code: def testNaNGradFails(self):
      @custom_gradient.custom_gradient
      def id_nan_grad(x):
          y = array_ops.identity(x)
  
          def grad_fn(dy):
              dx = np.nan * dy
              # dx = dy
              return dx
  
          return y, grad_fn
  
      def f(x):
          return id_nan_grad(x)
  
      x = constant_op.constant(
          np.random.random_sample((1, 1)), dtype=dtypes.float32
      )
      error = gradient_checker.max_error(
          *gradient_checker.compute_gradient(f, [x])
      )
      # Typical test would assert error < max_err, so assert this test would
      # raise AssertionError, since NaN is not < 1.0.
      with self.assertRaisesRegex(
          AssertionError, "nan not less than 1.0"
      ):
          self.assertLess(error, 1.0)
  
imports: import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import custom_gradient
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import sparse_ops
  import tensorflow.python.ops.nn_grad
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 14 for calling the GradientCheckerTest obj's underlying member method code with example usage: obj.testNaNGradFails() and returns: y,  grad_fn, id_nan_grad, dx