code: def get_outliers_inliers(X, y):
      """Internal method to separate inliers from outliers.
  
      Parameters
      ----------
      X : numpy array of shape (n_samples, n_features)
          The input samples
  
      y : list or array of shape (n_samples,)
          The ground truth of input samples.
  
      Returns
      -------
      X_outliers : numpy array of shape (n_samples, n_features)
          Outliers.
  
      X_inliers : numpy array of shape (n_samples, n_features)
          Inliers.
  
      """
      X_outliers = X[np.where(y == 1)]
      X_inliers = X[np.where(y == 0)]
      return X_outliers, X_inliers
  
file_path: pyod/pyod/utils/data.py
imports: from __future__ import division
  from __future__ import print_function
  from warnings import warn
  import numpy
  from sklearn.datasets import make_blobs
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import train_test_split
  from sklearn.utils import check_X_y
  from sklearn.utils import check_consistent_length
  from sklearn.utils import check_random_state
  from sklearn.utils import column_or_1d
  from .utility import check_parameter
  from .utility import precision_n_scores
label_desc: Use image node_id 2 for calling a global function with example usage: get_outliers_inliers(X, y) and returns: X_outliers,  X_inliers