code: def main_worker(gpu, ngpus_per_node, argss):
      global args
      args = argss
  
      BatchNorm = nn.BatchNorm2d
  
      criterion = nn.CrossEntropyLoss(ignore_index=args.ignore_label)
  
      model = PFENet(
          layers=args.layers,
          classes=2,
          zoom_factor=8,
          criterion=nn.CrossEntropyLoss(ignore_index=255),
          BatchNorm=BatchNorm,
          pretrained=True,
          shot=args.shot,
          ppm_scales=args.ppm_scales,
          vgg=args.vgg,
      )
  
      global logger, writer
      logger = get_logger()
      writer = SummaryWriter(args.save_path)
      logger.info("=> creating model ...")
      logger.info("Classes: {}".format(args.classes))
      logger.info(model)
      print(args)
  
      model = torch.nn.DataParallel(model.cuda())
  
      if args.weight:
          if os.path.isfile(args.weight):
              logger.info("=> loading weight '{}'".format(args.weight))
              checkpoint = torch.load(args.weight)
              model.load_state_dict(checkpoint["state_dict"])
              logger.info("=> loaded weight '{}'".format(args.weight))
          else:
              logger.info(
                  "=> no weight found at '{}'".format(args.weight)
              )
  
      value_scale = 255
      mean = [0.485, 0.456, 0.406]
      mean = [item * value_scale for item in mean]
      std = [0.229, 0.224, 0.225]
      std = [item * value_scale for item in std]
  
      assert args.split in [0, 1, 2, 3, 999]
  
      if args.resized_val:
          val_transform = transform.Compose(
              [
                  transform.Resize(size=args.val_size),
                  transform.ToTensor(),
                  transform.Normalize(mean=mean, std=std),
              ]
          )
      else:
          val_transform = transform.Compose(
              [
                  transform.test_Resize(size=args.val_size),
                  transform.ToTensor(),
                  transform.Normalize(mean=mean, std=std),
              ]
          )
      val_data = dataset.SemData(
          split=args.split,
          shot=args.shot,
          data_root=args.data_root,
          data_list=args.val_list,
          transform=val_transform,
          mode="val",
          use_coco=args.use_coco,
          use_split_coco=args.use_split_coco,
      )
      val_sampler = None
      val_loader = torch.utils.data.DataLoader(
          val_data,
          batch_size=args.batch_size_val,
          shuffle=False,
          num_workers=args.workers,
          pin_memory=True,
          sampler=val_sampler,
      )
  
      loss_val, mIoU_val, mAcc_val, allAcc_val, class_miou = validate(
          val_loader, model, criterion
      )
  
imports: import os
  import random
  import time
  import cv2
  import numpy
  import logging
  import argparse
  import torch
  import torch.backends.cudnn
  import torch.nn
  import torch.nn.functional
  import torch.nn.parallel
  import torch.optim
  import torch.utils.data
  import torch.multiprocessing
  import torch.distributed
  from tensorboardX import SummaryWriter
  from model.PFENet import PFENet
  from util import dataset
  from util import transform, config
  from util.util import AverageMeter, poly_learning_rate, intersectionAndUnionGPU
label_desc: Use image node_id 6 for calling a global function with example usage: main_worker(gpu, ngpus_per_node, argss) without return types