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","."]
docstr: implementation of :class:`~maze.distributions.torch_dist.TorchProbabilityDistribution` interface
          
docstr_tok: ["implementation","of",":","class",":","`","~maze.distributions.torch_dist.TorchProbabilityDistribution","`","interface"]
code: def deterministic_sample(self):
      """implementation of :class:`~maze.distributions.torch_dist.TorchProbabilityDistribution` interface"""
      return self._squash(self.dist.mean)
  
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 4 for calling the SquashedGaussianProbabilityDistribution obj's underlying member method code with example usage: obj.deterministic_sample() and returns: self