code: def weighted_forward(sd_model, x, c, w, *args, **kwargs):
      try:
          # Temporarily append weights to a place accessible during loss calc
          sd_model._custom_loss_weight = w
  
          # Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
          # Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
          if not hasattr(sd_model, "_old_get_loss"):
              sd_model._old_get_loss = sd_model.get_loss
          sd_model.get_loss = MethodType(weighted_loss, sd_model)
  
          # Run the standard forward function, but with the patched 'get_loss'
          return sd_model.forward(x, c, *args, **kwargs)
      finally:
          try:
              # Delete temporary weights if appended
              del sd_model._custom_loss_weight
          except AttributeError:
              pass
  
          # If we have an old loss function, reset the loss function to the original one
          if hasattr(sd_model, "_old_get_loss"):
              sd_model.get_loss = sd_model._old_get_loss
              del sd_model._old_get_loss
  
imports: import torch
  from torch.nn.functional import silu
  from types import MethodType
  from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
  from modules.hypernetworks import hypernetwork
  from modules.shared import cmd_opts
  from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, xlmr_m18
  import ldm.modules.attention
  import ldm.modules.diffusionmodules.model
  import ldm.modules.diffusionmodules.openaimodel
  import ldm.models.diffusion.ddpm
  import ldm.models.diffusion.ddim
  import ldm.models.diffusion.plms
  import ldm.modules.encoders.modules
  import sgm.modules.attention
  import sgm.modules.diffusionmodules.model
  import sgm.modules.diffusionmodules.openaimodel
  import sgm.modules.encoders.modules
label_desc: Use image node_id 6 for calling a global function with example usage: weighted_forward(sd_model, x, c, w) and returns: sd_model