code: def forward(
      self,
      inputs: torch.Tensor,
      reconstructions: torch.Tensor,
      *,  # added because I changed the order here
      regularization_log: Dict[str, torch.Tensor],
      optimizer_idx: int,
      global_step: int,
      last_layer: torch.Tensor,
      split: str = "train",
      weights: Union[None, float, torch.Tensor] = None,
  ) -> Tuple[torch.Tensor, dict]:
      if self.scale_input_to_tgt_size:
          inputs = torch.nn.functional.interpolate(
              inputs,
              reconstructions.shape[2:],
              mode="bicubic",
              antialias=True,
          )
  
      if self.dims > 2:
          inputs, reconstructions = map(
              lambda x: rearrange(x, "b c t h w -> (b t) c h w"),
              (inputs, reconstructions),
          )
  
      rec_loss = torch.abs(
          inputs.contiguous() - reconstructions.contiguous()
      )
      if self.perceptual_weight > 0:
          p_loss = self.perceptual_loss(
              inputs.contiguous(), reconstructions.contiguous()
          )
          rec_loss = rec_loss + self.perceptual_weight * p_loss
  
      nll_loss, weighted_nll_loss = self.get_nll_loss(rec_loss, weights)
  
      # now the GAN part
      if optimizer_idx == 0:
          # generator update
          if (
              global_step >= self.discriminator_iter_start
              or not self.training
          ):
              logits_fake = self.discriminator(
                  reconstructions.contiguous()
              )
              g_loss = -torch.mean(logits_fake)
              if self.training:
                  d_weight = self.calculate_adaptive_weight(
                      nll_loss, g_loss, last_layer=last_layer
                  )
              else:
                  d_weight = torch.tensor(1.0)
          else:
              d_weight = torch.tensor(0.0)
              g_loss = torch.tensor(0.0, requires_grad=True)
  
          loss = (
              weighted_nll_loss + d_weight * self.disc_factor * g_loss
          )
          log = dict()
          for k in regularization_log:
              if k in self.regularization_weights:
                  loss = (
                      loss
                      + self.regularization_weights[k]
                      * regularization_log[k]
                  )
              if k in self.additional_log_keys:
                  log[f"{split}/{k}"] = (
                      regularization_log[k].detach().float().mean()
                  )
  
          log.update(
              {
                  f"{split}/loss/total": loss.clone().detach().mean(),
                  f"{split}/loss/nll": nll_loss.detach().mean(),
                  f"{split}/loss/rec": rec_loss.detach().mean(),
                  f"{split}/loss/g": g_loss.detach().mean(),
                  f"{split}/scalars/logvar": self.logvar.detach(),
                  f"{split}/scalars/d_weight": d_weight.detach(),
              }
          )
  
          return loss, log
      elif optimizer_idx == 1:
          # second pass for discriminator update
          logits_real = self.discriminator(inputs.contiguous().detach())
          logits_fake = self.discriminator(
              reconstructions.contiguous().detach()
          )
  
          if (
              global_step >= self.discriminator_iter_start
              or not self.training
          ):
              d_loss = self.disc_factor * self.disc_loss(
                  logits_real, logits_fake
              )
          else:
              d_loss = torch.tensor(0.0, requires_grad=True)
  
          log = {
              f"{split}/loss/disc": d_loss.clone().detach().mean(),
              f"{split}/logits/real": logits_real.detach().mean(),
              f"{split}/logits/fake": logits_fake.detach().mean(),
          }
          return d_loss, log
      else:
          raise NotImplementedError(
              f"Unknown optimizer_idx {optimizer_idx}"
          )
  
imports: from typing import Dict, Iterator, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  import torchvision
  from einops import rearrange
  from matplotlib import colormaps
  from matplotlib import pyplot
  from ....util import default, instantiate_from_config
  from ..lpips.loss.lpips import LPIPS
  from ..lpips.model.model import weights_init
  from ..lpips.vqperceptual import hinge_d_loss, vanilla_d_loss
label_desc: Use image node_id 6 for calling the GeneralLPIPSWithDiscriminator obj's underlying member method code with example usage: obj.forward(inputs, reconstructions) and returns: loss,  log, d_loss,  log