class_docstr: Base class to build policies based on reward predictions.
class_docstr_tok: ["Base","class","to","build","policies","based","on","reward","predictions","."]
code: def _variables(self):
      policy_variables = self._reward_network.variables
      for c in self._constraints:
          policy_variables.append(c.variables)
      return policy_variables
  
imports: import abc
  from typing import Iterable, Optional, Text, Tuple
  import gin
  import six
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.networks import heteroscedastic_q_network
  from tf_agents.bandits.policies import constraints
  from tf_agents.bandits.specs import utils
  from tf_agents.policies import tf_policy
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
label_desc: Use image node_id 3 for calling the RewardPredictionBasePolicy obj's underlying member method code with example usage: obj._variables() and returns: policy_variables