class_docstr: Exact match score.
  
  This is the most strict multi-label metric, defined as the number of
  samples that have all their labels correctly classified, divided by the
  total number of samples.
  
  Parameters
  ----------
  cm
      This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a
      confusion matrix reduces the amount of storage and computation time.
  
  Examples
  --------
  
  >>> from river import metrics
  
  >>> y_true = [
  ...     {0: False, 1: True, 2: True},
  ...     {0: True, 1: True, 2: False},
  ...     {0: True, 1: True, 2: False},
  ... ]
  
  >>> y_pred = [
  ...     {0: True, 1: True, 2: True},
  ...     {0: True, 1: False, 2: False},
  ...     {0: True, 1: True, 2: False},
  ... ]
  
  >>> metric = metrics.multioutput.ExactMatch()
  >>> for yt, yp in zip(y_true, y_pred):
  ...     metric.update(yt, yp)
  
  >>> metric
  ExactMatch: 33.33%
code: def _eval(self, y_true, y_pred):
      return y_true == y_pred
  
imports: from __future__ import annotations
  from river import metrics
  from river.metrics.multioutput.base import MultiOutputClassificationMetric
label_desc: Use image node_id 1 for calling the ExactMatch obj's underlying member method code with example usage: obj._eval(y_true, y_pred) and returns: unknown