All models have outputs that are instances of subclasses of [~file_utils.ModelOutput]. Those are
data structures containing all the information returned by the model, but that can also be used as tuples or
dictionaries.
Let's see of this looks on an example:
```python
from transformers import BertTokenizer, BertForSequenceClassification
import torch
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = BertForSequenceClassification.from_pretrained("bert-base-uncased")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
outputs = model(**inputs, labels=labels)
```
The outputs object is a [~modeling_outputs.SequenceClassifierOutput], as we can see in the
documentation of that class below, it means it has an optional loss, a logits an optional hidden_states and
