docstr: Input is either state_with_presence, or
  ServingFeatureData (in the case of sparse features)
docstr_tok: ["Input","is","either","state_with_presence",",","or","ServingFeatureData","(","in","the","case","of","sparse","features",")"]
code: def act(
      self,
      obs: Union[
          rlt.ServingFeatureData, Tuple[torch.Tensor, torch.Tensor]
      ],
      possible_actions_mask: Optional[torch.Tensor],
  ) -> rlt.ActorOutput:
      """Input is either state_with_presence, or
      ServingFeatureData (in the case of sparse features)"""
      assert isinstance(obs, tuple)
      if isinstance(obs, rlt.ServingFeatureData):
          state: rlt.ServingFeatureData = obs
      else:
          state = rlt.ServingFeatureData(
              float_features_with_presence=obs,
              id_list_features={},
              id_score_list_features={},
          )
      scores = self.scorer(state, possible_actions_mask)
      return self.sampler.sample_action(scores).cpu().detach()
  
imports: from typing import Optional, Tuple, Union
  import reagent.core.types
  import torch
  from reagent.core.fb_checker import IS_FB_ENVIRONMENT
  from reagent.core.parameters import RLParameters
  from reagent.gym.policies import Policy
  from reagent.gym.policies.samplers.discrete_sampler import GreedyActionSampler, SoftmaxActionSampler
  from reagent.gym.policies.samplers.top_k_sampler import TopKSampler
  from reagent.gym.policies.scorers.discrete_scorer import discrete_dqn_serving_scorer, parametric_dqn_serving_scorer
  from reagent.gym.policies.scorers.slate_q_scorer import slate_q_serving_scorer
  from reagent.models.actor import LOG_PROB_MAX, LOG_PROB_MIN
label_desc: Use image node_id 2 for calling the DiscreteDQNPredictorPolicy obj's underlying member method code with example usage: obj.act(obs, possible_actions_mask) and returns: self