code: def __init__(self, net: Tensor, deterministic: bool = False):
      self.net = net
      self.device = net.device
      self.deterministic = deterministic
      self.mean, log_var = torch.chunk(net, 2, dim=1)
      self.log_var = torch.clamp(log_var, -30.0, 20.0)
      if self.deterministic:
          zeros = torch.zeros_like(self.mean)
          self.var = self.std = zeros
      else:
          self.std = torch.exp(0.5 * self.log_var)
          self.var = torch.exp(self.log_var)
  
imports: import torch
  import numpy
  import torch.nn
  from torch import Tensor
  from typing import Tuple
  from typing import Optional
  from cftool.types import tensor_dict_type
  from .common import IAutoEncoder
  from .common import AutoEncoderInit
  from .common import AutoEncoderLPIPSWithDiscriminator
  from ...schemas import CustomTrainStepLoss
  from ....schema import IDLModel
  from ....constants import INPUT_KEY
  from ....constants import PREDICTIONS_KEY
label_desc: Use image node_id 1 to create a new GaussianDistribution object  with example: obj = GaussianDistribution(net, deterministic)