code: def predict(self, X):
          
          # Check is fit had been called
          check_is_fitted(self, ['inverse_influence_matrix'])
          # Check that X have correct shape
          X = check_array(X)
          return self.__find_leverages(X, self.inverse_influence_matrix) <= self.threshold_value
docs: Predict inside or outside AD for X.
  
          Parameters
          ----------
          X : array-like or sparse matrix, shape (n_samples, n_features)
              The input samples. Internally, it will be converted to
              ``dtype=np.float32`` and if a sparse matrix is provided
              to a sparse ``csr_matrix``.
  
          Returns
          -------
          ad : array of shape = [n_samples]
              Array contains True (reaction in AD) and False (reaction residing outside AD).