class_docstr: Wrapper class on top of the TensorFlow v2 native iterators :class:`tf.data.Iterator`.
docstr: Create a data generator wrapper for TensorFlow. Supported iterators: initializable, reinitializable, feedable.
  
  :param iterator: TensorFlow Dataset.
  :param size: Total size of the dataset.
  :param batch_size: Size of the minibatches.
  :raises `TypeError`, `ValueError`: If input parameters are not valid.
code: def __init__(
      self, iterator: "tf.data.Dataset", size: int, batch_size: int
  ) -> None:
      """
      Create a data generator wrapper for TensorFlow. Supported iterators: initializable, reinitializable, feedable.
  
      :param iterator: TensorFlow Dataset.
      :param size: Total size of the dataset.
      :param batch_size: Size of the minibatches.
      :raises `TypeError`, `ValueError`: If input parameters are not valid.
      """
      # pylint: disable=E0401
      import tensorflow as tf
  
      super().__init__(size=size, batch_size=batch_size)
      self._iterator = iterator
      self._iterator_iter = iter(iterator)
  
      if not isinstance(iterator, tf.data.Dataset):
          raise TypeError("Only support object tf.data.Dataset")
  
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 1 to create a new TensorFlowV2DataGenerator object from inherited base classes: DataGenerator with example: obj = TensorFlowV2DataGenerator(iterator, size, batch_size)