def for_multi_modal_transcription(
        cls: Type["FeedbackDataset"],
        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) transcription tasks.

        Args:
            use_markdown: Set this parameter to True if you want to use markdown in your TextQuestion. Defaults to `False`.
            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 transcription containing a "content" field with video, audio or image data and a TextQuestion named "description"
        """
        default_guidelines = "This is a multi-modal transcription dataset that contains video, audio or image data. Please describe the media content."
        return cls(
            fields=[
                TextField(name="content", use_markdown=True, required=True),
            ],
            questions=[
                TextQuestion(
                    name="description",
                    description="Provide a description of the media content, detailing the specific characteristics or elements present in each file.",
                    use_markdown=True,
                    required=True,
                )
            ],
            guidelines=default_guidelines if guidelines is None else guidelines,
            metadata_properties=metadata_properties,
            vectors_settings=vectors_settings,
        )

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

        Args:
            use_markdown: Set this parameter to True if you want to use markdown in your TextQuestion. Defaults to `False`.
            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 transcription containing a "content" field with video, audio or image data and a TextQuestion named "description"
        
argilla-v1/src/argilla_v1/client/feedback/dataset/local/mixins.py