To adapt a pretrained model for a new classification task, its original output layer is bypassed, and the intermediate representations—or extracted features—are fed into a newly defined custom output network. Instead of a simple linear mapping, this custom network can be a small-scale architecture, such as a stack of multiple fully connected layers with non-linear activations. During the forward pass, the extracted features serve as the direct input to this custom output network, which is trained from scratch to generate the final predictions.