getting some object oriented errors with kmeans_process function not recognized

processor_pipeline_nicholas.py

class SequenceFeatureEnricher(object):
    def __init__(self, regression_features=True, std_features=True, kmeans_features=True, masknan: float = None, n_clusters=8):
        self.regression_features = regression_features
        self.std_features = std_features
        self.kmeans_features= kmeans_features
        self.n_clusters=n_clusters
        self.masknan = masknan
        self.sample_sequences = []
        self.sequence_features = []
        
        # So we can map sequence features back to minmax values for scaling
        self.sequence_features_scalar_map = []
        if regression_features:
            for f in range(d.ENRICH_START, d.NUM_INPUTS):
                self.sequence_features_scalar_map.append(f)
                self.sequence_features_scalar_map.append(f)
        if std_features:
            for f in range(d.ENRICH_START, d.NUM_INPUTS):
                self.sequence_features_scalar_map.append(f)
                
    def kmeans_process(self, nd: np.ndarray):
        from sklearn.cluster import KMeans
        kmeans=KMeans(n_clusters=self.n_clusters, n_jobs=-1, verbose=1).fit(nd)
        return kmeans.labels_, kmeans.cluster_centers_
    
    def process(self, nd: np.ndarray):

        # Add some features
        for sequence in range(0, nd.shape[0]):

            features_to_add = []

            if self.regression_features:
                for f in range(d.ENRICH_START, d.NUM_INPUTS):
                    m = np.nansum(nd[sequence][:, f]) / np.nansum(np.arange(0, nd.shape[1]))
                    b = nd[sequence][:, f][0]

                    features_to_add.extend([m, b])

            if self.std_features:
                for f in range(d.ENRICH_START, d.NUM_INPUTS):
                    features_to_add.append(np.nanstd(nd[sequence][:, f]))
           
            self.sample_sequences.append(nd[sequence])
            self.sequence_features.append(features_to_add)