src/transformers/pipelines/table_question_answering.py/TableQuestionAnsweringPipeline/preprocess
class TableQuestionAnsweringPipeline:    def preprocess(self, pipeline_input, sequential=None, padding=True, truncation=None):
        if truncation is None:
            if self.type == "tapas":
                truncation = "drop_rows_to_fit"
            else:
                truncation = "do_not_truncate"

        table, query = pipeline_input["table"], pipeline_input["query"]
        if table.empty:
            raise ValueError("table is empty")
        if query is None or query == "":
            raise ValueError("query is empty")
        inputs = self.tokenizer(table, query, return_tensors=self.framework, truncation=truncation, padding=padding)
        inputs["table"] = table
        return inputs