code: def update(self, a, b):
      n = self.num_classes
      if self.mat is None:
          self.mat = torch.zeros(
              (n, n), dtype=torch.int64, device=a.device
          )
      with torch.inference_mode():
          k = (a >= 0) & (a < n)
          inds = n * a[k].to(torch.int64) + b[k]
          self.mat += torch.bincount(inds, minlength=n**2).reshape(
              n, n
          )
  
imports: import datetime
  import errno
  import os
  import time
  from collections import defaultdict, deque
  import torch
  import torch.distributed
label_desc: Use image node_id 2 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.update(a, b) without return types