code: def normal_kl(mean1, logvar1, mean2, logvar2):
      r"""
      Compute the KL divergence between two gaussians.
  
      Shapes are automatically broadcasted, so batches can be compared to
      scalars, among other use cases.
      """
      tensor = None
      for obj in (mean1, logvar1, mean2, logvar2):
          if isinstance(obj, torch.Tensor):
              tensor = obj
              break
      assert (
          tensor is not None
      ), "at least one argument must be a Tensor"
  
      # Force variances to be Tensors. Broadcasting helps convert scalars to
      # Tensors, but it does not work for torch.exp().
      logvar1, logvar2 = [
          x
          if isinstance(x, torch.Tensor)
          else torch.tensor(x).to(tensor)
          for x in (logvar1, logvar2)
      ]
  
      return 0.5 * (
          -1.0
          + logvar2
          - logvar1
          + torch.exp(logvar1 - logvar2)
          + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
      )
  
imports: import enum
  import math
  import copy
  import numpy
  import torch
  import torch.nn.functional
  import torch.nn
  from recbole.model.init import xavier_normal_initialization
  from recbole.utils.enum_type import InputType
  from recbole.model.abstract_recommender import AutoEncoderMixin, GeneralRecommender
  from recbole.model.layers import MLPLayers
  import typing
label_desc: Use image node_id 3 for calling a global function with example usage: normal_kl(mean1, logvar1, mean2, logvar2) and returns: unknown