def for_question_answering(
        cls,
        formatting_func: Optional[
            Callable[[Dict[str, Any]], Union[None, Tuple[str, str, str], Iterator[Tuple[str, str, str]]]]
        ] = None,
        question: Optional[TextField] = None,
        context: Optional[TextField] = None,
        answer: Optional[TextQuestion] = None,
    ) -> "TrainingTaskForQuestionAnswering":
        """Training data for question answering

        Args:
            formatting_func: A formatting function converting a dictionary of records into zero,
                one or more question-context-answer text tuples.
            question: The TextField to use for training.
            context: The TextField to use for training.
            answer: The TextQuestion to use for training.

        Examples:
            >>> # with defaults
            >>> from argilla_v1 import TrainingTaskForQuestionAnswering
            >>> dataset = rg.FeedbackDataset.from_argilla(name="...")
            >>> task = TrainingTaskForQuestionAnswering(
            ...     question=dataset.field_by_name("question"),
            ...     context=dataset.field_by_name("context"),
            ...     answer=dataset.question_by_name("answer"),
            ... )
            >>> dataset.prepare_for_training(framework="...", task=task)
            >>> # with formatting_func
            >>> from argilla_v1 import TrainingTaskForQuestionAnswering
            >>> dataset = rg.FeedbackDataset.from_argilla(name="...")
            >>> def formatting_func(sample: Dict[str, Any]):
            ...     question = sample["question"]
            ...     context = sample["context"]
            ...     for answer in sample["answer"]:
            ...         if not all([question, context, answer["value"]]):
            ...             continue
            ...         yield question, context, answer["value"]
            >>> task = TrainingTaskForQuestionAnswering(formatting_func=formatting_func)
            >>> dataset.prepare_for_training(framework="...", task=task)
        """
        if (question and context and answer) and formatting_func is not None:
            raise ValueError(
                "You must provide either `question`, `context` and `answer`, or a `formatting_func`, not both."
            )

        if formatting_func is not None:
            if question or context or answer:
                raise ValueError(
                    "`formatting_func` is already defined, so you cannot define `question`, `context` and `answer`."
                )
            return TrainingTaskForQuestionAnswering(formatting_func=formatting_func)
        else:
            defaults = QuestionAnsweringDefaults(question=question, context=context, answer=answer)
            return TrainingTaskForQuestionAnswering(defaults=defaults)
Training data for question answering

        Args:
            formatting_func: A formatting function converting a dictionary of records into zero,
                one or more question-context-answer text tuples.
            question: The TextField to use for training.
            context: The TextField to use for training.
            answer: The TextQuestion to use for training.

        Examples:
            >>> # with defaults
            >>> from argilla_v1 import TrainingTaskForQuestionAnswering
            >>> dataset = rg.FeedbackDataset.from_argilla(name="...")
            >>> task = TrainingTaskForQuestionAnswering(
            ...     question=dataset.field_by_name("question"),
            ...     context=dataset.field_by_name("context"),
            ...     answer=dataset.question_by_name("answer"),
            ... )
            >>> dataset.prepare_for_training(framework="...", task=task)
            >>> # with formatting_func
            >>> from argilla_v1 import TrainingTaskForQuestionAnswering
            >>> dataset = rg.FeedbackDataset.from_argilla(name="...")
            >>> def formatting_func(sample: Dict[str, Any]):
            ...     question = sample["question"]
            ...     context = sample["context"]
            ...     for answer in sample["answer"]:
            ...         if not all([question, context, answer["value"]]):
            ...             continue
            ...         yield question, context, answer["value"]
            >>> task = TrainingTaskForQuestionAnswering(formatting_func=formatting_func)
            >>> dataset.prepare_for_training(framework="...", task=task)
        
argilla-v1/src/argilla_v1/client/feedback/training/schemas/base.py