code: def build_lightning_optimizers(model, config):
      optimizer = build_optimizer(model, config)
  
      if config.training.lr_scheduler:
          lr_scheduler = build_scheduler(optimizer, config)
          return {
              "optimizer": optimizer,
              "lr_scheduler": {
                  "scheduler": lr_scheduler,
                  "interval": "step",
              },
          }
      else:
          return optimizer
  
imports: import logging
  import os
  import warnings
  from enum import Enum
  from typing import Any, Dict, List, Optional, Tuple, Union
  import mmf
  import pytorch_lightning
  import torch
  from mmf.common.meter import Meter
  from mmf.common.registry import registry
  from mmf.datasets.iteration_strategies import ConstantIterationStrategy, IterationStrategy, SizeProportionalIterationStrategy
  from mmf.datasets.processors.processors import Processor
  from mmf.utils.configuration import Configuration, get_global_config
  from mmf.utils.distributed import is_dist_initialized, is_main, is_xla, synchronize
  from mmf.utils.general import get_optimizer_parameters
  from omegaconf import DictConfig, OmegaConf
  from packaging import version
label_desc: Use image node_id 13 for calling a global function with example usage: build_lightning_optimizers(model, config) and returns: dict, optimizer