Placeholder PR - needs rebase and conflict resolution.

Important: #3292 needs to be merged before the current PR. (The current PR contains updated documentation that describe the new TF installation updates in #3292)

As of June 2024 we have a PyTorch Engine backend and we will be depreciating the TensorFlow backend. Currently, if you want to use TensorFlow, you need to run pip install deeplabcut[tf] or similar in order to install the correct version of TensorFlow in your conda env. Please note, we will be providing bug fixes, but we will not be supporting new TensorFlow versions beyond version 2.18.

Installing TensorFlow manually and getting it to have access to the GPU can be a bit tricky.
However, we try to simplify the installation procedure via optional dependencies.

A specific note for Windows users: TensorFlow’s own docs state that native Windows GPU support
ended after 2.10. We recommend Windows users to install The Windows Subsystem for Linux (WSL) if they want GPU support.

Installation via the tf optional dependencies
We recommend installing DeepLabCut with TensorFlow by specifying one of the 'extra's': tf, tf-cu11 or tf-cu12. E.g,

pip install deeplabcut[tf]

This table provides a more detailed summary on the available extras

Extra	Version	Python	CUDA	Role (summary)
tf	2.12 - 2.18	3.10-3.12	11.8 - 12.5	Default TensorFlow stack for most users.
tf-cu11	2.14	3.10 / 3.11	11.8	Pinned TF for CUDA 11.x-era stack
tf-cu12	2.18	3.10-3.12	12.5	Pinned TF for CUDA 12.x-era stack
tf-latest	2.18+	3.10-3.12	12.5+	(Not recommended!) Newest TensorFlow ≥ 2.18
apple_mchips	2.12 - 2.18	3.10-3.12	11.8 - 12.5	(Not recommended!) Legacy tensorflow-macos. Prefer tf instead.