class_docstr: An actor defined by a scorer and sampler.
  The scorer gives each action an score. And the sampler samples actions
  based on the action scores.
code: def forward(self, state):
      action_scores = self.scorer(state)
      return self.sampler.sample_action(
          action_scores, possible_actions_mask=None
      )
  
imports: import math
  from typing import List, Optional
  import torch
  from reagent.core import types
  from reagent.core.parameters import CONTINUOUS_TRAINING_ACTION_RANGE
  from reagent.core.tensorboardX import SummaryWriterContext
  from reagent.models.base import ModelBase
  from reagent.models.fully_connected_network import FullyConnectedNetwork
  from torch.distributions import Dirichlet
  from torch.distributions.normal import Normal
label_desc: Use image node_id 4 for calling the StochasticActor obj's underlying member method code with example usage: obj.forward(state) and returns: self