You would access the fully connected layer and just replace it with the output argument being the number of classes that your new task has.  For example:
model = get_model()
checkpoint = torch.load(path_to_your_pth_file)
model.load_state_dict(checkpoint['state_dict'])
model.fc = nn.Linear(2048, 10) #input is whatever the output of prior layer is and output is the number of classes that you have

You can then decide how you would like to proceed with the training step since your last layer has random weights and the rest of your model has trained weights.  You can either just train the last layer or the whole network.