please show the source code of using momentum in pytorch

Sure, here is an example code snippet showing how to use momentum in PyTorch:

import torch
import torch.optim as optim

# Define your model
model = ...

# Define your optimizer with momentum
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

# Train your model
for epoch in range(num_epochs):
    for inputs, labels in dataloader:
        # Zero the gradients
        optimizer.zero_grad()

        # Forward pass
        outputs = model(inputs)
        loss = ...

        # Backward pass
        loss.backward()

        # Update the weights
        optimizer.step()

In this example, we use the `SGD` optimizer with a learning rate of 0.01 and momentum of 0.9. During training, we zero the gradients using `optimizer.zero_grad()`, compute the forward pass using the model, compute the loss, compute the backward pass using `loss.backward()`, and update the weights using `optimizer.step()`. The momentum term helps to accelerate the optimization process by adding a fraction of the previous update vector to the current update vector.