code: def evaluate_emb(emb, labels):
      
      d_mat = get_distance_matrix(emb)
      d_mat = d_mat.asnumpy()
      labels = labels.asnumpy()
  
      names = []
      accs = []
      for k in [1, 2, 4, 8, 16]:
          names.append('Recall@%d' % k)
          correct, cnt = 0.0, 0.0
          for i in range(emb.shape[0]):
              d_mat[i, i] = 1e10
              nns = argpartition(d_mat[i], k)[:k]
              if any(labels[i] == labels[nn] for nn in nns):
                  correct += 1
              cnt += 1
          accs.append(correct/cnt)
      return names, accs
docs: Evaluate embeddings based on Recall@k.