Hello Mathieu,
You have understood my request and your picture is perfect. I also understand your very clear answer, thank you very much.
This kind of // configuration works with a decision Forest posted on Keras so I was dreaming of a better model such as gradient boosting.
Yesterday, I tested your model from the tuto but with 50 features  and 100 000 rows (50 inputs layers in a loop) concatenated +3 Dense layers + softmax with multiclass and then a layer ( not the final softmax) into the gradient model argument. It works  but overfit quickly  ( even with the early stopping in the gradient model and a callback in the functiional API).
Thank you again for your explanations and I hope for a future gradient model to make NN + trees more efficient on tabular structured data. ( atention layers and tabnet gives also poor result in comparison to gradient).