code: def test_variable_and_constant_identical(self):
      expected = (
          2.0 * np.log(2.0),
          [1.0 + np.log(2.0), 1.0 + np.log(2.0)],
      )
      x = tf.constant(2.0)
      self.assertAllClose(
          expected,
          self.evaluate(
              gradient.value_and_gradient(
                  lambda a, b: a * tf.math.log(x), x, x
              )
          ),
          atol=1e-6,
          rtol=1e-6,
      )
      x = tf.Variable(2.0)
      self.evaluate(x.initializer)
      self.assertAllClose(
          expected,
          self.evaluate(
              gradient.value_and_gradient(
                  lambda a, b: a * tf.math.log(x), x, x
              )
          ),
          atol=1e-6,
          rtol=1e-6,
      )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.gradient import batch_jacobian
  from tensorflow_probability.python.math.gradient import value_and_gradient_with_auto_expansion
label_desc: Use image node_id 8 for calling the GradientTest obj's underlying member method code with example usage: obj.test_variable_and_constant_identical() without return types