class_docstr: Feature selection using data-mined expert knowledge.
  Based on the SURF algorithm as introduced in:
  Moore, Jason et al. Multiple Threshold Spatially Uniform ReliefF
  for the Genetic Analysis of Complex Human Diseases.
docstr: Identify nearest hits and misses within radius defined by average distance over whole distance array.
  This works the same regardless of endpoint type. 
code: def _find_neighbors(self, inst, avg_dist):
      """Identify nearest hits and misses within radius defined by average distance over whole distance array.
      This works the same regardless of endpoint type."""
      NN = []
      min_indicies = []
  
      for i in range(self._datalen):
          if inst != i:
              locator = [inst, i]
              if i > inst:
                  locator.reverse()
              d = self._distance_array[locator[0]][locator[1]]
              if (
                  d < avg_dist
              ):  # Defining the neighborhood with an average distance radius.
                  min_indicies.append(i)
      for i in range(len(min_indicies)):
          NN.append(min_indicies[i])
      return np.array(NN, dtype=np.int32)
  
file_path: scikit-rebate/skrebate/surf.py
imports: from __future__ import print_function
  import numpy
  from joblib import Parallel, delayed
  from .relieff import ReliefF
  from .scoring_utils import SURF_compute_scores
label_desc: Use image node_id 2 for calling the SURF obj's underlying member method code with example usage: obj._find_neighbors(inst, avg_dist) and returns: np