Hi @Alexandros_Tzoumas Great question!
Setting the same seed for Tensorflow can help to ensure that the code produces consistent results across different runs. However, the random number generator is not the only source of randomness in Tensorflow, there are other factors such as the order in which data is processed or the initialization of variables can also affect the results of the code. So, it is possible for different versions of Tensorflow to produce different results, even when the same seed is used.
To ensure that code is deterministic, it may be necessary to take additional steps beyond setting the random number generator seed. Such as using specific Tensorflow functions or features that are designed to be deterministic, or carefully controlling other sources of randomness in the code.
You can check this links that may help you to understand why this have to be made in order to avoid controlling for several factors.