code: def compute_actions(
      self,
      obs_batch,
      state_batches=None,
      prev_action_batch=None,
      prev_reward_batch=None,
      info_batch=None,
      episodes=None,
      **kwargs,
  ):
      input_dict = {"obs": obs_batch}
      if prev_action_batch is not None:
          input_dict["prev_actions"] = prev_action_batch
      if prev_reward_batch is not None:
          input_dict["prev_rewards"] = prev_reward_batch
  
      return self.compute_actions_from_input_dict(
          input_dict=input_dict,
          episodes=episodes,
          state_batches=state_batches,
      )
  
file_path: ray/rllib_contrib/alpha_zero/src/rllib_alpha_zero/alpha_zero/alpha_zero_policy.py
imports: import numpy
  from rllib_alpha_zero.alpha_zero.mcts import Node, RootParentNode
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.torch_policy import TorchPolicy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
label_desc: Use image node_id 2 for calling the AlphaZeroPolicy obj's underlying member method code with example usage: obj.compute_actions(obs_batch, state_batches, prev_action_batch, prev_reward_batch, info_batch, episodes) and returns: self