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: Replace the super-class iterator with ours.
code: def __iter__(self) -> Any:
      """Replace the super-class iterator with ours."""
      self._try_to_patch_pytorch_dataloader()
      iterator = ConcatLoaderIterator(self.loaders)
      # handle fault tolerant restart.
      self.on_restart(iterator)
      self._iterator = iterator
      return iterator
  
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 2 for calling the ConcatLoader obj's underlying member method code with example usage: obj.__iter__() and returns: iterator