src/transformers/pipelines/text_classification.py/TextClassificationPipeline/_sanitize_parameters
class TextClassificationPipeline:    def _sanitize_parameters(self, return_all_scores=None, function_to_apply=None, top_k="", **tokenizer_kwargs):
        # Using "" as default argument because we're going to use `top_k=None` in user code to declare
        # "No top_k"
        preprocess_params = tokenizer_kwargs

        postprocess_params = {}
        if hasattr(self.model.config, "return_all_scores") and return_all_scores is None:
            return_all_scores = self.model.config.return_all_scores

        if isinstance(top_k, int) or top_k is None:
            postprocess_params["top_k"] = top_k
            postprocess_params["_legacy"] = False
        elif return_all_scores is not None:
            warnings.warn(
                "`return_all_scores` is now deprecated,  if want a similar funcionality use `top_k=None` instead of"
                " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.",
                UserWarning,
            )
            if return_all_scores:
                postprocess_params["top_k"] = None
            else:
                postprocess_params["top_k"] = 1

        if isinstance(function_to_apply, str):
            function_to_apply = ClassificationFunction[function_to_apply.upper()]

        if function_to_apply is not None:
            postprocess_params["function_to_apply"] = function_to_apply
        return preprocess_params, {}, postprocess_params