def query_profile_cache(
        self, op_class: str, args: Dict[str, Any]
    ) -> Tuple[str, int]:
        """Query the profile cache for the given op class and args.

        Parameters
        ----------
        op_class : str
            Op class name. gemm, conv or normalization
        args : Dict[str, Any]
            Op arguments.

        Returns
        -------
        Tuple[str, int]
            Queried best profiling results.

        Raises
        ------
        NotImplementedError
            If op class is not supported, raise error.
        """
        if op_class == "gemm":
            return self._profile_cache.query_gemm(args)
        if op_class == "conv":
            return self._profile_cache.query_conv(args)
        if op_class == "conv3d":
            return self._profile_cache.query_conv3d(args)
        if op_class == "normalization":
            return self._profile_cache.query_normalization(args)
        raise NotImplementedError
Query the profile cache for the given op class and args.

        Parameters
        ----------
        op_class : str
            Op class name. gemm, conv or normalization
        args : Dict[str, Any]
            Op arguments.

        Returns
        -------
        Tuple[str, int]
            Queried best profiling results.

        Raises
        ------
        NotImplementedError
            If op class is not supported, raise error.
        
python/aitemplate/backend/target.py