code: def ReRank2(probFea, galFea, k1=20, k2=6, lambda_value=0.3):
      query_num = probFea.shape[0]
      all_num = query_num + galFea.shape[0]
      feat = np.concatenate((probFea, galFea), axis=0)
  
      initial_rank = batch_numpy_topk(feat, feat, k1 + 1, N=6000)
      del probFea
      del galFea
      gc.collect()  # empty memory
      R = []
      for i in tqdm(range(all_num)):
          # k-reciprocal neighbors
          k_reciprocal_index = k_reciprocal_neigh(initial_rank, i, k1)
          k_reciprocal_expansion_index = k_reciprocal_index
          for j in range(len(k_reciprocal_index)):
              candidate = k_reciprocal_index[j]
              candidate_k_reciprocal_index = k_reciprocal_neigh(
                  initial_rank, candidate, int(np.around(k1 / 2))
              )
              if len(
                  np.intersect1d(
                      candidate_k_reciprocal_index, k_reciprocal_index
                  )
              ) > 2.0 / 3 * len(candidate_k_reciprocal_index):
                  k_reciprocal_expansion_index = np.append(
                      k_reciprocal_expansion_index,
                      candidate_k_reciprocal_index,
                  )
          k_reciprocal_expansion_index = np.unique(
              k_reciprocal_expansion_index
          )
          R.append(k_reciprocal_expansion_index)
  
      gc.collect()  # empty memory
      V = batch_v(feat, R, all_num)
      del R
      gc.collect()  # empty memory
      initial_rank = initial_rank[:, :k2]
  
      # Faster version
      if k2 != 1:
          V_qe = np.zeros_like(V, dtype=np.float16)
          for i in range(all_num):
              V_qe[i, :] = np.mean(V[initial_rank[i], :], axis=0)
          V = V_qe
          del V_qe
      del initial_rank
      gc.collect()  # empty memory
      invIndex = []
      for i in range(all_num):
          invIndex.append(np.where(V[:, i] != 0)[0])
      jaccard_dist = np.zeros((query_num, all_num), dtype=np.float32)
      for i in tqdm(range(query_num)):
          temp_min = np.zeros(shape=[1, all_num], dtype=np.float32)
          indNonZero = np.where(V[i, :] != 0)[0]
          indImages = [invIndex[ind] for ind in indNonZero]
          for j in range(len(indNonZero)):
              temp_min[0, indImages[j]] = temp_min[
                  0, indImages[j]
              ] + np.minimum(
                  V[i, indNonZero[j]], V[indImages[j], indNonZero[j]]
              )
          jaccard_dist[i] = 1 - temp_min / (2.0 - temp_min)
      del V
      gc.collect()  # empty memory
      original_dist = batch_euclidean_distance(
          feat, feat[:query_num, :]
      )
      final_dist = (
          jaccard_dist * (1 - lambda_value)
          + original_dist * lambda_value
      )
      del original_dist
      del jaccard_dist
      final_dist = final_dist[:query_num, query_num:]
      return final_dist
  
imports: import os
  import re
  import cv2
  import gc
  import numpy
  import pandas
  from tqdm import tqdm
  import warnings
label_desc: Use image node_id 18 for calling a global function with example usage: ReRank2(probFea, galFea, k1, k2, lambda_value) and returns: final_dist