code: def __init__(self, config: DictConfig):
      super().__init__(config)
      self.trainer = None
      self.trainer_config = self.config.trainer.params
      self.metrics_config = self.config.evaluation.get("metrics", [])
      self.torchmetrics_config = self.config.evaluation.get(
          "torchmetrics", []
      )
      self.data_module = None
      self.resume_from_checkpoint = None
      self.torchmetrics: Optional[LightningTorchMetrics] = None
  
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 1 to create a new LightningTrainer object from inherited base classes: BaseTrainer with example: obj = LightningTrainer(config)