class_docstr: Stationary Stochastic Bandit environment with per-arm features.
class_docstr_tok: ["Stationary","Stochastic","Bandit","environment","with","per-arm","features","."]
code: def batch_size(self) -> Optional[int]:
      return self._batch_size
  
imports: from typing import Callable, Optional, Sequence, Text
  import numpy
  import tensorflow
  from tf_agents.bandits.environments import bandit_py_environment
  from tf_agents.bandits.specs import utils
  from tf_agents.specs import array_spec
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
label_desc: Use image node_id 3 for calling the RankingPyEnvironment obj's underlying member method code with example usage: obj.batch_size() and returns: self