code: def _create_policy(
      self,
      use_mask: bool,
      num_samples_list: List[tf.compat.v2.Variable],
      exploitation_coefficient: Optional[float] = 1.0,
      max_exploration_probability_hint: Optional[float] = None,
  ) -> falcon_reward_prediction_policy.FalconRewardPredictionPolicy:
      if use_mask:
  
          def split_fn(obs):
              return obs[0], obs[1]
  
          policy = falcon_reward_prediction_policy.FalconRewardPredictionPolicy(
              time_step_spec=self._time_step_with_mask_spec,
              action_spec=self._action_spec,
              reward_network=DummyNet(self._obs_spec),
              exploitation_coefficient=exploitation_coefficient,
              max_exploration_probability_hint=max_exploration_probability_hint,
              num_samples_list=num_samples_list,
              emit_policy_info=(
                  utils.InfoFields.LOG_PROBABILITY,
                  utils.InfoFields.PREDICTED_REWARDS_MEAN,
              ),
              observation_and_action_constraint_splitter=split_fn,
          )
      else:
          policy = falcon_reward_prediction_policy.FalconRewardPredictionPolicy(
              time_step_spec=self._time_step_spec,
              action_spec=self._action_spec,
              reward_network=DummyNet(self._obs_spec),
              exploitation_coefficient=exploitation_coefficient,
              max_exploration_probability_hint=max_exploration_probability_hint,
              num_samples_list=num_samples_list,
              emit_policy_info=(
                  utils.InfoFields.LOG_PROBABILITY,
                  utils.InfoFields.PREDICTED_REWARDS_MEAN,
              ),
          )
      return policy
  
imports: from typing import List, Optional, Set
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.policies import falcon_reward_prediction_policy
  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.utils import test_utils
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 4 for calling the FalconRewardPredictionPolicyTest obj's underlying member method code with example usage: obj._create_policy(use_mask, num_samples_list, exploitation_coefficient, max_exploration_probability_hint) and returns: policy, obs,  obs