chore: import upstream snapshot with attribution

tutorials/01-basics/linear_regression/main.py

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+import torch
+import torch.nn as nn
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# 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')