code: def build_trainer(config: DictConfig) -> Any:
      """Builder function for creating a trainer class. Trainer class name
      is picked from the config.
  
      Args:
          config (DictConfig): Configuration that will be used to create
              the trainer.
  
      Returns:
          (BaseTrainer): A trainer instance
      """
      trainer_type = config.training.trainer
      trainer_cls = registry.get_trainer_class(trainer_type)
      trainer_obj = trainer_cls(config)
  
      return trainer_obj
  
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 2 for calling a global function with example usage: build_trainer(config) and returns: trainer_obj