code: def testIncompatibleInputAndOutputImageShapes(self):
      depth_radius = 1
      bias = 1.59018219
      alpha = 0.117728651
      beta = 0.404427052
      input_grads = random_ops.random_uniform(
          shape=[4, 4, 4, 4],
          minval=-10000,
          maxval=10000,
          dtype=dtypes.float32,
          seed=-2033,
      )
      input_image = random_ops.random_uniform(
          shape=[4, 4, 4, 4],
          minval=-10000,
          maxval=10000,
          dtype=dtypes.float32,
          seed=-2033,
      )
      invalid_output_image = random_ops.random_uniform(
          shape=[4, 4, 4, 4, 4, 4],
          minval=-10000,
          maxval=10000,
          dtype=dtypes.float32,
          seed=-2033,
      )
      with self.assertRaises(
          (ValueError, errors_impl.InvalidArgumentError)
      ):
          self.evaluate(
              nn.lrn_grad(
                  input_grads=input_grads,
                  input_image=input_image,
                  output_image=invalid_output_image,
                  depth_radius=depth_radius,
                  bias=bias,
                  alpha=alpha,
                  beta=beta,
              )
          )
  
imports: import copy
  import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import nn
  from tensorflow.python.ops import random_ops
  import tensorflow.python.ops.nn_grad
  from tensorflow.python.platform import test
label_desc: Use image node_id 5 for calling the LRNOpTest obj's underlying member method code with example usage: obj.testIncompatibleInputAndOutputImageShapes() without return types