class_docstr: Empirical coverage percentage for interval predictions.
  
  Parameters
  ----------
  multioutput : string "uniform_average" or "raw_values" determines how        multioutput results will be treated.
  
  score_average : bool, optional, default = True
      specifies whether scores for each quantile should be averaged.
class_docstr_tok: ["Empirical","coverage","percentage","for","interval","predictions",".","Parameters","--","--","--","--","--","multioutput",":","string","``","uniform_average","''","or","``","raw_values","''","determines","how","multioutput","results","will","be","treated",".","score_average",":","bool",",","optional",",","default","=","True","specifies","whether","scores","for","each","quantile","should","be","averaged","."]
code: def __init__(self, multioutput="uniform_average", score_average=True):
      self.score_average = score_average
      self.multioutput = multioutput
      super().__init__(
          score_average=score_average, multioutput=multioutput
      )
  
imports: from logging import warning
  import numpy
  import pandas
  from pandas.api.types import is_numeric_dtype
  from sklearn.utils import check_array, check_consistent_length
  from sktime.datatypes import check_is_scitype, convert, convert_to
  from sktime.performance_metrics.forecasting._classes import BaseForecastingErrorMetric
  from sktime.performance_metrics.forecasting._coerce import _coerce_to_scalar
label_desc: Use image node_id 1 to create a new EmpiricalCoverage object from inherited base classes: _BaseProbaForecastingErrorMetric with example: obj = EmpiricalCoverage(multioutput, score_average)