code: def nll(
      self, sample: Tensor, dims: Tuple[int, ...] = (1, 2, 3)
  ) -> Tensor:
      if self.deterministic:
          return torch.tensor([0.0], device=self.device)
      return 0.5 * torch.sum(
          np.log(2.0 * np.pi)
          + self.log_var
          + torch.pow(sample - self.mean, 2) / self.var,
          dim=dims,
      )
  
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 4 for calling the GaussianDistribution obj's underlying member method code with example usage: obj.nll(sample, dims) and returns: unknown, torch