class_docstr: Maintains a confusion matrix for a given classification problem.
  
  The ConfusionMeter constructs a confusion matrix for a multi-class
  classification problems. It does not support multi-label, multi-class problems:
  for such problems, please use MultiLabelConfusionMeter.
  
  :param k (int): number of classes in the classification problem
  :param normalized (boolean): Determines whether or not the confusion matrix is normalized or not
docstr: Returns:
      Confustion matrix of K rows and K columns, where rows corresponds
      to ground-truth targets and columns corresponds to predicted
      targets.
docstr_tok: ["Returns",":","Confustion","matrix","of","K","rows","and","K","columns",",","where","rows","corresponds","to","ground-truth","targets","and","columns","corresponds","to","predicted","targets","."]
code: def value(self):
      """
      Returns:
          Confustion matrix of K rows and K columns, where rows corresponds
          to ground-truth targets and columns corresponds to predicted
          targets.
      """
      if self.normalized:
          conf = self.conf.astype(np.float32)
          return conf / conf.sum(1).clip(min=1e-12)[:, None]
      else:
          return self.conf
  
imports: from .None import meter
  import numpy
label_desc: Use image node_id 4 for calling the ConfusionMeter obj's underlying member method code with example usage: obj.value() and returns: unknown, self