code: def __init__(
      self, parameters: torch.Tensor, deterministic: bool = False
  ):
      self.parameters = parameters
      self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
      self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
      self.deterministic = deterministic
      self.std = torch.exp(0.5 * self.logvar)
      self.var = torch.exp(self.logvar)
      if self.deterministic:
          self.var = self.std = torch.zeros_like(
              self.mean,
              device=self.parameters.device,
              dtype=self.parameters.dtype,
          )
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  import numpy
  import torch
  import torch.nn
  from ...utils import BaseOutput, is_torch_version
  from ...utils.torch_utils import randn_tensor
  from ..activations import get_activation
  from ..attention_processor import SpatialNorm
  from ..unet_2d_blocks import AutoencoderTinyBlock, UNetMidBlock2D, get_down_block, get_up_block
label_desc: Use image node_id 1 to create a new DiagonalGaussianDistribution object from inherited base classes: object with example: obj = DiagonalGaussianDistribution(parameters, deterministic)