code: def get(self):
      try:
          p0 = (
              self.cm.total_true_positives / self.cm.n_samples
          )  # same as accuracy
      except ZeroDivisionError:
          p0 = 0
  
      pe = 0
  
      for c in self.cm.classes:
          estimation_row = self.cm.sum_row[c] / self.cm.n_samples
          estimation_col = self.cm.sum_col[c] / self.cm.n_samples
          pe += estimation_row * estimation_col
  
      try:
          return (p0 - pe) / (1 - pe)
      except ZeroDivisionError:
          return 0.0
  
imports: from __future__ import annotations
  from river import metrics
label_desc: Use image node_id 1 for calling the CohenKappa obj's underlying member method code with example usage: obj.get() and returns: unknown, int