code: def testPolicyWithEpsilonGreedyAndActionMask(self):
      reward_net = DummyNet(self._observation_spec, self._action_spec)
      obs_spec = (
          tensor_spec.TensorSpec([2], tf.float32),
          tensor_spec.TensorSpec([3], tf.int32),
      )
      agent = neural_epsilon_greedy_agent.NeuralEpsilonGreedyAgent(
          ts.time_step_spec(obs_spec),
          self._action_spec,
          reward_network=reward_net,
          optimizer=None,
          observation_and_action_constraint_splitter=lambda x: (
              x[0],
              x[1],
          ),
          epsilon=0.1,
      )
      observations = (
          tf.constant([[1, 2], [3, 4]], dtype=tf.float32),
          tf.constant([[0, 0, 1], [0, 1, 0]], dtype=tf.int32),
      )
      time_steps = ts.restart(observations, batch_size=2)
      policy = agent.policy
      action_step = policy.action(time_steps)
      # Batch size 2.
      self.assertAllEqual([2], action_step.action.shape)
      self.evaluate(tf.compat.v1.global_variables_initializer())
      actions = self.evaluate(action_step.action)
      self.assertAllEqual(actions, [2, 1])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.bandits.agents import neural_epsilon_greedy_agent
  from tf_agents.bandits.networks import global_and_arm_feature_network
  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
label_desc: Use image node_id 3 for calling the AgentTest obj's underlying member method code with example usage: obj.testPolicyWithEpsilonGreedyAndActionMask() without return types