code: def compute_topk_accuracy(correct_counts_k, total_counts_k):
      """
      Computes the top-k accuracy
      Args:
          correct_counts: Tensor of shape [K], K being the top-k parameter.
          total_counts: Tensor of shape [K], and K being the top-k parameter.
      Returns:
          A list of length `K`, such that k-th index corresponds to top-k accuracy
          over all distributed processes.
      """
      top_k_scores = []
  
      for ki in range(len(correct_counts_k)):
          correct_count = correct_counts_k[ki].item()
          total_count = total_counts_k[ki].item()
          top_k_scores.append(correct_count / float(total_count))
  
      return top_k_scores
  
imports: import logging
  import re
  import string
  from collections import Counter
  from typing import List, Union
  import torch
  from torchmetrics import Metric
label_desc: Use image node_id 1 for calling a global function with example usage: compute_topk_accuracy(correct_counts_k, total_counts_k) and returns: top_k_scores