code: def criterion(inputs, target):
      losses = {}
      for name, x in inputs.items():
          losses[name] = nn.functional.cross_entropy(
              x, target, ignore_index=255
          )
  
      if len(losses) == 1:
          return losses["out"]
  
      return losses["out"] + 0.5 * losses["aux"]
  
file_path: vision/references/segmentation/train.py
imports: import datetime
  import os
  import time
  import warnings
  import presets
  import torch
  import torch.utils.data
  import torchvision
  import utils
  from coco_utils import get_coco
  from torch import nn
  from torch.optim.lr_scheduler import PolynomialLR
  from torchvision.transforms import functional, InterpolationMode
label_desc: Use image node_id 3 for calling a global function with example usage: criterion(inputs, target) and returns: unknown, losses