class_docstr: Policy that samples actions based on the FALCON algorithm.
class_docstr_tok: ["Policy","that","samples","actions","based","on","the","FALCON","algorithm","."]
code: def num_trainable_elements(self):
      return self._num_trainable_elements
  
file_path: agents/tf_agents/bandits/policies/falcon_reward_prediction_policy.py
imports: from typing import Iterable, Optional, Sequence, Text, Tuple
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.policies import constraints
  from tf_agents.bandits.policies import reward_prediction_base_policy
  from tf_agents.distributions import shifted_categorical
  from tf_agents.policies import utils
  from tf_agents.typing import types
label_desc: Use image node_id 3 for calling the FalconRewardPredictionPolicy obj's underlying member method code with example usage: obj.num_trainable_elements() and returns: self