class_docstr: Tanh-squashed diagonal Gaussian (Normal) Torch probability distribution.
  
  :param logits: the logits for both mean and standard deviation.
  :param action_space: the underlying gym.spaces action space.
class_docstr_tok: ["Tanh-squashed","diagonal","Gaussian","(","Normal",")","Torch","probability","distribution",".",":","param","logits",":","the","logits","for","both","mean","and","standard","deviation",".",":","param","action_space",":","the","underlying","gym.spaces","action","space","."]
code: def _un_squash(self, values: torch.Tensor) -> torch.Tensor:
      normed_values = (values - self.low) / (
          self.high - self.low
      ) * 2.0 - 1.0
      save_normed_values = torch.clamp(
          normed_values, -1.0 + EPSILON, 1.0 - EPSILON
      )
      un_squashed = atanh(save_normed_values)
      return un_squashed
  
file_path: maze/maze/distributions/squashed_gaussian.py
imports: from typing import Sequence
  import numpy
  import gym
  import torch
  from torch.distributions import Normal, kl_divergence
  from maze.core.annotations import override
  from maze.distributions.torch_dist import TorchProbabilityDistribution
  from maze.distributions.utils import MIN_LOG_NN_OUTPUT, MAX_LOG_NN_OUTPUT, EPSILON, tensor_clamp, atanh
  from maze.perception.perception_utils import convert_to_torch
label_desc: Use image node_id 9 for calling the SquashedGaussianProbabilityDistribution obj's underlying member method code with example usage: obj._un_squash(values) and returns: un_squashed