Decoupling tensorflow from keras in stubs

Whilst working on https://github.com/python/typeshed/pull/11696, I was having issues referencing the source code. After reading the doc and a bit of investigation, I realized why:

The runtime of https://github.com/python/typeshed/tree/main/stubs/tensorflow/tensorflow/keras actually are just references to the keras source code at https://github.com/keras-team/keras/tree/master/keras (import keras), and is generated / injected in import tensorflow.keras by https://github.com/keras-team/keras/blob/6454a4888a494c20ab0ea1dc6912cbcc13c5f940/pip_build.py#L62

Even tensorflow's own doc links back to karas source code https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/Callback

>>> from keras import callbacks
2024-04-03 23:28:35.387416: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-04-03 23:28:36.959410: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
>>> callbacks.__file__
'...\\typeshed\\.venv\\lib\\site-packages\\keras\\callbacks\\__init__.py'
>>> from tensorflow.keras import callbacks 
>>> callbacks.__file__
'...\\typeshed\\.venv\\lib\\site-packages\\keras\\callbacks\\__init__.py'

In other words, keras "pollutes" the tensorflow namespace! I don't know if the type stubs specifications allow two distributions to write to the same stub-only package, which would allow us to perfectly reflect what's truly happening. Second best thing would be to have stubs for keras and keep tensorflow.keras in the tensorflow stubs (since tensorflow is dependent on keras anyway)

CC @hoel-bagard & @hmc-cs-mdrissi

Lee stubs/tensorflow/tensorflow/keras junto con pip_build.py de Keras y las fuentes de ejecución enlazadas para entender cómo se inyecta tensorflow.keras. Confirma con los maintainers la disposición compatible de los paquetes stub; se considera terminado cuando el diseño elegido representa correctamente tanto keras como tensorflow.keras sin ambigüedad de namespace.