code: def _get_sampling_N(dist_matrix, p_ix, r):  # pragma: no cover
      """Computes the set of r-neighbours.
  
      Parameters
      ----------
      dist_matrix : array-like, shape (n_samples, n_features)
          The distance matrix w.r.t. to the training samples.
  
      p_ix : int
          Subsetting index
  
      r : int
          Neighbourhood radius
  
  
      Returns
      -------
      sample : array, shape (n_sample, )
          Returns a list of neighbourhood data points.
      """
  
      p_distances = dist_matrix[p_ix, :]
      sample = np.nonzero(p_distances <= r)[0]
      return sample
  
file_path: pyod/pyod/models/loci.py
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  from numba import njit
  from scipy.spatial.distance import pdist, squareform
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from .base import BaseDetector
label_desc: Use image node_id 2 for calling a global function with example usage: _get_sampling_N(dist_matrix, p_ix, r) and returns: sample