code: def weighted_loss(args, output, target, epoch=0):
      mask_output, salt_output = output
      mask_target, salt_target = target
  
      lovasz_loss = lovasz_hinge(mask_output, mask_target)
      focal_loss = focal_loss2d(mask_output, mask_target)
  
      focal_weight = 0.2
  
      if salt_output is not None and args.train_cls:
          salt_loss = F.binary_cross_entropy_with_logits(
              salt_output, salt_target
          )
          return (
              salt_loss,
              focal_loss.item(),
              lovasz_loss.item(),
              salt_loss.item(),
              lovasz_loss.item() + focal_loss.item() * focal_weight,
          )
  
      return (
          lovasz_loss + focal_loss * focal_weight,
          focal_loss.item(),
          lovasz_loss.item(),
          0.0,
          lovasz_loss.item() + focal_loss.item() * focal_weight,
      )
  
imports: import os
  import argparse
  import time
  import torch
  import torch.nn
  import torch.optim
  import torch.nn.functional
  from torch.optim.lr_scheduler import CosineAnnealingLR, ReduceLROnPlateau
  from loader import get_train_loaders, add_depth_channel
  from models import UNetResNetV4, UNetResNetV5, UNetResNetV6
  from lovasz_losses import lovasz_hinge
  from focal_loss import FocalLoss2d
  from postprocessing import binarize, crop_image, resize_image
  from metrics import intersection_over_union, intersection_over_union_thresholds
  import settings
label_desc: Use image node_id 1 for calling a global function with example usage: weighted_loss(args, output, target, epoch) and returns: unknown,  focal_loss,  lovasz_loss,  int,  unknown, salt_loss,  focal_loss,  lovasz_loss,  salt_loss,  unknown