code: def testActionBatchWithMask(
      self, batch_size, actions_from_reward_layer
  ):
      obs_spec = (
          tensor_spec.TensorSpec([self._obs_dim], tf.float32),
          tensor_spec.TensorSpec([self._num_actions], tf.int32),
      )
      time_step_spec = ts.time_step_spec(obs_spec)
      policy = neural_linucb_policy.NeuralLinUCBPolicy(
          DummyNet(obs_spec[0]),
          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=0.5,
          time_step_spec=time_step_spec,
          observation_and_action_constraint_splitter=lambda x: (
              x[0],
              x[1],
          ),
      )
  
      action_fn = common.function_in_tf1()(policy.action)
      action_step = action_fn(
          self._time_step_batch_with_mask(batch_size=batch_size)
      )
      self.assertEqual(action_step.action.shape.as_list(), [batch_size])
      self.assertEqual(action_step.action.dtype, tf.int32)
      self.evaluate(tf.compat.v1.global_variables_initializer())
      actions = self.evaluate(action_step.action)
      self.assertAllEqual(actions, range(batch_size))
  
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 15 for calling the NeuralLinUCBPolicyTest obj's underlying member method code with example usage: obj.testActionBatchWithMask(batch_size, actions_from_reward_layer) without return types