Use SGD to optimize the parameters in self.model

Sure, here is an example code snippet that uses SGD optimizer to optimize the parameters in self.model:

import torch.optim as optim

# Define the SGD optimizer with a learning rate of 0.01
optimizer = optim.SGD(self.model.parameters(), lr=0.01)

# Loop over your training data batches
for batch in training_data:
    # Clear gradients
    optimizer.zero_grad()

    # Forward pass
    output = self.model(batch)

    # Calculate loss
    loss = compute_loss(output, target)

    # Backward pass
    loss.backward()

    # Update parameters
    optimizer.step()

In this code, `self.model` is assumed to be a PyTorch model that has trainable parameters. We define an SGD optimizer with a learning rate of 0.01 and use it to optimize the model parameters. In each training batch, we first clear the gradients, perform a forward pass to get the model output, compute the loss, perform a backward pass to compute the gradients, and finally update the model parameters using the `optimizer.step()` call.