def _extract_labels_precomputed_features(self, label_examples: List[Message], attribute: Text=INTENT) -> Tuple[(List[FeatureArray], List[FeatureArray])]:
    'Collects precomputed encodings.'
    features = defaultdict(list)
    for e in label_examples:
        label_features = self._extract_features(e, attribute)
        for (feature_key, feature_value) in label_features.items():
            features[feature_key].append(feature_value)
    sequence_features = []
    sentence_features = []
    for (feature_name, feature_value) in features.items():
        if (SEQUENCE in feature_name):
            sequence_features.append(FeatureArray(np.array(feature_value), number_of_dimensions=3))
        else:
            sentence_features.append(FeatureArray(np.array(feature_value), number_of_dimensions=3))
    return (sequence_features, sentence_features)
Collects precomputed encodings.
def _extract_labels_precomputed_features(self, label_examples: List[Message], attribute: Text=INTENT) -> Tuple[(List[FeatureArray], List[FeatureArray])]:
    
    features = defaultdict(list)
    for e in label_examples:
        label_features = self._extract_features(e, attribute)
        for (feature_key, feature_value) in label_features.items():
            features[feature_key].append(feature_value)
    sequence_features = []
    sentence_features = []
    for (feature_name, feature_value) in features.items():
        if (SEQUENCE in feature_name):
            sequence_features.append(FeatureArray(np.array(feature_value), number_of_dimensions=3))
        else:
            sentence_features.append(FeatureArray(np.array(feature_value), number_of_dimensions=3))
    return (sequence_features, sentence_features)