Change a logic in pipeline test regarding TF
# What does this PR do?

- **Currently**, the tiny models in pipeline tests are created using `model_class(config)`, and for TF models, the weights are not created at this point. Then we set the device (**which turns to be CPU instead of GPU!**), and the wegiths are created in CPU context --> we get the expected exceptions.
- In the future, we will use the tiny model from Hub repos
- The models are loaded using `from_pretrained`
- If GPU available, those weights are initialized in GPU context automatically for TF models, including embedding layers
- From what @Rocketknight1 says at the end, we won't get the expected exceptions in this situation (TF, layer weights loaded in GPU context)

**In order to use the tiny models from the Hub without any pipeline test failure, we will have to skip this check under the above described situation.**

From @Rocketknight1 
> Embedding layers in Keras have different behaviours on CPU and GPU when you pass invalid indices. On CPU, the layer checks inputs and throws an error if you're out of range, but on GPU you just get a zeros tensor as output with no error
_The documentation is not available anymore as the PR was closed or merged._<|||||>cc @gante to this one - he's been working on a way to check for invalid indices even for embedding layers on GPU