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 _squash(self, raw_values: torch.Tensor) -> torch.Tensor:
      squashed = ((torch.tanh(raw_values) + 1.0) / 2.0) * (
          self.high - self.low
      ) + self.low
      return tensor_clamp(squashed, self.low, self.high)
  
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 8 for calling the SquashedGaussianProbabilityDistribution obj's underlying member method code with example usage: obj._squash(raw_values) and returns: tensor_clamp