class_docstr: Percentage of interval constraint violations 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: ["Percentage","of","interval","constraint","violations","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","."]
docstr: Retrieve test parameters.
docstr_tok: ["Retrieve","test","parameters","."]
code: def get_test_params(self):
      """Retrieve test parameters."""
      params1 = {}
      return [params1]
  
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 3 for calling the ConstraintViolation obj's underlying member method code with example usage: obj.get_test_params() and returns: list