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","."]
code: def __init__(
      self,
      logits: torch.Tensor,
      action_space: spaces.Box,
      temperature: float,
  ):
      # split distribution parameters
      mean, log_std = torch.chunk(logits, chunks=2, dim=-1)
  
      # apply temperature
      log_std = log_std / temperature
  
      super().__init__(
          dist=Normal(mean, torch.exp(log_std)),
          action_space=action_space,
      )
  
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 2 to create a new DiagonalGaussianProbabilityDistribution object from inherited base classes: TorchProbabilityDistribution with example: obj = DiagonalGaussianProbabilityDistribution(logits, action_space, temperature)