def __property_strategy(attr: str):
    """Returns a np.ndarray with the information required of the instances

    Args:
        iterable (Iterable[Instance]): Instances to describe

    Returns:
        np.ndarray: Array of the feature descriptors of each instance
    """
    try:
        if attr not in ("features", "transformed"):
            raise AttributeError()
    except AttributeError:
        raise ValueError(
            f"Object of class Instance does not have a property named {attr}"
        )

    def strategy(iterable: Iterable[Instance]) -> np.ndarray:
        return np.asarray([getattr(i, attr) for i in iterable])

    return strategy

def descriptor(key: str, verbose: bool = False):
    """Decorator to create new descriptor strategies

    Args:
        key (str): Key to refer the descriptor-function
        verbose (bool, optional): Prints a message when the function is registered. Defaults to False.
    """

    def decorate(func: DescStrategy):
        if verbose:
            print(f"Registering descriptor function: {func.__name__} with key: {key}")
        DESCRIPTORS[key] = func
        return func

    return decorate

def instance_strategy(iterable: Iterable[Instance]) -> np.ndarray:
    """It returns the instance information as its descriptor

    Args:
        iterable (Iterable[Instance]): Instances to describe

    Returns:
        np.ndarray: Array of descriptor instance (whole instace data)
    """
    return np.asarray([*iterable])

def performance_strategy(iterable: Iterable[Instance]) -> np.ndarray:
    """It generates the performance descriptor of an instance
    based on the scores of the solvers in the portfolio over such instance

    Args:
        iterable (Iterable[Instance]): Instances to describe

    Returns:
        np.ndarray: Array of performance descriptors of each instance
    """
    return np.asarray([np.mean(i.portfolio_scores, axis=1) for i in iterable])