code: def train(
      train_loader,
      model,
      criterion,
      optimizer,
      epoch,
      device,
      print_freq,
      rank,
  ):
      running_loss = 0.0
      for i, data in enumerate(train_loader, 0):
          # get the inputs; data is a list of [inputs, labels]
          inputs, labels = data[0].to(device), data[1].to(device)
  
          # zero the parameter gradients
          optimizer.zero_grad()
  
          # forward + backward + optimize
          outputs = model(inputs)
          loss = criterion(outputs, labels)
          loss.backward()
          optimizer.step()
  
          # print statistics
          running_loss += loss.item()
          if i % print_freq == 0:  # print every print_freq mini-batches
              print(
                  "Rank %d: [%d, %5d] loss: %.3f"
                  % (rank, epoch + 1, i + 1, running_loss / print_freq)
              )
              running_loss = 0.0
  
file_path: MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-distributeddataparallel/train.py
imports: import torch
  import torchvision
  import torchvision.transforms
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import os
  import argparse
label_desc: Use image node_id 1 for calling a global function with example usage: train(train_loader, model, criterion, optimizer, epoch, device, print_freq, rank) without return types