class_docstr: Measures top-k accuracy of multi-class predictions.
class_docstr_tok: ["Measures","top-k","accuracy","of","multi-class","predictions","."]
code: def value(self):
      result = {}
      correct = torch.stack(self.values, 0)
      for k in self.topk:
          correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
          result[k] = correct_k.mul_(100.0 / correct.size(0))
      return result
  
file_path: CrypTen/examples/meters.py
imports: import torch
label_desc: Use image node_id 4 for calling the AccuracyMeter obj's underlying member method code with example usage: obj.value() and returns: result