Thank you for your answer. What about the following code? I tried it in another way. And I found the below error.

class Model(nn.Module):
    # Our model
    def __init__(self):
        super(Model, self).__init__()
        self.fc1 = nn.Conv2d(1,10,3)
        self.bn1 = nn.BatchNorm2d(10)
        self.fc2= nn.Conv2d(10,20,3)
        self.bn2 = nn.BatchNorm2d(20)
        self.fc3= nn.Linear(11520,10)
        
    def forward(self,x):
        print(f'inout_size: {x.size()}')
        x = F.relu(self.fc1(x))
        x = self.bn1(x)
        x = F.relu(self.fc2(x))
        x = self.bn2(x)
        x = x.view(x.size(0),-1)
        x = self.fc3(x)
        print(f'output_size: {x.size()}')
        return(x)
########################################    



def train(gpu):
    rank = gpu
    
    dist.init_process_group(backed = 'nccl',
                           init_method = 'env://',
                           world_size= 4,
                           rank=rank)
    
    torch.manual_seed(0)
    
    model = Model()
    
    torch.cuda.set_device(gpu)
    model= model.to(device)
    optimizer = optim.Adam(model.parameters(),lr=0.1)
    lr_sch = lr_scheduler.StepLR(optimizer,step_size=2,gamma=0.1)
    criterion = nn.CrossEntropyLoss().to(device)
    
    ######################################
    model = nn.DistributedDataParallel(model, device_ids = [gpu])
    #####################################

    
    mnist =torchvision.datasets.MNIST('./data',train= True,download=True,
                                      transform =transforms.ToTensor())
    ####################################
    train_sampler = torch.utils.data.distributed.DistributedSampler(mnist,
                                                                    num_replicas=4,
                                                                    rank = rank)
    
    ###################################
    
    dataloader = DataLoader(mnist,batch_size=32,num_workers =4,pin_memory=True,
                                               sampler = train_sampler)
    
    #####################################
    
    
    for epoch in range(10):
        total_loss =0
        for X,y in dataloader:   
            X= X.to(device)
            y = y.long().to(device)
            pred = model(X)
            loss = criterion(pred,y)
            t_loss+= loss.item()
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()  
        print(f'Loss: {t_loss/len(dataloader)}')
     def main():
      os.environ['MASTER_ADDR'] = '172.20.24.55' ### the IP of vm_gpu02
      os.environ['MASTER_PORT'] = '9000'
      mp.spawn(train,nprocs=4)

if __name__=='__main__':
      main()

process 3 terminated with exit code 1