code: def forward(
      self,
      latent_inputs,
      latent_predictions,
      image_inputs,
      split="train",
  ):
      log = dict()
      loss = (latent_inputs - latent_predictions) ** 2
      log[f"{split}/latent_l2_loss"] = loss.mean().detach()
      image_reconstructions = None
      if self.perceptual_weight > 0.0:
          image_reconstructions = self.decoder.decode(
              latent_predictions
          )
          image_targets = self.decoder.decode(latent_inputs)
          perceptual_loss = self.perceptual_loss(
              image_targets.contiguous(),
              image_reconstructions.contiguous(),
          )
          loss = (
              self.latent_weight * loss.mean()
              + self.perceptual_weight * perceptual_loss.mean()
          )
          log[
              f"{split}/perceptual_loss"
          ] = perceptual_loss.mean().detach()
  
      if self.perceptual_weight_on_inputs > 0.0:
          image_reconstructions = default(
              image_reconstructions,
              self.decoder.decode(latent_predictions),
          )
          if self.scale_input_to_tgt_size:
              image_inputs = torch.nn.functional.interpolate(
                  image_inputs,
                  image_reconstructions.shape[2:],
                  mode="bicubic",
                  antialias=True,
              )
          elif self.scale_tgt_to_input_size:
              image_reconstructions = torch.nn.functional.interpolate(
                  image_reconstructions,
                  image_inputs.shape[2:],
                  mode="bicubic",
                  antialias=True,
              )
  
          perceptual_loss2 = self.perceptual_loss(
              image_inputs.contiguous(),
              image_reconstructions.contiguous(),
          )
          loss = (
              loss
              + self.perceptual_weight_on_inputs
              * perceptual_loss2.mean()
          )
          log[
              f"{split}/perceptual_loss_on_inputs"
          ] = perceptual_loss2.mean().detach()
      return loss, log
  
file_path: generative-models/sgm/modules/autoencoding/losses/lpips.py
imports: import torch
  import torch.nn
  from ....util import default, instantiate_from_config
  from ..lpips.loss.lpips import LPIPS
label_desc: Use image node_id 3 for calling the LatentLPIPS obj's underlying member method code with example usage: obj.forward(latent_inputs, latent_predictions, image_inputs, split) and returns: loss,  log