class_docstr: Stationary Stochastic Bandit environment with per-arm features.
class_docstr_tok: ["Stationary","Stochastic","Bandit","environment","with","per-arm","features","."]
code: def _cascading_to_scorevector(self, chosen_items, chosen_values):
      scores = np.zeros(
          (self.batch_size, self._num_slots + 1), dtype=np.float32
      )
      r = np.arange(self.batch_size)
      scores[r, chosen_items] = chosen_values
      return scores[:, :-1]
  
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 6 for calling the RankingPyEnvironment obj's underlying member method code with example usage: obj._cascading_to_scorevector(chosen_items, chosen_values) and returns: scores