code: def testVjp(self, has_aux):
      x_shape = (tf.TensorShape([10]), tf.TensorShape([1, 10]))
      y_shape = tf.TensorShape([])
      dtype = np.float32
  
      def f(a, b):
          y = tf_np.sum(tf_np.sqrt(tf_np.exp(a)) + b)
          if has_aux:
              return y, tf_np.asarray(1)
          else:
              return y
  
      rng = tf.random.Generator.from_seed(1234)
      x, dy_list = tf.nest.map_structure(
          lambda shape: uniform(rng, shape, dtype),
          [x_shape, [y_shape] * 2],
      )
      tf_x = x
      outputs = extensions.vjp(f, *x, has_aux=has_aux)
      if has_aux:
          y, vjp, aux = outputs
      else:
          y, vjp = outputs
      with tf.GradientTape(persistent=True) as tape:
          tape.watch(tf_x)
          outputs = f(*x)
          if has_aux:
              expected_y, expected_aux = outputs
              self.assertAllClose(expected_aux, aux)
          else:
              expected_y = outputs
      self.assertAllClose(expected_y, y)
      for dy in dy_list:
          expected_dx = tape.gradient(
              expected_y, tf_x, output_gradients=dy
          )
          self.assertAllClose(expected_dx, vjp(dy))
  
imports: import functools
  import itertools
  from absl import flags
  from absl.testing import parameterized
  from jax import lax
  import numpy
  import tensorflow.compat.v2
  from trax.tf_numpy import extensions
  import trax.tf_numpy.numpy
label_desc: Use image node_id 5 for calling the ExtensionsTest obj's underlying member method code with example usage: obj.testVjp(has_aux) and returns: y,  tf_np, y