pipeline (preprocess).
Then define the outputs. Same policy as the inputs. The simpler, the better. Those will be the outputs of
postprocess method.
Start by inheriting the base class Pipeline. with the 4 methods needed to implement preprocess,
_forward, postprocess and _sanitize_parameters.
```python
from transformers import Pipeline
class MyPipeline(Pipeline):
    def _sanitize_parameters(self, **kwargs):
        preprocess_kwargs = {}
        if "maybe_arg" in kwargs:
            preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
        return preprocess_kwargs, {}, {}
def preprocess(self, inputs, maybe_arg=2):
    model_input = Tensor(inputs["input_ids"])
    return {"model_input": model_input}
def _forward(self, model_inputs):
    # model_inputs == {"model_input": model_input}
    outputs = self.model(**model_inputs)
