code: def _pairwise_distances_no_broadcast_helper(X, Y):  # pragma: no cover
      """Internal function for calculating the distance with numba. Do not use.
  
      Parameters
      ----------
      X : array of shape (n_samples, n_features)
          First input samples
  
      Y : array of shape (n_samples, n_features)
          Second input samples
  
      Returns
      -------
      distance : array of shape (n_samples,)
          Intermediate results. Do not use.
  
      """
      euclidean_sq = np.square(Y - X)
      return np.sqrt(np.sum(euclidean_sq, axis=1)).ravel()
  
file_path: pyod/pyod/utils/stat_models.py
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  from numba import njit
  from scipy.stats import pearsonr
  from sklearn.utils.validation import check_array
  from sklearn.utils.validation import check_consistent_length
label_desc: Use image node_id 2 for calling a global function with example usage: _pairwise_distances_no_broadcast_helper(X, Y) and returns: np