code: def testActionBatch(self, batch_size, actions_from_reward_layer):
      policy = neural_linucb_policy.NeuralLinUCBPolicy(
          DummyNet(self._obs_spec),
          self._encoding_dim,
          get_reward_layer(),
          actions_from_reward_layer=tf.constant(
              actions_from_reward_layer, dtype=tf.bool
          ),
          cov_matrix=self._a,
          data_vector=self._b,
          num_samples=self._num_samples_per_arm,
          epsilon_greedy=0.0,
          time_step_spec=self._time_step_spec,
      )
  
      action_step = policy.action(
          self._time_step_batch(batch_size=batch_size)
      )
      self.assertEqual(action_step.action.dtype, tf.int32)
      self.evaluate(tf.compat.v1.global_variables_initializer())
      action_fn = common.function_in_tf1()(policy.action)
      action_step = action_fn(
          self._time_step_batch(batch_size=batch_size)
      )
      actions_ = self.evaluate(action_step.action)
      self.assertAllGreaterEqual(actions_, self._action_spec.minimum)
      self.assertAllLessEqual(actions_, self._action_spec.maximum)
  
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 14 for calling the NeuralLinUCBPolicyTest obj's underlying member method code with example usage: obj.testActionBatch(batch_size, actions_from_reward_layer) without return types