code: def testAction(self, policy_class):
      tf.compat.v1.set_random_seed(1)
      policy = policy_class(
          self._time_step_spec,
          self._action_spec,
          reward_network=DummyNet(self._obs_spec),
      )
      observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
      time_step = ts.restart(observations, batch_size=2)
      action_step = policy.action(time_step, seed=1)
      self.assertEqual(action_step.action.shape.as_list(), [2])
      self.assertEqual(action_step.action.dtype, tf.int32)
      # Initialize all variables
      self.evaluate(tf.compat.v1.global_variables_initializer())
      self.assertAllInSet(self.evaluate(action_step.action), [0, 1, 2])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from absl.testing import parameterized
  import numpy
  import tensorflow
  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 boltzmann_reward_prediction_policy
  from tf_agents.bandits.policies import constraints
  from tf_agents.bandits.policies import falcon_reward_prediction_policy
  from tf_agents.bandits.policies import greedy_reward_prediction_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.specs import array_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 6 for calling the RewardPredictionPoliciesTest obj's underlying member method code with example usage: obj.testAction(policy_class) without return types