code: def test_pytree(self):
      def f_jax(params, x):
          # params is a dict
          return x @ params["w"] + params["b"]
  
      x = np.ones((2, 3), dtype=np.float32)
      params = dict(
          w=np.ones((3, 4), dtype=np.float32),
          b=np.ones((2, 4), dtype=np.float32),
      )
      res_jax = f_jax(params, x)
      f_tf = jax2tf.convert(f_jax)
  
      res_tf = f_tf(params, x)
      self.assertAllClose(res_jax, res_tf.numpy())
  
      restored_f, restored_model = tf_test_util.SaveAndLoadFunction(
          f_tf, input_args=(params, x), save_gradients=True
      )
      self.assertAllClose(restored_f(params, x).numpy(), res_tf.numpy())
  
      # Gradients for the converted function
      params_v = tf.nest.map_structure(tf.Variable, params)
      with tf.GradientTape() as tape:
          res = f_tf(params_v, x)
          loss = tf.reduce_sum(res)
          g_tf = tape.gradient(loss, params_v)
  
      params_v = tf.nest.map_structure(tf.Variable, params)
      with tf.GradientTape() as tape:
          res = restored_f(params_v, x)
          loss = tf.reduce_sum(res)
          g_restored_f = tape.gradient(loss, params_v)
  
      self.assertAllClose(g_tf["w"].numpy(), g_restored_f["w"].numpy())
      self.assertAllClose(g_tf["b"].numpy(), g_restored_f["b"].numpy())
  
imports: from absl.testing import absltest
  import os
  import jax
  from jax import lax
  import jax.numpy
  import numpy
  import tensorflow
  from jax.experimental import jax2tf
  from jax.experimental.jax2tf.tests import tf_test_util
  from jax._src import test_util
  from jax import config
label_desc: Use image node_id 9 for calling the SavedModelTest obj's underlying member method code with example usage: obj.test_pytree() and returns: unknown