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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def check_features(self, features):
 """ Method to ensure data to be added is not empty and vectorized. Parameters features : iterable Any data that can be converted to a numpy array. Returns ------- features : numpy array Flattened non-empty numpy array. Raises ------ ValueError If input data is empty. """


        if not isinstance(features, np.ndarray):
            features = np.asarray(features)

        if features.size <= 0:
            raise ValueError('provided features are empty.')

        if features.ndim > 1:
            features = np.ravel(features)

        return features