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: Get all data for a selected data type
  
  Args:
      data_type (str): can be either ``'train'``, ``'test'``, ``'valid'`` or ``'all'``
  
  Returns:
       a tuple of all inputs for a data type and all expected outputs for a data type
docstr_tok: ["Get","all","data","for","a","selected","data","type","Args",":","data_type","(","str",")",":","can","be","either","``","'train","'","``",",","``","'test","'","``",",","``","'valid","'","``","or","``","'all","'","``","Returns",":","a","tuple","of","all","inputs","for","a","data","type","and","all","expected","outputs","for","a","data","type"]
code: def get_instances(
      self, data_type: str = "train"
  ) -> Tuple[tuple, tuple]:
      """Get all data for a selected data type
  
      Args:
          data_type (str): can be either ``'train'``, ``'test'``, ``'valid'`` or ``'all'``
  
      Returns:
           a tuple of all inputs for a data type and all expected outputs for a data type
      """
      data = self.data[data_type]
      return tuple(zip(*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 5 for calling the DataLearningIterator obj's underlying member method code with example usage: obj.get_instances(data_type) and returns: tuple