code: def score_candidates(
      x, y, candidate_inds, fwd_mean, bwd_mean, margin
  ):
      scores = np.zeros(candidate_inds.shape)
      for i in range(scores.shape[0]):
          for j in range(scores.shape[1]):
              k = candidate_inds[i, j]
              scores[i, j] = score(
                  x[i], y[k], fwd_mean[i], bwd_mean[k], margin
              )
      return scores
  
file_path: sentence-transformers/examples/applications/parallel-sentence-mining/bitext_mining_utils.py
imports: import faiss
  import numpy
  import time
  import gzip
  import lzma
label_desc: Use image node_id 2 for calling a global function with example usage: score_candidates(x, y, candidate_inds, fwd_mean, bwd_mean, margin) and returns: scores