class_docstr: Per-output wrapper.
  
  A copy of the metric is maintained for each output.
  
  Parameters
  ----------
  metric
      A classification or a regression metric.
class_docstr_tok: ["Per-output","wrapper",".","A","copy","of","the","metric","is","maintained","for","each","output",".","Parameters","--","--","--","--","--","metric","A","classification","or","a","regression","metric","."]
code: def __init__(self, metric):
      self._metric = metric
      self.metrics = defaultdict(partial(deepcopy, self._metric))
  
file_path: river/river/metrics/multioutput/per_output.py
imports: from __future__ import annotations
  from collections import defaultdict
  from copy import deepcopy
  from functools import partial
  from river import metrics, utils
  from river.metrics.multioutput.base import MultiOutputMetric
label_desc: Use image node_id 1 to create a new PerOutput object from inherited base classes: MultiOutputMetric, metrics.base with example: obj = PerOutput(metric)