def for_proximal_policy_optimization(
        cls: Type["FeedbackDataset"],
        rating_scale: int = 7,
        context: bool = False,
        use_markdown: 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 proximal policy optimization tasks.

        Args:
            rating_scale: Set this parameter to the number of relevancy scale you want to add to your dataset
            context: Set this parameter to True if you want to add context to your dataset
            use_markdown: Set this parameter to True if you want to use markdown in 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 proximal policy optimization containing "context" and "action" fields and a LabelQuestion named "label"
        """
        default_guidelines = "This is a proximal policy optimization dataset that contains contexts and prompts. Please choose the label that best describes prompt."
        fields = [TextField(name="prompt", use_markdown=use_markdown)]
        if context:
            fields.append(TextField(name="context", use_markdown=use_markdown, required=False))

        return cls(
            fields=fields,
            questions=[
                RatingQuestion(
                    name="prompt",
                    values=list(range(1, rating_scale + 1)),
                    description="Choose one of the labels that best describes the prompt.",
                )
            ],
            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 proximal policy optimization tasks.

        Args:
            rating_scale: Set this parameter to the number of relevancy scale you want to add to your dataset
            context: Set this parameter to True if you want to add context to your dataset
            use_markdown: Set this parameter to True if you want to use markdown in 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 proximal policy optimization containing "context" and "action" fields and a LabelQuestion named "label"
        
argilla-v1/src/argilla_v1/client/feedback/dataset/local/mixins.py