code: def train(rank, args, model, device, dataloader_kwargs):
      torch.manual_seed(args.seed + rank)
  
      train_loader = torch.utils.data.DataLoader(
          datasets.MNIST(
              "../data",
              train=True,
              download=True,
              transform=transforms.Compose(
                  [
                      transforms.ToTensor(),
                      transforms.Normalize((0.1307,), (0.3081,)),
                  ]
              ),
          ),
          batch_size=args.batch_size,
          shuffle=True,
          num_workers=1,
          **dataloader_kwargs,
      )
  
      optimizer = optim.SGD(
          model.parameters(), lr=args.lr, momentum=args.momentum
      )
      for epoch in range(1, args.epochs + 1):
          train_epoch(
              epoch, args, model, device, train_loader, optimizer
          )
  
file_path: tensorboardX/examples/demo_hogwild.py
imports: from __future__ import print_function
  import argparse
  import os
  import torch
  import torch.optim
  import torch.nn
  import torch.nn.functional
  import torch.multiprocessing
  from torchvision import datasets, transforms
  from tensorboardX import GlobalSummaryWriter
label_desc: Use image node_id 1 for calling a global function with example usage: train(rank, args, model, device, dataloader_kwargs) without return types