class_docstr: Abstract base class for preprocessing defences.
  
  By default, the gradient is estimated using BPDA with the identity function.
      To modify, override `estimate_gradient`
docstr: Provide an estimate of the gradients of the defence for the backward pass. If the defence is not differentiable,
  this is an estimate of the gradient, most often replacing the computation performed by the defence with the
  identity function (the default).
  
  :param x: Input data for which the gradient is estimated. First dimension is the batch size.
  :param grad: Gradient value so far.
  :return: The gradient (estimate) of the defence.
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code: def estimate_gradient(
      self, x: np.ndarray, grad: np.ndarray
  ) -> np.ndarray:  # pylint: disable=W0613,R0201
      """
      Provide an estimate of the gradients of the defence for the backward pass. If the defence is not differentiable,
      this is an estimate of the gradient, most often replacing the computation performed by the defence with the
      identity function (the default).
  
      :param x: Input data for which the gradient is estimated. First dimension is the batch size.
      :param grad: Gradient value so far.
      :return: The gradient (estimate) of the defence.
      """
      return grad
  
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 7 for calling the Preprocessor obj's underlying member method code with example usage: obj.estimate_gradient(x, grad) and returns: grad