code: def train(learning_rate):
      import torch.distributed as dist
      from torch.nn.parallel import DistributedDataParallel as DDP
      from torch.utils.data.distributed import DistributedSampler
  
      print("Running distributed training")
      dist.init_process_group("gloo")
  
      temp_dir = tempfile.mkdtemp()
  
      train_dataset = datasets.MNIST(
          temp_dir,
          train=True,
          download=True,
          transform=transforms.Compose(
              [
                  transforms.ToTensor(),
                  transforms.Normalize((0.1307,), (0.3081,)),
              ]
          ),
      )
  
      train_sampler = DistributedSampler(dataset=train_dataset)
      data_loader = torch.utils.data.DataLoader(
          train_dataset, batch_size=batch_size, sampler=train_sampler
      )
  
      model = Net()
      ddp_model = DDP(model)
  
      optimizer = optim.SGD(
          ddp_model.parameters(), lr=learning_rate, momentum=momentum
      )
      for epoch in range(1, num_epochs + 1):
          train_one_epoch(ddp_model, data_loader, optimizer, epoch)
  
      dist.destroy_process_group()
  
      shutil.rmtree(temp_dir)
  
file_path: spark/python/test_support/test_pytorch_training_file.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  import tempfile
  import shutil
label_desc: Use image node_id 2 for calling a global function with example usage: train(learning_rate) without return types