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: Manipulate self.train, self.valid, and self.test into their final form. 
docstr_tok: ["Manipulate","self.train",",","self.valid",",","and","self.test","into","their","final","form","."]
code: def split(self, *args, **kwargs):
      """Manipulate self.train, self.valid, and self.test into their final form."""
      pass
  
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 1 for calling the DataLearningIterator obj's underlying member method code with example usage: obj.split() without return types