code: import torch 
  import torch.nn as nn
  import torchvision
  import torchvision.transforms as transforms
  
  
  # Device configuration
  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
  
  # Hyper-parameters
  sequence_length = 28
  input_size = 28
  hidden_size = 128
  num_layers = 2
  num_classes = 10
  batch_size = 100
  num_epochs = 2
  learning_rate = 0.01
  
  # MNIST dataset
  train_dataset = torchvision.datasets.MNIST(root='../../data/',
                                             train=True, 
                                             transform=transforms.ToTensor(),
                                             download=True)
  
  test_dataset = torchvision.datasets.MNIST(root='../../data/',
                                            train=False, 
                                            transform=transforms.ToTensor())
  
  # Data loader
  train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                             batch_size=batch_size, 
                                             shuffle=True)
  
  test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                            batch_size=batch_size, 
                                            shuffle=False)
  
  # Recurrent neural network (many-to-one)
  class RNN(nn.Module):
      def __init__(self, input_size, hidden_size, num_layers, num_classes):
          super(RNN, self).__init__()
          self.hidden_size = hidden_size
          self.num_layers = num_layers
          self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
          self.fc = nn.Linear(hidden_size, num_classes)
      
      def forward(self, x):
          # Set initial hidden and cell states 
          h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) 
          c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
          
          # Forward propagate LSTM
          out, _ = self.lstm(x, (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size)
          
          # Decode the hidden state of the last time step
          out = self.fc(out[:, -1, :])
          return out
  
  model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)
  
  
  # Loss and optimizer
  criterion = nn.CrossEntropyLoss()
  optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
  
  # Train the model
  total_step = len(train_loader)
  for epoch in range(num_epochs):
      for i, (images, labels) in enumerate(train_loader):
          images = images.reshape(-1, sequence_length, input_size).to(device)
          labels = labels.to(device)
          
          # Forward pass
          outputs = model(images)
          loss = criterion(outputs, labels)
          
          # Backward and optimize
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
          
          if (i+1) % 100 == 0:
              print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
                     .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
  
  # Test the model
  model.eval()
  with torch.no_grad():
      correct = 0
      total = 0
      for images, labels in test_loader:
          images = images.reshape(-1, sequence_length, input_size).to(device)
          labels = labels.to(device)
          outputs = model(images)
          _, predicted = torch.max(outputs.data, 1)
          total += labels.size(0)
          correct += (predicted == labels).sum().item()
  
      print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total)) 
  
  # Save the model checkpoint
  torch.save(model.state_dict(), './outputs/model.ckpt')
  
apis: torch.utils.data.DataLoader torch.nn.LSTM torch.nn.Linear torch.no_grad torch.nn.CrossEntropyLoss torch.cuda.is_available torch.max