ValueError: Transformer '<class '__main__.Prepare_input'>' not supported; supported transformers are coremltools.converters.sklearn._dict_vectorizer,coremltools.converters.sklearn._one_hot_encoder,coremltools.converters.sklearn._normalizer,coremltools.converters.sklearn._standard_scaler,coremltools.converters.sklearn._imputer,coremltools.converters.sklearn._NuSVC,coremltools.converters.sklearn._NuSVR,coremltools.converters.sklearn._SVC,coremltools.converters.sklearn._SVR,coremltools.converters.sklearn._linear_regression,coremltools.converters.sklearn._LinearSVC,coremltools.converters.sklearn._LinearSVR,coremltools.converters.sklearn._logistic_regression,coremltools.converters.sklearn._random_forest_classifier,coremltools.converters.sklearn._random_forest_regressor,coremltools.converters.sklearn._decision_tree_classifier,coremltools.converters.sklearn._decision_tree_regressor,coremltools.converters.sklearn._gradient_boosting_classifier,coremltools.converters.sklearn._gradient_boosting_regressor.

class Prepare_input(BaseEstimator):
    def __init__(self, *args, **kwargs):
        return super().__init__(*args, **kwargs)

    def fit(self, x, y=None):
        return self

    def transform(self, X, y=None):
        x= self.extract_features(x)
        return x

    def extract_features(signal):
        print("transforming...")
        signal = some_modif(signal)
        return signal

classifier1= RandomForestClassifier(max_depth=700, n_estimators=100, random_state = 42)
pipeline1 = Pipeline([('Prepare_input', input_transform), ('classifier', classifier1)])

pipeline1.fit(train_data, train_labels)

coreml_model = coremltools.converters.sklearn.convert(pipeline1)
coreml_model.save('PPG_classif.mlmodel')