class_docstr: Wrapper class on top of the TensorFlow v2 native iterators :class:`tf.data.Iterator`.
docstr: Provide the next batch for training in the form of a tuple `(x, y)`. The generator should loop over the data
  indefinitely.
  
  :return: A tuple containing a batch of data `(x, y)`.
  :raises `ValueError`: If the iterator has reached the end.
code: def get_batch(self) -> tuple:
      """
      Provide the next batch for training in the form of a tuple `(x, y)`. The generator should loop over the data
      indefinitely.
  
      :return: A tuple containing a batch of data `(x, y)`.
      :raises `ValueError`: If the iterator has reached the end.
      """
      # Get next batch
      x, y = next(self._iterator_iter)
      return x.numpy(), y.numpy()
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import abc
  import inspect
  import logging
  from typing import Any, Dict, Generator, Iterator, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
label_desc: Use image node_id 2 for calling the TensorFlowV2DataGenerator obj's underlying member method code with example usage: obj.get_batch() and returns: x,  y