code: def train(args):
      args.distributed = False
      if "WORLD_SIZE" in os.environ:
          args.distributed = int(os.environ["WORLD_SIZE"]) > 1
  
      if args.distributed:
          torch.cuda.set_device(args.local_rank)
          torch.distributed.init_process_group(
              backend="nccl", init_method="env://"
          )
          args.N_gpu = torch.distributed.get_world_size()
      else:
          args.N_gpu = 1
  
      dboxes = dboxes300_coco()
      encoder = Encoder(dboxes)
      cocoGt = get_coco_ground_truth(args)
  
      ssd300 = model(args)
  
      args.learning_rate = (
          args.learning_rate * args.N_gpu * (args.batch_size / 32)
      )
      iteration = 0
      loss_func = Loss(dboxes)
  
      loss_func.cuda()
  
      optimizer = torch.optim.SGD(
          tencent_trick(ssd300),
          lr=args.learning_rate,
          momentum=args.momentum,
          weight_decay=args.weight_decay,
      )
  
      scheduler = MultiStepLR(
          optimizer=optimizer, milestones=args.multistep, gamma=0.1
      )
  
      scaler = torch.cuda.amp.GradScaler(enabled=args.fp16_mode)
  
      val_dataloader, inv_map = get_val_dataloader(args)
      train_loader = get_train_loader(args, dboxes)
  
      acc = 0
      logger = Logger(args.batch_size, args.local_rank, args.N_gpu)
  
      for epoch in range(0, args.epochs):
          logger.start_epoch()
          scheduler.step()
  
          iteration = train_loop(
              ssd300,
              loss_func,
              scaler,
              epoch,
              optimizer,
              train_loader,
              iteration,
              logger,
              args,
          )
  
          logger.end_epoch()
  
          if epoch in args.evaluation:
              acc = evaluate(
                  ssd300, val_dataloader, cocoGt, encoder, inv_map, args
              )
              if args.local_rank == 0:
                  print(
                      "Epoch {:2d}, Accuracy: {:4f} mAP".format(
                          epoch, acc
                      )
                  )
  
      return acc, logger.average_speed()
  
imports: import os
  import sys
  import time
  from argparse import ArgumentParser
  import math
  import numpy
  import time
  import torch
  from torch.optim.lr_scheduler import MultiStepLR
  import torch.utils.data.distributed
  from src.model import model, Loss
  from src.utils import dboxes300_coco, Encoder
  from src.evaluate import evaluate
  from src.train import train_loop, tencent_trick
  from src.data import *
label_desc: Use image node_id 2 for calling a global function with example usage: train(args) and returns: acc,  logger