System information

• TensorFlow version (you are using): 2.4
• Are you willing to contribute it (Yes/No): Yes, but it seems that multiple other things might depend on it

Describe the feature and the current behavior/state.
In the current version of Keras, the loss function is applied for each output of the model elementwise (if there are multiple), or one can define for each individual output one loss function. This seems to be not really consistent with other fundamentals in Keras / TF, and it would be better to have at least the possibility to get the full list of model-outputs as a y_pred input of the loss function. It's especially confusing since in the train_step

Will this change the current api? How?
Yes, there wouldn't be multiple losses for a model but only one. One could still get the same results by building a "wrapper" around the individual losses and adding it as a loss.

Who will benefit from this feature?
Everyone who wants to build more complex models, especially if you have different output dimensions in your model-output, where the proposed solution is in keras-team/keras#14140 would not work anymore (e.g., one output an image and one output a label).

Any Other info.