<!-- BEGIN COPY / PASTE -->
    import torch
    import torch.nn as nn
    import torch.optim as optim

    # Check if GPU is available
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # Define a simple model
    class SimpleModel(nn.Module):
        def __init__(self):
            super(SimpleModel, self).__init__()
            self.fc = nn.Linear(10, 1)

        def forward(self, x):
            return self.fc(x)

    # Instantiate the model and move it to the GPU
    model = SimpleModel().to(device)

    # Define a loss function and optimizer
    criterion = nn.MSELoss()
    optimizer = optim.SGD(model.parameters(), lr=0.01)

    # Dummy input and target tensors
    inputs = torch.randn(5, 10).to(device)
    targets = torch.randn(5, 1).to(device)

    # Training step
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = criterion(outputs, targets)
    loss.backward()
    optimizer.step()
    <!-- END COPY / PASTE -->