictionary","that","will","be","used","to","override","optimizer","args","stored","in","checkpoints",".","This","allows","us","to","load","a","checkpoint","and","resume","training","using","a","different","set","of","optimizer","args",",","e.g.",",","with","a","different","learning","rate","."]
code: def optimizer_config(self):
      """
      Return a kwarg dictionary that will be used to override optimizer
      args stored in checkpoints. This allows us to load a checkpoint and
      resume training using a different set of optimizer args, e.g., with a
      different learning rate.
      """
      raise NotImplementedError
  
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 5 for calling the FairseqOptimizer obj's underlying member method code with example usage: obj.optimizer_config() without return types