code: def testScanGrad(self, jit_grad, jit_scan, jit_f):
      rng = np.random.RandomState(0)
  
      d = rng.randn(2)
  
      def f(c, a):
          assert a.shape == (3,)
          assert c.shape == (4,)
          b = (
              tf_np.sum(tf_np.sin(a))
              + tf_np.sum(tf_np.sin(c))
              + tf_np.sum(tf_np.sin(d))
          )
          c = tf_np.sin(c * b)
          assert (
              b.shape == ()
          )  # pylint: disable=g-explicit-bool-comparison
          return c, b
  
      if jit_f:
          f = extensions.jit(f)
  
      if jit_scan == "no_xla":
          scan = extensions.jit(extensions.scan, static_argnums=(0,))
      elif jit_scan == "xla_forced_compile":
          # TODO(b/187107596): Remove `skipTest`
          self.skipTest(
              "Taking gradients of `jit(scan, experimental_compile=True)` triggers "
              "'Support for TensorList crossing the XLA/TF boundary is not "
              "implemented' error"
          )
          # `xla_forced_compile=True` doesn't support gradients, so we use
          # `experimental_compile=True`.
          scan = extensions.jit(
              extensions.scan,
              static_argnums=(0,),
              experimental_compile=True,
          )
      else:
          scan = extensions.scan
  
      xs = tf_np.asarray(rng.randn(5, 3))
      c = tf_np.asarray(rng.randn(4))
  
      def losses(scan, c, xs):
          c, ys = scan(f, c, xs)
          return tf_np.concatenate(
              tf.nest.flatten(
                  tf.nest.map_structure(
                      lambda a: tf_np.reshape(a, [-1]), (c, ys)
                  )
              )
          )
  
      def loss(scan, c, xs):
          return tf_np.sum(losses(scan, c, xs))
  
      def grad_origin(c, xs):
          return extensions.grad(functools.partial(loss, scan))(c, xs)
  
      if jit_grad == "no_xla":
          grad_jit = extensions.jit(grad_origin)
      elif jit_grad == "xla_forced_compile":
          grad_jit = extensions.jit(
              grad_origin, xla_forced_compile=True
          )
      else:
          grad_jit = grad_origin
  
      ans = grad_jit(c, xs)
      expected = extensions.grad(
          functools.partial(loss, scan_reference)
      )(c, xs)
      self.assertDTypesEqual(expected, ans)
      self.assertAllClose(expected, ans)
  
      theoretical, numerical = tf.test.compute_gradient(
          to_tf_fn(functools.partial(losses, scan)), (c, xs)
      )
      self.assertAllClose(theoretical, numerical, atol=1e-3, rtol=3e-4)
  
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 24 for calling the ExtensionsTest obj's underlying member method code with example usage: obj.testScanGrad(jit_grad, jit_scan, jit_f) and returns: c,  b, tf_np, tf_np, extensions