class_docstr: Dataset iterator for learning models, e. g. neural networks.
  
  Args:
      data: list of (x, y) pairs for every data type in ``'train'``, ``'valid'`` and ``'test'``
      seed: random seed for data shuffling
      shuffle: whether to shuffle data during batching
  
  Attributes:
      shuffle: whether to shuffle data during batching
      random: instance of ``Random`` initialized with a seed
docstr: Transform the data for a specific data type (e.g. ``'train'``). 
docstr_tok: ["Transform","the","data","for","a","specific","data","type","(","e.g",".","``","'train","'","``",")","."]
code: def preprocess(
      self, data: List[Tuple[Any, Any]], *args, **kwargs
  ) -> List[Tuple[Any, Any]]:
      """Transform the data for a specific data type (e.g. ``'train'``)."""
      return data
  
file_path: DeepPavlov/deeppavlov/core/data/data_learning_iterator.py
imports: from random import Random
  from typing import List, Dict, Tuple, Any, Iterator
  from deeppavlov.core.common.registry import register
label_desc: Use image node_id 2 for calling the DataLearningIterator obj's underlying member method code with example usage: obj.preprocess(data) and returns: data