code: def kl(
      self, other: Optional["GaussianDistribution"] = None
  ) -> Tensor:
      if self.deterministic:
          return torch.tensor([0.0], device=self.device)
      if other is None:
          return 0.5 * torch.sum(
              torch.pow(self.mean, 2) + self.var - 1.0 - self.log_var,
              dim=[1, 2, 3],
          )
      return 0.5 * torch.sum(
          torch.pow(self.mean - other.mean, 2) / other.var
          + self.var / other.var
          - 1.0
          - self.log_var
          + other.log_var,
          dim=[1, 2, 3],
      )
  
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 3 for calling the GaussianDistribution obj's underlying member method code with example usage: obj.kl(other) and returns: unknown, torch, unknown