code: def _check_dataloader(dataloader):
      # Check the type of dataloader recursively.
      if isinstance(dataloader, list):
          return all([_check_dataloader(d) for d in dataloader])
      if isinstance(dataloader, dict):
          return all(
              [_check_dataloader(v) for v in dataloader.values()]
          )
      if isinstance(dataloader, torch_data.DataLoader):
          return is_traceable(dataloader)
      return True
  
imports: import logging
  import os
  import warnings
  from pathlib import Path
  from typing import Any, Dict, Union, Optional, List, Type
  import pytorch_lightning
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import torchmetrics
  import torchmetrics.classification
  import torch.utils.data
  import nni
  from nni.common.serializer import is_traceable
  from nni.nas.evaluator import MutableEvaluator
label_desc: Use image node_id 1 for calling a global function with example usage: _check_dataloader(dataloader) and returns: True, all, all, is_traceable