code: def _load_checkpoint(self, model_pytorch, pretrained, train_handlers):
      load_path = model_pytorch if pretrained else None
      if os.path.exists(load_path):
          logger.info(f"Add Checkpoint Loader for Path: {load_path}")
  
          load_dict = {
              self.model_dict_key: f"$@{self.const.key_network()}"
          }
          if not [
              t
              for t in train_handlers
              if t.get("_target_") == CheckpointLoader.__name__
          ]:
              loader = {
                  "_target_": CheckpointLoader.__name__,
                  "load_path": load_path,
                  "load_dict": load_dict,
                  "strict": self.load_strict,
              }
              train_handlers.insert(0, loader)
  
imports: import glob
  import json
  import logging
  import os
  import subprocess
  import sys
  from typing import Dict, Optional, Sequence
  import monai.bundle
  import torch
  from monai.bundle import ConfigParser
  from monai.data import partition_dataset
  from monai.handlers import CheckpointLoader
  from monailabel.config import settings
  from monailabel.interfaces.datastore import Datastore
  from monailabel.interfaces.tasks.train import TrainTask
  from monailabel.utils.others.class_utils import unload_module
  from monailabel.utils.others.generic import device_list, name_to_device
label_desc: Use image node_id 7 for calling the BundleTrainTask obj's underlying member method code with example usage: obj._load_checkpoint(model_pytorch, pretrained, train_handlers) without return types