code: def ReliefF_compute_scores(
      inst,
      attr,
      nan_entries,
      num_attributes,
      mcmap,
      NN,
      headers,
      class_type,
      X,
      y,
      labels_std,
      data_type,
      weights=None,
  ):
      """Unique scoring procedure for ReliefF algorithm. Scoring based on k nearest hits and misses of current target instance."""
      scores = np.zeros(num_attributes)
      if isinstance(weights, np.ndarray):
          for feature_num in range(num_attributes):
              scores[feature_num] += weights[
                  feature_num
              ] * compute_score(
                  attr,
                  mcmap,
                  NN,
                  feature_num,
                  inst,
                  nan_entries,
                  headers,
                  class_type,
                  X,
                  y,
                  labels_std,
                  data_type,
              )
      else:
          for feature_num in range(num_attributes):
              scores[feature_num] += compute_score(
                  attr,
                  mcmap,
                  NN,
                  feature_num,
                  inst,
                  nan_entries,
                  headers,
                  class_type,
                  X,
                  y,
                  labels_std,
                  data_type,
              )
      return scores
  
file_path: scikit-rebate/skrebate/scoring_utils.py
imports: import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: ReliefF_compute_scores(inst, attr, nan_entries, num_attributes, mcmap, NN, headers, class_type, X, y, labels_std, data_type, weights) and returns: scores