code: def testMultipleActionsRaiseError(self):
      action_spec = [
          tensor_spec.BoundedTensorSpec((), tf.int32, 0, 2)
      ] * 2
      with self.assertRaisesRegexp(
          NotImplementedError,
          "action_spec can only contain a single BoundedTensorSpec",
      ):
          greedy_multi_objective_policy.GreedyMultiObjectiveNeuralPolicy(
              self._time_step_spec,
              action_spec,
              self._scalarizer,
              self._create_objective_networks(),
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from typing import Dict, List, Text, Tuple
  import numpy
  import tensorflow
  from tf_agents.bandits.multi_objective import multi_objective_scalarizer
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.bandits.networks import heteroscedastic_q_network
  from tf_agents.bandits.policies import greedy_multi_objective_neural_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.policies import utils
  from tf_agents.specs import array_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import test_utils
label_desc: Use image node_id 6 for calling the GreedyRewardPredictionPolicyTest obj's underlying member method code with example usage: obj.testMultipleActionsRaiseError() without return types