code: def testActionBatchWithVariablesAndPolicyUpdate(
      self, batch_size, actions_from_reward_layer
  ):
      a_list = []
      a_new_list = []
      b_list = []
      b_new_list = []
      num_samples_list = []
      num_samples_new_list = []
      for k in range(1, self._num_actions + 1):
          a_initial_value = (
              k
              + 1
              + 2 * k * tf.eye(self._encoding_dim, dtype=tf.float32)
          )
          a_for_one_arm = tf.compat.v2.Variable(a_initial_value)
          a_list.append(a_for_one_arm)
          b_initial_value = tf.constant(
              k * np.ones(self._encoding_dim), dtype=tf.float32
          )
          b_for_one_arm = tf.compat.v2.Variable(b_initial_value)
          b_list.append(b_for_one_arm)
          num_samples_initial_value = tf.constant([1], dtype=tf.float32)
          num_samples_for_one_arm = tf.compat.v2.Variable(
              num_samples_initial_value
          )
          num_samples_list.append(num_samples_for_one_arm)
  
          # Variables for the new policy (they differ by an offset).
          a_new_for_one_arm = tf.compat.v2.Variable(
              a_initial_value + _POLICY_VARIABLES_OFFSET
          )
          a_new_list.append(a_new_for_one_arm)
          b_new_for_one_arm = tf.compat.v2.Variable(
              b_initial_value + _POLICY_VARIABLES_OFFSET
          )
          b_new_list.append(b_new_for_one_arm)
          num_samples_for_one_arm_new = tf.compat.v2.Variable(
              num_samples_initial_value + _POLICY_VARIABLES_OFFSET
          )
          num_samples_new_list.append(num_samples_for_one_arm_new)
  
      policy = neural_linucb_policy.NeuralLinUCBPolicy(
          encoding_network=DummyNet(self._obs_spec),
          encoding_dim=self._encoding_dim,
          reward_layer=get_reward_layer(),
          actions_from_reward_layer=tf.constant(
              actions_from_reward_layer, dtype=tf.bool
          ),
          cov_matrix=a_list,
          data_vector=b_list,
          num_samples=num_samples_list,
          epsilon_greedy=0.0,
          time_step_spec=self._time_step_spec,
      )
  
      new_policy = neural_linucb_policy.NeuralLinUCBPolicy(
          encoding_network=DummyNet(self._obs_spec),
          encoding_dim=self._encoding_dim,
          reward_layer=get_reward_layer(),
          actions_from_reward_layer=tf.constant(
              actions_from_reward_layer, dtype=tf.bool
          ),
          cov_matrix=a_new_list,
          data_vector=b_new_list,
          num_samples=num_samples_new_list,
          epsilon_greedy=0.0,
          time_step_spec=self._time_step_spec,
      )
  
      action_step = policy.action(
          self._time_step_batch(batch_size=batch_size)
      )
      new_action_step = new_policy.action(
          self._time_step_batch(batch_size=batch_size)
      )
      self.assertEqual(
          action_step.action.shape, new_action_step.action.shape
      )
      self.assertEqual(
          action_step.action.dtype, new_action_step.action.dtype
      )
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
      self.evaluate(new_policy.update(policy))
  
      action_fn = common.function_in_tf1()(policy.action)
      action_step = action_fn(
          self._time_step_batch(batch_size=batch_size)
      )
      new_action_fn = common.function_in_tf1()(new_policy.action)
      new_action_step = new_action_fn(
          self._time_step_batch(batch_size=batch_size)
      )
  
      actions_, new_actions_ = self.evaluate(
          [action_step.action, new_action_step.action]
      )
      self.assertAllEqual(actions_, new_actions_)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.bandits.policies import neural_linucb_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 16 for calling the NeuralLinUCBPolicyTest obj's underlying member method code with example usage: obj.testActionBatchWithVariablesAndPolicyUpdate(batch_size, actions_from_reward_layer) without return types