def train_network(X, y, epochs=1000, learning_rate=1.0):
    """Train the neural network"""
    # Initialize weights
    np.random.seed(42)
    W1 = np.random.randn(2, 4) * 0.5
    b1 = np.zeros((1, 4))
    W2 = np.random.randn(4, 1) * 0.5
    b2 = np.zeros((1, 1))

    losses = []

    for epoch in range(epochs):
        # Forward pass
        z1, a1, z2, predictions = forward_pass(X, W1, b1, W2, b2)

        # Compute loss
        loss = compute_loss(predictions, y)
        losses.append(loss)

        # Backward pass
        dW1, db1, dW2, db2 = backward_pass(X, y, z1, a1, z2, predictions, W1, b1, W2, b2)

        # Update weights
        W1 -= learning_rate * dW1
        b1 -= learning_rate * db1
        W2 -= learning_rate * dW2
        b2 -= learning_rate * db2

        # Print progress
        if epoch % 100 == 0:
            print(f"Epoch {epoch:4d}: Loss = {loss:.6f}")

    return W1, b1, W2, b2, losses

# Train the network
W1_trained, b1_trained, W2_trained, b2_trained, loss_history = train_network(X, y)