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.
code: def __init__(self, loaders: dict[str, Any], mode: str = "min_size"):
      # FIXME: max_cycle will make dataloaders cycle iterators,
      # causing extra problems.
      if mode != "min_size":
          raise ValueError("Only min_size mode is supported now.")
      super().__init__(loaders, mode)
  
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 1 to create a new ConcatLoader object from inherited base classes: CombinedLoader with example: obj = ConcatLoader(loaders, mode)