code: def mine_bitext(
      x,
      y,
      src_inds,
      trg_inds,
      mode="mine",
      retrieval="max",
      margin="ratio",
      threshold=0,
      neighborhood=4,
      use_gpu=False,
      dist="cosine",
      use_shift_embeds=False,
  ):
      # Adapted From: https://github.com/google-research/xtreme/blob/
      #               522434d1aece34131d997a97ce7e9242a51a688a/third_party/utils_retrieve.py
      import faiss
  
      src_orig_inds = np.arange(len(x))
      trg_orig_inds = np.arange(len(y))
      out_ls = []
  
      x = unique_embeddings(x, src_inds)
      y = unique_embeddings(y, trg_inds)
      if dist == "cosine":
          faiss.normalize_L2(x)
          faiss.normalize_L2(y)
  
      if use_shift_embeds:
          x2y, y2x = shift_embeddings(x, y)
  
      # calculate knn in both directions
      if retrieval != "bwd":
          if use_shift_embeds:
              # project x to y space, and search k-nn ys for each x
              x2y_sim, x2y_ind = knn(
                  x2y, y, min(y.shape[0], neighborhood), use_gpu, dist
              )
              x2y_mean = x2y_sim.mean(axis=1)
          else:
              x2y_sim, x2y_ind = knn(
                  x, y, min(y.shape[0], neighborhood), use_gpu, dist
              )
              x2y_mean = x2y_sim.mean(axis=1)
  
      if retrieval != "fwd":
          if use_shift_embeds:
              y2x_sim, y2x_ind = knn(
                  y2x, x, min(x.shape[0], neighborhood), use_gpu, dist
              )
              y2x_mean = y2x_sim.mean(axis=1)
          else:
              y2x_sim, y2x_ind = knn(
                  y, x, min(x.shape[0], neighborhood), use_gpu, dist
              )
              y2x_mean = y2x_sim.mean(axis=1)
  
      # margin function
      if margin == "absolute":
          # noinspection PyUnusedLocal
          def margin(a, b):
              return a
  
      elif margin == "distance":
  
          def margin(a, b):
              return a - b
  
      else:  # margin == 'ratio':
  
          def margin(a, b):
              return a / b
  
      if mode == "search":
          scores = score_candidates(
              x, y, x2y_ind, x2y_mean, y2x_mean, margin
          )
          best = x2y_ind[np.arange(x.shape[0]), scores.argmax(axis=1)]
  
          for i in src_inds:
              out_ls.append(trg_orig_inds[best[i]])
  
      elif mode == "score":
          for i, j in zip(src_inds, trg_inds):
              s = score(x[i], y[j], x2y_mean[i], y2x_mean[j], margin)
              out_ls.append((s, src_orig_inds[i], trg_orig_inds[j]))
  
      elif mode == "mine":
          if use_shift_embeds:
              fwd_scores = score_candidates(
                  x2y, y, x2y_ind, x2y_mean, y2x_mean, margin
              )
              bwd_scores = score_candidates(
                  y2x, x, y2x_ind, y2x_mean, x2y_mean, margin
              )
          else:
              fwd_scores = score_candidates(
                  x, y, x2y_ind, x2y_mean, y2x_mean, margin
              )
              bwd_scores = score_candidates(
                  y, x, y2x_ind, y2x_mean, x2y_mean, margin
              )
          fwd_best = x2y_ind[
              np.arange(x.shape[0]), fwd_scores.argmax(axis=1)
          ]
          bwd_best = y2x_ind[
              np.arange(y.shape[0]), bwd_scores.argmax(axis=1)
          ]
          if retrieval == "fwd":
              for i, j in enumerate(fwd_best):
                  out_ls.append(
                      (
                          fwd_scores[i].max(),
                          src_orig_inds[i],
                          trg_orig_inds[j],
                      )
                  )
          if retrieval == "bwd":
              for j, i in enumerate(bwd_best):
                  out_ls.append(
                      (
                          bwd_scores[j].max(),
                          src_orig_inds[i],
                          trg_orig_inds[j],
                      )
                  )
          if retrieval == "intersect":
              for i, j in enumerate(fwd_best):
                  if bwd_best[j] == i:
                      out_ls.append(
                          (
                              fwd_scores[i].max(),
                              src_orig_inds[i],
                              trg_orig_inds[j],
                          )
                      )
          if retrieval == "max":
              indices = np.stack(
                  (
                      np.concatenate((np.arange(x.shape[0]), bwd_best)),
                      np.concatenate((fwd_best, np.arange(y.shape[0]))),
                  ),
                  axis=1,
              )
              # noinspection PyArgumentList
              scores = np.concatenate(
                  (fwd_scores.max(axis=1), bwd_scores.max(axis=1))
              )
              seen_src, seen_trg = set(), set()
              for i in np.argsort(-scores):
                  src_ind, trg_ind = indices[i]
                  if (
                      src_ind not in seen_src
                      and trg_ind not in seen_trg
                  ):
                      seen_src.add(src_ind)
                      seen_trg.add(trg_ind)
                      if scores[i] > threshold:
                          out_ls.append(
                              (
                                  scores[i],
                                  src_orig_inds[src_ind],
                                  trg_orig_inds[trg_ind],
                              )
                          )
      return out_ls
  
imports: import hashlib
  import numpy
  import torch
  from dataclasses import dataclass
  from typing import List
  from jiant.tasks.core import BaseExample, BaseTokenizedExample, BaseDataRow, BatchMixin, Task, TaskTypes
  from jiant.tasks.lib.templates.shared import construct_single_input_tokens_and_segment_ids, create_input_set_from_tokens_and_segments
  from jiant.utils.python.io import read_file, read_file_lines
label_desc: Use image node_id 1 for calling a global function with example usage: mine_bitext(x, y, src_inds, trg_inds, mode, retrieval, margin, threshold, neighborhood, use_gpu, dist, use_shift_embeds) and returns: out_ls, a, unknown, unknown