class_docstr: computes the top first and top five accuracy for the model batch by
  batch.
  Args:
      average (bool): a way to output one single value for metrics
                      that are calculated in several trials.
      topk (tuple): the value of k for calculating the topk accuracy.
class_docstr_tok: ["computes","the","top","first","and","top","five","accuracy","for","the","model","batch","by","batch",".","Args",":","average","(","bool",")",":","a","way","to","output","one","single","value","for","metrics","that","are","calculated","in","several","trials",".","topk","(","tuple",")",":","the","value","of","k","for","calculating","the","topk","accuracy","."]
code: def reset(self):
      self.accuracy = torch.FloatTensor()
  
imports: import dataclasses
  import math
  import warnings
  from collections import defaultdict
  import numpy
  import torch
  from sklearn.metrics import confusion_matrix, auc
  from baal.utils.array_utils import to_prob
label_desc: Use image node_id 2 for calling the Accuracy obj's underlying member method code with example usage: obj.reset() without return types