class_docstr: A model arch similar to FullyConnectedActor, except that the last layer
  represents the parameters of a multi-dimensional Gaussian distribution.
  The action to return is sampled from the Gaussian distribution.
code: def _get_loc_and_scale_log(self, state: rlt.FeatureData):
      loc_scale = self.fc(state.float_features)
      loc = loc_scale[::, : self.action_dim]
      scale_log = loc_scale[::, self.action_dim :]
  
      if self.use_layer_norm:
          loc = self.loc_layer_norm(loc)
          scale_log = self.scale_layer_norm(scale_log)
  
      scale_log = scale_log.clamp(LOG_PROB_MIN, LOG_PROB_MAX)
      return loc, scale_log
  
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 5 for calling the GaussianFullyConnectedActor obj's underlying member method code with example usage: obj._get_loc_and_scale_log(state) and returns: loc,  scale_log