code: def act(
      self,
      obs: rlt.FeatureData,
      possible_actions_mask: Optional[torch.Tensor] = None,
  ) -> rlt.ActorOutput:
      # pyre-fixme[35]: Target cannot be annotated.
      obs: torch.Tensor = obs.float_features
      batch_size, _ = obs.shape
  
      actions = []
      log_probs = []
      for m in self.dists:
          actions.append(m.sample((batch_size, 1)))
          log_probs.append(m.log_prob(actions[-1]).float())
  
      return rlt.ActorOutput(
          action=torch.cat(actions, dim=1),
          log_prob=torch.cat(log_probs, dim=1).sum(1, keepdim=True),
      )
  
imports: from typing import List, Optional
  import gym
  import numpy
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.parameters import CONTINUOUS_TRAINING_ACTION_RANGE
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.scorers.discrete_scorer import apply_possible_actions_mask
label_desc: Use image node_id 3 for calling the MultiDiscreteRandomPolicy obj's underlying member method code with example usage: obj.act(obs, possible_actions_mask) and returns: rlt