code: def testActionBatchWithVariablesAndPolicyUpdate(
      self, batch_size, exploration_strategy
  ):
      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 = tf.constant(
              [[2 * k + 1, k + 1], [k + 1, 2 * k + 1]], 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, k], 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)
  
      self.evaluate(tf.compat.v1.global_variables_initializer())
  
      policy = linear_policy.LinearBanditPolicy(
          self._action_spec,
          a_list,
          b_list,
          num_samples_list,
          self._time_step_spec,
          exploration_strategy,
      )
      self.assertLen(policy.variables(), 3 * self._num_actions)
  
      new_policy = linear_policy.LinearBanditPolicy(
          self._action_spec,
          a_new_list,
          b_new_list,
          num_samples_new_list,
          self._time_step_spec,
          exploration_strategy,
      )
      self.assertLen(new_policy.variables(), 3 * self._num_actions)
  
      self.evaluate(new_policy.update(policy))
  
      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
      )
      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
  import itertools
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.policies import linear_bandit_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
label_desc: Use image node_id 16 for calling the LinearBanditPolicyTest obj's underlying member method code with example usage: obj.testActionBatchWithVariablesAndPolicyUpdate(batch_size, exploration_strategy) without return types