docstr: Act on a single observation
docstr_tok: ["Act","on","a","single","observation"]
code: def act(
      self, obs: Any, possible_actions_mask: Optional[np.ndarray] = None
  ) -> Tuple[Any, Optional[float]]:
      """Act on a single observation"""
      # preprocess and convert to batch data
      preprocessed_obs = self.obs_preprocessor(obs)
      if possible_actions_mask is not None:
          # pyre-fixme[9]: possible_actions_mask has type `Optional[ndarray]`;
          #  used as `Tensor`.
          possible_actions_mask = torch.tensor(
              possible_actions_mask, device=self.device
          )
  
      # store intermediate actor output for post_step
      # pyre-fixme[6]: For 2nd param expected `Optional[Tensor]` but got
      #  `Optional[ndarray]`.
      actor_output = self.policy.act(
          preprocessed_obs, possible_actions_mask
      )
      log_prob = actor_output.log_prob
      if log_prob is not None:
          log_prob = log_prob.cpu().squeeze(0).item()
      return self.action_extractor(actor_output), log_prob
  
file_path: ReAgent/reagent/gym/agents/agent.py
imports: from typing import Any, Dict, Optional, Tuple, Union
  import numpy
  import torch
  from reagent.gym.envs.env_wrapper import EnvWrapper
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.random_policies import make_random_policy_for_env
  from reagent.gym.types import PostEpisode, PostStep, Trajectory, Transition
label_desc: Use image node_id 4 for calling the Agent obj's underlying member method code with example usage: obj.act(obs, possible_actions_mask) and returns: self,  log_prob