tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
sequence = (
...     "Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, "
...     "therefore very close to the Manhattan Bridge."
... )
inputs = tokenizer(sequence, return_tensors="tf")
tokens = inputs.tokens()
outputs = model(**inputs)[0]
predictions = tf.argmax(outputs, axis=2)
```
This outputs a list of each token mapped to its corresponding prediction. Differently from the pipeline, here every
token has a prediction as we didn't remove the "0"th class, which means that no particular entity was found on that
token.
In the above example, predictions is an integer that corresponds to the predicted class. We can use the
model.config.id2label property in order to recover the class name corresponding to the class number, which is
