code: def configure_monitor_callbacks(self) -> List[ModelCheckpoint]:
      criteria, mode = self.monitor_criteria()
      monitor_callback = ModelCheckpoint(
          monitor=criteria,
          dirpath=get_mmf_env(key="save_dir"),
          filename="best",
          mode=mode,
          save_top_k=1,
          save_last=False,
          verbose=True,
      )
      return [monitor_callback]
  
imports: import logging
  import math
  import os
  from typing import Any, Dict, List, Optional
  import omegaconf
  from mmf.common.registry import registry
  from mmf.datasets.lightning_multi_datamodule import LightningMultiDataModule
  from mmf.modules.metrics import Metrics
  from mmf.trainers.base_trainer import BaseTrainer
  from mmf.trainers.lightning_core.loop_callback import LightningLoopCallback
  from mmf.trainers.lightning_core.loop_callback_with_torchmetrics import LightningTorchMetricsCallback
  from mmf.trainers.lightning_core.torchmetric import LightningTorchMetrics
  from mmf.utils.build import build_lightning_model
  from mmf.utils.checkpoint import get_ckpt_from_path, get_ckpt_path_from_folder, get_config_from_folder_or_ckpt
  from mmf.utils.checkpoint_updater import is_model_only_checkpoint
  from mmf.utils.configuration import get_mmf_env
  from mmf.utils.download import download_pretrained_model
  from mmf.utils.file_io import PathManager
  from mmf.utils.general import get_max_updates, print_model_parameters
  from mmf.utils.logger import setup_output_folder, TensorboardLogger
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import seed_everything, Trainer
  from pytorch_lightning.callbacks import ModelCheckpoint
label_desc: Use image node_id 18 for calling the LightningTrainer obj's underlying member method code with example usage: obj.configure_monitor_callbacks() and returns: list