code: def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
      log = dict()
      posterior = DiagonalGaussianDistribution(z)
      if self.sample:
          z = posterior.sample()
      else:
          z = posterior.mode()
      kl_loss = posterior.kl()
      kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
      log["kl_loss"] = kl_loss
      return z, log
  
file_path: generative-models/sgm/modules/autoencoding/regularizers/__init__.py
imports: from abc import abstractmethod
  from typing import Any, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from ....modules.distributions.distributions import DiagonalGaussianDistribution
  from .base import AbstractRegularizer
label_desc: Use image node_id 3 for calling the DiagonalGaussianRegularizer obj's underlying member method code with example usage: obj.forward(z) and returns: z,  log