class_docstr: PyTorch specific Policy class to use with RLlib.
class_docstr_tok: ["PyTorch","specific","Policy","class","to","use","with","RLlib","."]
code: def compute_actions(
      self,
      obs_batch: Union[List[TensorStructType], TensorStructType],
      state_batches: Optional[List[TensorType]] = None,
      prev_action_batch: Union[
          List[TensorStructType], TensorStructType
      ] = None,
      prev_reward_batch: Union[
          List[TensorStructType], TensorStructType
      ] = None,
      info_batch: Optional[Dict[str, list]] = None,
      episodes: Optional[List["Episode"]] = None,
      explore: Optional[bool] = None,
      timestep: Optional[int] = None,
      **kwargs,
  ) -> Tuple[TensorStructType, List[TensorType], Dict[str, TensorType]]:
      with torch.no_grad():
          seq_lens = torch.ones(len(obs_batch), dtype=torch.int32)
          input_dict = self._lazy_tensor_dict(
              {
                  SampleBatch.CUR_OBS: obs_batch,
                  "is_training": False,
              }
          )
          if prev_action_batch is not None:
              input_dict[SampleBatch.PREV_ACTIONS] = np.asarray(
                  prev_action_batch
              )
          if prev_reward_batch is not None:
              input_dict[SampleBatch.PREV_REWARDS] = np.asarray(
                  prev_reward_batch
              )
          state_batches = [
              convert_to_torch_tensor(s, self.device)
              for s in (state_batches or [])
          ]
          return self._compute_action_helper(
              input_dict, state_batches, seq_lens, explore, timestep
          )
  
imports: import copy
  import functools
  import logging
  import math
  import os
  import threading
  import time
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
  import gymnasium
  import numpy
  import tree
  import ray
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.policy.policy import Policy, PolicyState
  from ray.rllib.policy.rnn_sequencing import pad_batch_to_sequences_of_same_size
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils import NullContextManager, force_list
  from ray.rllib.utils.annotations import DeveloperAPI, override
  from ray.rllib.utils.error import ERR_MSG_TORCH_POLICY_CANNOT_SAVE_MODEL
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.metrics import DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY, NUM_AGENT_STEPS_TRAINED, NUM_GRAD_UPDATES_LIFETIME
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.spaces.space_utils import normalize_action
  from ray.rllib.utils.threading import with_lock
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
  from ray.rllib.utils.typing import AlgorithmConfigDict, GradInfoDict, ModelGradients, ModelWeights, TensorStructType, TensorType
label_desc: Use image node_id 3 for calling the TorchPolicy 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, explore, timestep) and returns: self