lambda_mult: float = 0.5,
        exec_option: Optional[str] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Return docs selected using maximal marginal relevance.
        Maximal marginal relevance optimizes for similarity to query AND diversity
        among selected documents.
        Examples:
        >>> # Search using an embedding
        >>> data = vector_store.max_marginal_relevance_search(
        ...        query = <query_to_search>,
        ...        embedding_function = <embedding_function_for_query>,
        ...        k = <number_of_items_to_return>,
        ...        exec_option = <preferred_exec_option>,
        ... )
        Args:
            query: Text to look up documents similar to.
            k: Number of Documents to return. Defaults to 4.
            fetch_k: Number of Documents for MMR algorithm.
            lambda_mult: Value between 0 and 1. 0 corresponds
                        to maximum diversity and 1 to minimum.
                        Defaults to 0.5.
            exec_option (str): Supports 3 ways to perform searching.
                - "python" - Pure-python implementation running on the client.
                        Can be used for data stored anywhere. WARNING: using this
                        option with big datasets is discouraged due to potential
                        memory issues.
                    - "compute_engine" - Performant C++ implementation of the Deep
                        Lake Compute Engine. Runs on the client and can be used for
                        any data stored in or connected to Deep Lake. It cannot be
                        used with in-memory or local datasets.
                    - "tensor_db" - Performant, fully-hosted Managed Tensor Database.
                        Responsible for storage and query execution. Only available
                        for data stored in the Deep Lake Managed Database. To store
                        datasets in this database, specify