text: import matplotlib.pyplot as plt
  import numpy as np
  import torch
  import torch.nn as nn
  
  # Hyper-parameters
  input_size = 1
  output_size = 1
  num_epochs = 60
  learning_rate = 0.001
  
  # Toy dataset
  x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], 
                      [9.779], [6.182], [7.59], [2.167], [7.042], 
                      [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)
  
  y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], 
                      [3.366], [2.596], [2.53], [1.221], [2.827], 
                      [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)
  
  # Linear regression model
  model = nn.Linear(input_size, output_size)
  
  # Loss and optimizer
  criterion = nn.MSELoss()
  optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  
  
  # Train the model
  for epoch in range(num_epochs):
      # Convert numpy arrays to torch tensors
      inputs = torch.from_numpy(x_train)
      targets = torch.from_numpy(y_train)
  
      # Forward pass
      outputs = model(inputs)
      loss = criterion(outputs, targets)
      
      # Backward and optimize
      optimizer.zero_grad()
      loss.backward()
      optimizer.step()
      
      if (epoch+1) % 5 == 0:
          print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
  
  # Plot the graph
  predicted = model(torch.from_numpy(x_train)).detach().numpy()
  plt.plot(x_train, y_train, 'ro', label='Original data')
  plt.plot(x_train, predicted, label='Fitted line')
  plt.legend()
  plt.show()
  
  # Save the model checkpoint
  torch.save(model.state_dict(), 'model.ckpt')