code: def test_works_correctly_vector_of_vars(self):
      x = tf.Variable(2, name="x", dtype=tf.float32)
      y = tf.Variable(3, name="y", dtype=tf.float32)
      self.evaluate([x.initializer, y.initializer])
  
      f = lambda z: z[0] * z[1]
      g = lambda z: z[0] ** 2 * z[1] ** 2 / 2
  
      with tf.GradientTape() as tape:
          z = tf.stack([x, y])
          fz = custom_gradient.custom_gradient(f(z), g(z), z)
      gz = tape.gradient(fz, [x, y])
      [z_, fz_, gx_, gy_] = self.evaluate([z, fz, gz[0], gz[1]])
  
      self.assertEqual(f(z_), fz_)
      self.assertEqual(g(z_), gx_)
      self.assertEqual(g(z_), gy_)
  
file_path: probability/tensorflow_probability/python/math/custom_gradient_test.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import custom_gradient
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 3 for calling the CustomGradientTest obj's underlying member method code with example usage: obj.test_works_correctly_vector_of_vars() without return types