code: def mean_topk_accuracy(
      predicted_classes: Tensor, targets: Tensor, k: Sequence[int]
  ) -> Dict[int, Tensor]:
      """Computes the mean accuracy for the specified values of k.
  
      The mean is calculated over the batch dimension.
  
      Args:
          predicted_classes:
              Tensor of shape (batch_size, num_classes) with the predicted classes sorted
              in descending order of confidence.
          targets:
              Tensor of shape (batch_size) containing the target classes.
          k:
              Sequence of integers specifying the values of k for which the accuracy
              should be computed.
  
      Returns:
          Dictionary containing the mean accuracy for each value of k. For example for
          k=(1, 5) the dictionary could look like this: {1: 0.4, 5: 0.6}.
      """
      accuracy = {}
      targets = targets.unsqueeze(1)
      with torch.no_grad():
          for num_k in k:
              correct = torch.eq(predicted_classes[:, :num_k], targets)
              accuracy[num_k] = correct.float().sum() / targets.shape[0]
      return accuracy
  
imports: from typing import Dict, Sequence
  import torch
  from torch import Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: mean_topk_accuracy(predicted_classes, targets, k) and returns: accuracy