class_docstr: Abstract base class for preprocessing defences implemented in TensorFlow v2 that support efficient
  preprocessor-chaining.
docstr: Perform data preprocessing in TensorFlow v2 and return preprocessed data as tuple.
  
  :param x: Dataset to be preprocessed.
  :param y: Labels to be preprocessed.
  :return: Preprocessed data.
docstr_tok: ["Perform","data","preprocessing","in","TensorFlow","v2","and","return","preprocessed","data","as","tuple",".",":","param","x",":","Dataset","to","be","preprocessed",".",":","param","y",":","Labels","to","be","preprocessed",".",":","return",":","Preprocessed","data","."]
code: def forward(
      self, x: "tf.Tensor", y: Optional[Any] = None
  ) -> Tuple["tf.Tensor", Optional[Any]]:
      """
      Perform data preprocessing in TensorFlow v2 and return preprocessed data as tuple.
  
      :param x: Dataset to be preprocessed.
      :param y: Labels to be preprocessed.
      :return: Preprocessed data.
      """
      raise NotImplementedError
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import abc
  from typing import List, Optional, Tuple, Any, TYPE_CHECKING
  import numpy
  from art import config
label_desc: Use image node_id 1 for calling the PreprocessorTensorFlowV2 obj's underlying member method code with example usage: obj.forward(x, y) without return types