class_docstr: Diagonal Gaussian (Normal) Torch probability distribution.
  
  :param logits: The logits for both mean and standard deviation.
class_docstr_tok: ["Diagonal","Gaussian","(","Normal",")","Torch","probability","distribution",".",":","param","logits",":","The","logits","for","both","mean","and","standard","deviation","."]
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) -> torch.Tensor:
      """implementation of :class:`~maze.distributions.torch_dist.TorchProbabilityDistribution` interface"""
      return self.dist.mean
  
file_path: maze/maze/distributions/gaussian.py
imports: from typing import Sequence
  import torch
  from gym import spaces
  from torch.distributions import Normal, kl_divergence
  from maze.core.annotations import override
  from maze.distributions.torch_dist import TorchProbabilityDistribution
label_desc: Use image node_id 4 for calling the DiagonalGaussianProbabilityDistribution obj's underlying member method code with example usage: obj.deterministic_sample() and returns: self