AFAIK, The closest thing we have on tensorflow.org 3 right now is the segmentation tutorial, just because it, effectively, runs a separate classifier per pixel.
THere’s nothing to stop you from using an extra dimension in your outputs and labels to run a bunch of classifiers in parallel.
But also note that if your model returns a distionary of tensors, keras’s model.fit will also accept a dictionary of losses and loss_weights (it optimizes the weighted sum of the losses). So if you need 10 binary classifiers, 3 different-N N-way classifiers, and a M-element regression you can do that from a single model.fit.