class_docstr: ## Gaussian Distribution
docstr: :param parameters: are the means and log of variances of the embedding of shape
      `[batch_size, z_channels * 2, z_height, z_height]`
code: def __init__(self, parameters: torch.Tensor):
      """
      :param parameters: are the means and log of variances of the embedding of shape
          `[batch_size, z_channels * 2, z_height, z_height]`
      """
      # Split mean and log of variance
      self.mean, log_var = torch.chunk(parameters, 2, dim=1)
      # Clamp the log of variances
      self.log_var = torch.clamp(log_var, -30.0, 20.0)
      # Calculate standard deviation
      self.std = torch.exp(0.5 * self.log_var)
  
imports: from typing import List
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 1 to create a new GaussianDistribution object  with example: obj = GaussianDistribution(parameters)