parameters: (self, y_true, y_pred, sample_weight=None)
return_statement: return metrics_utils.update_confusion_matrix_variables(
          {self._confusion_matrix_cond: self.accumulator},
          y_true,
          y_pred,
          thresholds=self.thresholds,
          thresholds_distributed_evenly=self._thresholds_distributed_evenly,
          sample_weight=sample_weight)
docstring: Accumulates the metric statistics.
  
      Args:
        y_true: The ground truth values.
        y_pred: The predicted values.
        sample_weight: Optional weighting of each example. Defaults to 1. Can be a
          `Tensor` whose rank is either 0, or the same rank as `y_true`, and must
          be broadcastable to `y_true`.
  
      Returns:
        Update op.
docstring_summary: Accumulates the metric statistics.
docstring_tokens: Accumulates the metric statistics .
function: def update_state(self, y_true, y_pred, sample_weight=None):
      """Accumulates the metric statistics.
  
      Args:
        y_true: The ground truth values.
        y_pred: The predicted values.
        sample_weight: Optional weighting of each example. Defaults to 1. Can be a
          `Tensor` whose rank is either 0, or the same rank as `y_true`, and must
          be broadcastable to `y_true`.
  
      Returns:
        Update op.
      """
      return metrics_utils.update_confusion_matrix_variables(
          {self._confusion_matrix_cond: self.accumulator},
          y_true,
          y_pred,
          thresholds=self.thresholds,
          thresholds_distributed_evenly=self._thresholds_distributed_evenly,
          sample_weight=sample_weight)
function_tokens: def update_state ( self , y_true , y_pred , sample_weight = None ) : return metrics_utils . update_confusion_matrix_variables ( { self . _confusion_matrix_cond : self . accumulator } , y_true , y_pred , thresholds = self . thresholds , thresholds_distributed_evenly = self . _thresholds_distributed_evenly , sample_weight = sample_weight )