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.
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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.
      """
      return {
          "lr": self.args.lr[0],
          "betas": eval(self.args.adamax_betas),
          "eps": self.args.adamax_eps,
          "weight_decay": self.args.weight_decay,
          "bias_correction": not self.args.no_bias_correction,
      }
  
file_path: fairseq/fairseq/optim/adamax.py
imports: import torch
  import torch.optim
  from .None import LegacyFairseqOptimizer, register_optimizer
label_desc: Use image node_id 3 for calling the FairseqAdamax obj's underlying member method code with example usage: obj.optimizer_config() and returns: dict