The development of effective channel feedback strategies is crucial for enabling the widespread adoption of Frequency Division Duplexing (FDD) in Massive MIMO systems. Conventional methods for multi-user FDD Massive MIMO with limited feedback fall into two families: Compressed Sensing and Codebook-based techniques. Recently, Deep Learning has emerged as a powerful paradigm for the challenging downlink FDD massive MIMO CSI feedback problem, outperforming conventional methods. End-to-end (E2E) models, such as that of Carpi et al. [ICC 2023], have demonstrated superior rate-overhead performance by jointly learning the pilot signals, the user's encoder and the base station's precoder. They adopt a "task-oriented approach": their objective is to maximize the downlink sum-rate while simultaneously minimizing a differentiable overhead penalty, with the balance between the two controlled by a trade-off parameter λ.