Fix TF input for np.ndarray
# What does this PR do?

This PR allows `np.ndarray` datatype as input of the models.

# Fixes
#9248 

I commented on the corresponding issue, I don't fully understand what's going on there in the error<|||||>This is on purpose because all the methods of a Keras Model allow to have `np.ndarray` as input. You can check for example `fit`, `predict` or `evaluate` here https://www.tensorflow.org/api_docs/python/tf/keras/Model. They all take a numpy array as possible input<|||||>Okey, I see. I still don't think we should provide this feature, just because keras has some automatic conversion internally. Is there a use case where one cannot forward a TF tensor and has to forward a `nd.array`? The general philosophy of the lib is to "not add too many magic functions" and allowing `nd.arrays` as inputs for TF seems like opening the door for lots of future issues to me. Let's see what @sgugger @LysandreJik think about it<|||||>On my side I would prefer to keep as much compliancy as possible with TF. But if everyone are not confident because of this Keras magic, I'm ok to do not provide it :)<|||||>The TF models do not accept numpy arrays inputs, so this would allow bad inputs to be passed to TF models. I think we should stick with inputs accepted by TF models only.<|||||>The TF models allow to have numpy arrays as inputs (if we had the `np.ndarray` type among the allowed ones). As example:
```python
from transformers import TFBertForSequenceClassification, BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
inputs = tokenizer("Hello", return_tensors="np")
model = TFBertForSequenceClassification.from_pretrained("bert-base-cased")
model(inputs)
```
Gives:
```
TFSequenceClassifierOutput(loss=None, logits=<tf.Tensor: shape=(1, 2), dtype=float32, numpy=array([[0.08065815, 0.58226204]], dtype=float32)>, hidden_states=None, attentions=None)
```

Keras layers/models are by default compliant with numpy arrays.<|||||>Ah my bad, I tried but forgot to checkout the PR before :facepalm:
If TF models do accept those inputs then, I have no strong objection.<|||||>@LysandreJik you mean adding a test in `test_modeling_tf_common` with numpy array as input for each model?<|||||>Yes, for example !<|||||>There is a now a new test to be sure that the models can be properly executed with numpy inputs.<|||||>Does it looks ok to be merged?