code: def _get_policy_info(
      self, batch_size: int, accepts_per_arm_features: bool
  ) -> types.NestedSpecTensorOrArray:
      dummy_log_prob = tf.ones([batch_size], dtype=tf.float32)
      num_actions = self._num_actions(accepts_per_arm_features)
      dummy_predicted_rewards = tf.ones(
          [batch_size, num_actions], dtype=tf.float32
      )
      if accepts_per_arm_features:
          dummy_chosen_arm_features = tf.ones(
              [batch_size, 3], dtype=tf.float32
          )
          return policy_utils.PerArmPolicyInfo(
              log_probability=dummy_log_prob,
              predicted_rewards_mean=dummy_predicted_rewards,
              predicted_rewards_sampled=(),
              bandit_policy_type=(),
              multiobjective_scalarized_predicted_rewards_mean=(),
              chosen_arm_features=dummy_chosen_arm_features,
          )
      else:
          return policy_utils.PolicyInfo(
              log_probability=dummy_log_prob,
              predicted_rewards_mean=dummy_predicted_rewards,
              predicted_rewards_sampled=(),
              bandit_policy_type=(),
              multiobjective_scalarized_predicted_rewards_mean=(),
          )
  
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 7 for calling the NeuralFalconAgentTest obj's underlying member method code with example usage: obj._get_policy_info(batch_size, accepts_per_arm_features) and returns: policy_utils, policy_utils