code: def topk_accuracy(output, target, topk=(1,)):
      """Computes the top-k accuracy for the specified values of k.
  
      Args:
          output (Tensor): output (Tensor): The output of the last layer of the network, before softmax. Shape: (batch_size, class_count).
          target (Tensor): The ground truth label. Shape: (batch_size)
          topk (tuple(int)): Compute accuracy at top-k for the values of k specified in this parameter.
      Returns:
          list(Tensor): A list of tensors of the same length as topk.
          Each tensor consists of boolean variables to show if this prediction ranks top k with each value of k.
          True means the prediction ranks top k and False means not.
          The shape of tensor is batch_size, i.e. the number of predictions.
  
      Examples:
          >>> output = torch.tensor(([0.3, 0.2, 0.1], [0.3, 0.2, 0.1]))
          >>> target = torch.tensor((0, 1))
          >>> top1, top2 = topk_accuracy(output, target, topk=(1, 2)) # get the boolean value
          >>> top1_value = top1.double().mean() # get the top 1 accuracy score
          >>> top2_value = top2.double().mean() # get the top 2 accuracy score
      """
  
      maxk = max(topk)
  
      # returns the k largest elements and their indexes of inputs along a given dimension.
      _, pred = output.topk(maxk, 1, True, True)
  
      pred = pred.t()
      correct = pred.eq(target.view(1, -1).expand_as(pred))
  
      result = []
      for k in topk:
          correct_k = torch.ge(correct[:k].float().sum(0), 1)
          result.append(correct_k)
      return result
  
file_path: pykale/kale/predict/losses.py
imports: import torch
  import torch.nn
  from torch.autograd import grad
  from torch.nn import functional
label_desc: Use image node_id 2 for calling a global function with example usage: topk_accuracy(output, target, topk) and returns: result