def for_multi_modal_classification(
        cls: Type["FeedbackDataset"],
        labels: List[str],
        multi_label: bool = False,
        guidelines: Optional[str] = None,
        metadata_properties: List[AllowedMetadataPropertyTypes] = None,
        vectors_settings: List[VectorSettings] = None,
    ) -> "FeedbackDataset":
        """
        You can use this method to create a basic dataset for multi-modal (video, audio,image) classification tasks.

        Args:
            labels: A list of labels for your dataset
            multi_label: Set this parameter to True if you want to add multiple labels to your dataset
            guidelines: Contains the guidelines for the dataset
            metadata_properties: Contains the metadata properties that will be indexed and could be used to filter the dataset. Defaults to `None`.
            vectors_settings: Define the configuration of the vectors associated to the records that will be used to perform the vector search. Defaults to `None`.

        Returns:
            A `FeedbackDataset` object for multi-modal classification containing a "content" field with video, audio or image data and LabelQuestion or MultiLabelQuestion named "label"
        """
        default_guidelines = "This is a multi-modal classification dataset that contains videos, audios or images. Given a set of media files and a predefined set of labels, the goal of multi-modal classification is to assign one or more labels to each media file based on its content. Please classify the media file by making the correct selection."

        description = "Classify the media content by selecting the correct label from the given list of labels."
        return cls(
            fields=[TextField(name="content", use_markdown=True, required=True)],
            questions=[
                LabelQuestion(
                    name="label",
                    labels=labels,
                    description=description,
                )
                if not multi_label
                else MultiLabelQuestion(
                    name="label",
                    labels=labels,
                    description=description,
                )
            ],
            guidelines=guidelines
            if guidelines is not None
            else default_guidelines
            if multi_label
            else default_guidelines.replace("one or more labels", "one label"),
            metadata_properties=metadata_properties,
            vectors_settings=vectors_settings,
        )

        You can use this method to create a basic dataset for multi-modal (video, audio,image) classification tasks.

        Args:
            labels: A list of labels for your dataset
            multi_label: Set this parameter to True if you want to add multiple labels to your dataset
            guidelines: Contains the guidelines for the dataset
            metadata_properties: Contains the metadata properties that will be indexed and could be used to filter the dataset. Defaults to `None`.
            vectors_settings: Define the configuration of the vectors associated to the records that will be used to perform the vector search. Defaults to `None`.

        Returns:
            A `FeedbackDataset` object for multi-modal classification containing a "content" field with video, audio or image data and LabelQuestion or MultiLabelQuestion named "label"
        
argilla-v1/src/argilla_v1/client/feedback/dataset/local/mixins.py