TensorFlow 1.x was all about building static graphs in a very un-Python manner, but with the TensorFlow 2.x line, you can also build models using the “eager” mode for immediate evaluation of ...

If this is what matters most for you, then your choice is probably TensorFlow. A network written in PyTorch is a Dynamic Computational Graph (DCG). It allows you to do any crazy thing you want to do.

PyTorch recreates the graph on the fly at each iteration step. In contrast, TensorFlow by default creates a single data flow graph, optimizes the graph code for performance, and then trains the model.