code: def testTrainedPolicy(self, accepts_per_arm_features):
      agent = self._create_agent(accepts_per_arm_features)
      # Train the policy.
      # Initialize all variables
      self.evaluate(tf.compat.v1.global_variables_initializer())
      for _ in range(80):
          self.evaluate(
              agent.train(
                  self._generate_training_experience(
                      accepts_per_arm_features
                  ),
                  None,
              ).loss
          )
  
      # Due to the large `exploitation_coefficient`, the trained policy is
      # expected to choose greedily.
      batch_size = 100
      observations = self._generate_observations(
          batch_size, accepts_per_arm_features
      )
      time_step = ts.restart(observations, batch_size)
      action_step = agent.policy.action(time_step, seed=1)
      actions = self.evaluate(action_step.action)
      p_info = self.evaluate(action_step.info)
      # Check the log probabilities in the policy info are near greedy.
      self.assertAllClose(
          p_info.log_probability, [0.0] * batch_size, atol=1e-2
      )
      # Check the chosen arms are greedy.
      self.assertAllEqual(
          actions, np.argmax(p_info.predicted_rewards_mean, axis=1)
      )
  
imports: from typing import Optional
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.agents import neural_falcon_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.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.typing import types
label_desc: Use image node_id 10 for calling the NeuralFalconAgentTest obj's underlying member method code with example usage: obj.testTrainedPolicy(accepts_per_arm_features) without return types