code: def knn_gpu(x, y, k, mem=5 * 1024 * 1024 * 1024):
      # Adapted From: https://github.com/google-research/xtreme/blob/
      #               522434d1aece34131d997a97ce7e9242a51a688a/third_party/utils_retrieve.py
      import faiss
  
      dim = x.shape[1]
      batch_size = mem // (dim * 4)
      sim = np.zeros((x.shape[0], k), dtype=np.float32)
      ind = np.zeros((x.shape[0], k), dtype=np.int64)
      for xfrom in range(0, x.shape[0], batch_size):
          xto = min(xfrom + batch_size, x.shape[0])
          bsims, binds = [], []
          for yfrom in range(0, y.shape[0], batch_size):
              yto = min(yfrom + batch_size, y.shape[0])
              idx = faiss.IndexFlatIP(dim)
              idx = faiss.index_cpu_to_all_gpus(idx)
              idx.add(y[yfrom:yto])
              bsim, bind = idx.search(x[xfrom:xto], min(k, yto - yfrom))
              bsims.append(bsim)
              binds.append(bind + yfrom)
              del idx
          bsims = np.concatenate(bsims, axis=1)
          binds = np.concatenate(binds, axis=1)
          aux = np.argsort(-bsims, axis=1)
          for i in range(xfrom, xto):
              for j in range(k):
                  sim[i, j] = bsims[i - xfrom, aux[i - xfrom, j]]
                  ind[i, j] = binds[i - xfrom, aux[i - xfrom, j]]
      return sim, ind
  
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 10 for calling a global function with example usage: knn_gpu(x, y, k, mem) and returns: sim,  ind