code: def nll(self, sample, dims=[1, 2, 3]):
      if self.deterministic:
          return torch.Tensor([0.0])
      logtwopi = np.log(2.0 * np.pi)
      return 0.5 * torch.sum(
          logtwopi
          + self.logvar
          + torch.pow(sample - self.mean, 2) / self.var,
          dim=dims,
      )
  
imports: from dataclasses import dataclass
  from typing import Optional
  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 4 for calling the DiagonalGaussianDistribution obj's underlying member method code with example usage: obj.nll(sample, dims) and returns: unknown, torch