```
This returns a dictionary string to list of ints. The input_ids are the indices corresponding
to each token in our sentence. We will see below what the attention_mask is used for and
in the next section the goal of token_type_ids.
The tokenizer can decode a list of token ids in a proper sentence:
```py
tokenizer.decode(encoded_input["input_ids"])
"[CLS] Hello, I'm a single sentence! [SEP]"
```
As you can see, the tokenizer automatically added some special tokens that the model expects. Not all models need
special tokens; for instance, if we had used gpt2-medium instead of bert-base-cased to create our tokenizer, we
would have seen the same sentence as the original one here. You can disable this behavior (which is only advised if you
have added those special tokens yourself) by passing add_special_tokens=False.
