Maybe I’m using the wrong terminology, but by fine tuning a model I mean to use a pretrained model, make some necessary changes for the new dataset (e.g. number of output units) and train this model using the new dataset.
The tutorial explains two different approaches, where the first one trains all parameters, while the latter one only trains the last output layer.
Passing the output of one model to another one is independent from your fine tuning use case, so you can ignore it for now.