class_docstr: Expected Calibration Error (ECE)
  
  Args:
      n_cls (int): number of existing target classes
      n_bins (int): number of bins to discretize the uncertainty.
  
  References:
      https://arxiv.org/pdf/1706.04599.pdf
class_docstr_tok: ["Expected","Calibration","Error","(","ECE",")","Args",":","n_cls","(","int",")",":","number","of","existing","target","classes","n_bins","(","int",")",":","number","of","bins","to","discretize","the","uncertainty",".","References",":","https",":","\/\/arxiv.org\/pdf\/1706.04599.pdf"]
code: def reset(self):
      self.tp = np.zeros([self.n_cls, self.n_bins])
      self.samples = np.zeros([self.n_cls, self.n_bins])
      self.conf_agg = np.zeros([self.n_cls, self.n_bins])
  
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 7 for calling the ECE_PerCLs obj's underlying member method code with example usage: obj.reset() without return types