how to use utils.save_checkpoint for downstream task

To use utils.save_checkpoint for a downstream task, you need to first define the model and optimizer for your task, and then checkpoint your current model and optimizer using the provided function. Here is an example:

First, define your model and optimizer:

model = MyDownstreamModel()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

Then, train your model and periodically save checkpoints:

for epoch in range(num_epochs):
# train your model
# ...

# save checkpoint
utils.save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
})

This will save your model's state_dict and your optimizer's state_dict to a file named 'checkpoint.pth.tar' in the current directory. You can load this checkpoint later by using the provided utils.load_checkpoint function.