Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a DistilBERT model and
   loads it with the weights stored in the checkpoint.
Define a sequence with a masked token, placing the tokenizer.mask_token instead of a word.
Encode that sequence into a list of IDs and find the position of the masked token in that list.
Retrieve the predictions at the index of the mask token: this tensor has the same size as the vocabulary, and the
   values are the scores attributed to each token. The model gives higher score to tokens it deems probable in that
   context.
Retrieve the top 5 tokens using the PyTorch topk or TensorFlow top_k methods.
Replace the mask token by the tokens and print the results
```py
from transformers import AutoModelForMaskedLM, AutoTokenizer
import torch
