code: def compute_gradient_error(
      x,
      x_shape,
      y,
      y_shape,
      x_init_value=None,
      delta=1e-3,
      init_targets=None,
      extra_feed_dict=None,
  ):
      """Computes the gradient error.
  
      Computes the maximum error for dy/dx between the computed Jacobian and the
      numerically estimated Jacobian.
  
      This function will modify the tensors passed in as it adds more operations
      and hence changing the consumers of the operations of the input tensors.
  
      This function adds operations to the current session. To compute the error
      using a particular device, such as a GPU, use the standard methods for
      setting a device (e.g. using with sess.graph.device() or setting a device
      function in the session constructor).
  
      Args:
        x: a tensor or list of tensors
        x_shape: the dimensions of x as a tuple or an array of ints. If x is a list,
        then this is the list of shapes.
        y: a tensor
        y_shape: the dimensions of y as a tuple or an array of ints.
        x_init_value: (optional) a numpy array of the same shape as "x"
          representing the initial value of x. If x is a list, this should be a list
          of numpy arrays.  If this is none, the function will pick a random tensor
          as the initial value.
        delta: (optional) the amount of perturbation.
        init_targets: list of targets to run to initialize model params.
        extra_feed_dict: dict that allows fixing specified tensor values
          during the Jacobian calculation.
  
      Returns:
        The maximum error in between the two Jacobians.
      """
      grad = compute_gradient(
          x,
          x_shape,
          y,
          y_shape,
          x_init_value,
          delta,
          init_targets,
          extra_feed_dict=extra_feed_dict,
      )
      return _compute_error(grad)
  
imports: import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import indexed_slices
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradients
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.util import deprecation
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 10 for calling a global function with example usage: compute_gradient_error(x, x_shape, y, y_shape, x_init_value, delta, init_targets, extra_feed_dict) and returns: _compute_error