class_docstr: Abstract base class for preprocessing defences implemented in TensorFlow v2 that support efficient
  preprocessor-chaining.
code: def estimate_gradient(
      self, x: np.ndarray, grad: np.ndarray
  ) -> np.ndarray:
      import tensorflow as tf
  
      def get_gradient(x: np.ndarray, grad: np.ndarray) -> np.ndarray:
          """
          Helper function for estimate_gradient
          """
  
          with tf.GradientTape() as tape:
              x = tf.convert_to_tensor(x, dtype=config.ART_NUMPY_DTYPE)
              tape.watch(x)
              grad = tf.convert_to_tensor(
                  grad, dtype=config.ART_NUMPY_DTYPE
              )
  
              x_prime = self.estimate_forward(x)
  
          x_grad = tape.gradient(
              target=x_prime, sources=x, output_gradients=grad
          )
  
          x_grad = x_grad.numpy()
          if x_grad.shape != x.shape:
              raise ValueError(
                  f"The input shape is {x.shape} while the gradient shape is {x_grad.shape}"
              )
  
          return x_grad
  
      if x.dtype == object:
          x_grad_list = []
          for i, x_i in enumerate(x):
              x_grad_list.append(get_gradient(x=x_i, grad=grad[i]))
          x_grad = np.empty(x.shape[0], dtype=object)
          x_grad[:] = list(x_grad_list)
      elif x.shape == grad.shape:
          x_grad = get_gradient(x=x, grad=grad)
      else:
          # Special case for loss gradients
          x_grad = np.zeros_like(grad)
          for i in range(grad.shape[1]):
              x_grad[:, i, ...] = get_gradient(
                  x=x, grad=grad[:, i, ...]
              )
  
      return x_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 4 for calling the PreprocessorTensorFlowV2 obj's underlying member method code with example usage: obj.estimate_gradient(x, grad) and returns: x_grad, x_grad