    # Maybe {"logits": Tensor(...)}
    return outputs
def postprocess(self, model_outputs):
    best_class = model_outputs["logits"].softmax(-1)
    return best_class
```
The structure of this breakdown is to support relatively seamless support for CPU/GPU, while supporting doing
pre/postprocessing on the CPU on different threads
preprocess will take the originally defined inputs, and turn them into something feedable to the model. It might
contain more information and is usually a Dict.
_forward is the implementation detail and is not meant to be called directly. forward is the preferred
called method as it contains safeguards to make sure everything is working on the expected device. If anything is
linked to a real model it belongs in the _forward method, anything else is in the preprocess/postprocess.
