code: def load_model(self) -> None:
      logger.info("Loading models")
  
      checkpoint_data = self.get_checkpoint_data()
      checkpoint_path = checkpoint_data["checkpoint_path"]
      ckpt = checkpoint_data["ckpt"]
      is_zoo = checkpoint_data["is_zoo"]
      config = checkpoint_data["config"]
  
      model_checkpoint_path = None
      if checkpoint_path is not None:
          assert (
              ckpt
          ), "checkpoint should have been loaded when path is available"
          if is_model_only_checkpoint(ckpt):
              # it is model only checkpoint, then we load it here
              model_checkpoint_path = checkpoint_path
          else:
              # it is a trainer checkpoint, we pass it as a trainer param
              self.resume_from_checkpoint = checkpoint_path
  
      attributes = self.get_model_config(is_zoo, config)
      self.model = build_lightning_model(
          attributes, model_checkpoint_path
      )
      if len(self.torchmetrics_config) > 0:
          logger.warning(
              "skip self.model.build_meters since torchmetrics are provided"
          )
      else:
          self.model.build_meters(self.run_type)
  
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 8 for calling the LightningTrainer obj's underlying member method code with example usage: obj.load_model() without return types