class_docstr: Abstract base class for preprocessing defences implemented in TensorFlow v2 that support efficient
  preprocessor-chaining.
docstr: Provide a differentiable estimate of the forward function, so that autograd can calculate gradients
  of the defence for the backward pass. If the defence is differentiable, just call `self.forward()`.
  If the defence is not differentiable and a differentiable estimate is not available, replace with
  an identity function.
  
  :param x: Dataset to be preprocessed.
  :param y: Labels to be preprocessed.
  :return: Preprocessed data.
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code: def estimate_forward(
      self, x: "tf.Tensor", y: Optional["tf.Tensor"] = None
  ) -> "tf.Tensor":
      """
      Provide a differentiable estimate of the forward function, so that autograd can calculate gradients
      of the defence for the backward pass. If the defence is differentiable, just call `self.forward()`.
      If the defence is not differentiable and a differentiable estimate is not available, replace with
      an identity function.
  
      :param x: Dataset to be preprocessed.
      :param y: Labels to be preprocessed.
      :return: Preprocessed data.
      """
      return self.forward(x, y=y)[0]
  
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 2 for calling the PreprocessorTensorFlowV2 obj's underlying member method code with example usage: obj.estimate_forward(x, y) and returns: self