class_docstr: Class responsible for configuring Train dataset preprocessing.
  
  For advanced use cases, this class can be subclassed and the `configure()` method
  overriden for custom data preprocessing.
class_docstr_tok: ["Class","responsible","for","configuring","Train","dataset","preprocessing",".","For","advanced","use","cases",",","this","class","can","be","subclassed","and","the","`","configure","(",")","`","method","overriden","for","custom","data","preprocessing","."]
docstr: Legacy hook for backwards compatiblity.
  
  This will be removed in the future.
docstr_tok: ["Legacy","hook","for","backwards","compatiblity",".","This","will","be","removed","in","the","future","."]
code: def _legacy_preprocessing(
      self,
      datasets: Dict[str, Dataset],
      preprocessor: Optional[Preprocessor],
  ) -> Dict[str, Dataset]:
      """Legacy hook for backwards compatiblity.
  
      This will be removed in the future.
      """
      return datasets
  
file_path: ray/python/ray/train/_internal/data_config.py
imports: import copy
  from typing import Dict, List, Literal, Optional, Union
  import ray
  from ray.actor import ActorHandle
  from ray.data import DataIterator, Dataset, ExecutionOptions, NodeIdStr
  from ray.data._internal.execution.interfaces.execution_options import ExecutionResources
  from ray.data.preprocessor import Preprocessor
  from ray.train.constants import TRAIN_DATASET_KEY
  from ray.util.annotations import DeveloperAPI, PublicAPI
label_desc: Use image node_id 5 for calling the DataConfig obj's underlying member method code with example usage: obj._legacy_preprocessing(datasets, preprocessor) and returns: datasets