code: def get_optimizer(self, trainer: Trainer):
      assert (
          len(trainer.optimizers) == 1
      ), "mmf lightning_trainer supports 1 optimizer per model for now."
      optimizer = trainer.optimizers[0]
      return optimizer
  
imports: import logging
  from typing import Any, Dict, List
  import torch
  from mmf.common.meter import Meter
  from mmf.common.registry import registry
  from mmf.common.report import Report
  from mmf.common.sample import SampleList
  from mmf.utils.logger import calculate_time_left, summarize_report
  from mmf.utils.timer import Timer
  from pytorch_lightning import LightningModule, Trainer
  from pytorch_lightning.callbacks.base import Callback
label_desc: Use image node_id 8 for calling the LightningLoopCallback obj's underlying member method code with example usage: obj.get_optimizer(trainer) and returns: optimizer