src/transformers/pipelines/base.py/Pipeline/_forward
class Pipeline:    def _forward(self, input_tensors: Dict[str, GenericTensor], **forward_parameters: Dict) -> ModelOutput:
        """
        _forward will receive the prepared dictionnary from `preprocess` and run it on the model. This method might
        involve the GPU or the CPU and should be agnostic to it. Isolating this function is the reason for `preprocess`
        and `postprocess` to exist, so that the hot path, this method generally can run as fast as possible.

        It is not meant to be called directly, `forward` is preferred. It is basically the same but contains additional
        code surrounding `_forward` making sure tensors and models are on the same device, disabling the training part
        of the code (leading to faster inference).
        """
        raise NotImplementedError("_forward not implemented")