How to use the pyod.utils.utility.check_detector function in pyod

The input samples.

        y : Ignored
            Not used, present for API consistency by convention.

        Returns
        -------
        self : object
            Fitted estimator.
        """
        # check detector_list
        if len(self.detector_list) < 2:
            raise ValueError("The detector list has less than 2 detectors.")

        for detector in self.detector_list:
            check_detector(detector)

        # check random state and input
        self.random_state = check_random_state(self.random_state)
        X = check_array(X)
        self._set_n_classes(y)
        self.n_features_ = X.shape[1]

        # normalize input data
        self.X_train_norm_ = X
        train_scores = np.zeros([self.X_train_norm_.shape[0], self.n_clf])

        # fit each base detector and calculate standardized train scores
        for k, detector in enumerate(self.detector_list):
            detector.fit(self.X_train_norm_)
            train_scores[:, k] = detector.decision_scores_
        self.train_scores_ = train_scores

# validate two lists length
        if len(self.estimator_list) != len(self.standardization_flag_list):
            raise ValueError(
                "estimator_list length ({0}) is not equal "
                "to standardization_flag_list length ({1})".format(
                    len(self.estimator_list),
                    len(self.standardization_flag_list)))

        # validate the estimator list is not empty
        check_parameter(len(self.estimator_list), low=1,
                        param_name='number of estimators',
                        include_left=True, include_right=True)

        for estimator in self.estimator_list:
            check_detector(estimator)

        return len(self.estimator_list)

if self.n_estimators <= 0:
            raise ValueError("n_estimators must be greater than zero, "
                             "got {0}.".format(self.n_estimators))

        if self.base_estimator is not None:
            self.base_estimator_ = self.base_estimator
        else:
            self.base_estimator_ = default

        if self.base_estimator_ is None:
            raise ValueError("base_estimator cannot be None")

        # make sure estimator is consistent with sklearn
        if self.check_detector:
            check_detector(self.base_estimator_)

self.local_max_features = local_max_features
        self.local_min_features = 0.5
        self.local_region_iterations = 20
        self.local_region_threshold = int(self.local_region_iterations / 2)
        self.n_bins = n_bins
        self.n_selected = 1
        self.random_state = random_state

        assert len(estimator_list) > 1, "The estimator list has less than 2 estimators."

        if self.n_bins >= self.n_clf:
            warnings.warn("Number of histogram bins greater than number of classifiers, reducing n_bins to n_clf.")
            self.n_bins = self.n_clf

        for estimator in self.estimator_list:
            check_detector(estimator)