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.
docstr: Same as
  https://github.com/haarnoja/sac/blob/108a4229be6f040360fcca983113df9c4ac23a6a/sac/policies/gaussian_policy.py#L133
code: def _squash_correction(self, squashed_action):
      """
      Same as
      https://github.com/haarnoja/sac/blob/108a4229be6f040360fcca983113df9c4ac23a6a/sac/policies/gaussian_policy.py#L133
      """
      return (1 - squashed_action**2 + self.eps).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 4 for calling the GaussianFullyConnectedActor obj's underlying member method code with example usage: obj._squash_correction(squashed_action) and returns: unknown