Hi.
I’m working through the series of sagemaker-hugginface notebooks and it is not clear to me how the predict data is preprocess before call the model.
The notebook 01_getting_started_pytorch.ipynb shows these 3 steps:

preprocess datasets
save datsets on s3
train the model using sagemaker Huggingface API
once model trained, deploy model and make predictions from a input data in a dictionary format like: {"inputs": "blablabla"}


my question is: ¿how are these input data being tokenized before to get in the model?