When training a model with a frozen feature extractor and a custom output network, the forward propagation is separated into two distinct stages. First, the input data is processed by the frozen pretrained layers to obtain the intermediate extracted features. Second, these fixed features are passed as input into the small custom output network to generate the final predictions. The loss is then calculated between these final predictions and the ground-truth labels, ensuring that only the custom network's weights are updated.