class_docstr: This loader is same as CombinedLoader in PyTorch-Lightning, but concatenate sub-loaders
  instead of loading them in parallel.
  
  Parameters
  ----------
  loaders
      For example, ::
  
          {
              "train": DataLoader(train_dataset),
              "val": DataLoader(val_dataset)
          }
  
      In this example, the loader will first produce the batches from "train", then "val".
  
  mode
      Only support "min_size" for now.
docstr: Copied from CombinedLoader.
code: def _try_to_patch_pytorch_dataloader():
      """Copied from CombinedLoader."""
      from torch.utils.data.dataloader import _BaseDataLoaderIter
  
      # prevent `NotImplementedError` from PyTorch:
      # https://github.com/pytorch/pytorch/blob/v1.9.0/torch/utils/data/dataloader.py#L541
      def __getstate__patch__(*_):
          return {}
  
      _BaseDataLoaderIter.__getstate__ = __getstate__patch__
  
file_path: nni/nni/nas/oneshot/pytorch/_dataloader_legacy.py
imports: from __future__ import annotations
  from typing import Any
  from pytorch_lightning.trainer.supporters import CombinedLoader, CombinedLoaderIterator
label_desc: Use image node_id 3 for calling the ConcatLoader obj's underlying member method code with example usage: obj._try_to_patch_pytorch_dataloader() and returns: dict