code: def weighted_loss(sd_model, pred, target, mean=True):
      # Calculate the weight normally, but ignore the mean
      loss = sd_model._old_get_loss(pred, target, mean=False)
  
      # Check if we have weights available
      weight = getattr(sd_model, "_custom_loss_weight", None)
      if weight is not None:
          loss *= weight
  
      # Return the loss, as mean if specified
      return loss.mean() if mean else 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 5 for calling a global function with example usage: weighted_loss(sd_model, pred, target, mean) and returns: unknown