code: def build_meters(run_type: str) -> List[Meter]:
      train_meter, val_meter, test_meter = None, None, None
      if "train" in run_type:
          train_meter = Meter()
          # val_meter used for validation after training loop
          val_meter = Meter()
      elif "val" in run_type or "inference" in run_type:
          val_meter = Meter()
  
      if "test" in run_type:
          test_meter = Meter()
  
      return train_meter, val_meter, test_meter
  
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 21 for calling a global function with example usage: build_meters(run_type) and returns: train_meter,  val_meter,  test_meter