code: def predict(self, X):
          
          # Check is fit had been called
          check_is_fitted(self, ['_x_min', '_x_max'])
  
          # Input validation
          X = check_array(X)
          return ((X - self._x_min).min(axis=1) >= 0) & ((self._x_max - X).min(axis=1) >= 0)
docs: Predict if a particular sample is an outlier or not.
  
          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
          -------
          is_inlier : array, shape (n_samples,)
                     For each observations, tells whether or not (True or False) it should
                     be considered as an inlier according to the fitted model.