code: def knn_cpu(x, y, k, dist="cosine"):
      # Adapted From: https://github.com/google-research/xtreme/blob/
      #               522434d1aece34131d997a97ce7e9242a51a688a/third_party/utils_retrieve.py
      import faiss
  
      # x: query, y: database
      dim = x.shape[1]
      if dist == "cosine":
          idx = faiss.IndexFlatIP(dim)
      else:
          idx = faiss.IndexFlatL2(dim)
      idx.add(y)
      sim, ind = idx.search(x, k)
  
      if dist != "cosine":
          sim = 1 / (1 + sim)
      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 11 for calling a global function with example usage: knn_cpu(x, y, k, dist) and returns: sim,  ind