class_docstr: Abstract base class for time series regressors.
  
  The base regressor specifies the methods and method signatures that all
  regressors have to implement. Attributes with a underscore suffix are set in the
  method fit.
  
  Parameters
  ----------
  fit_time_           : integer, time (in milliseconds) for fit to run.
  _class_dictionary   : dictionary mapping classes_ onto integers 0...n_classes_-1.
  _threads_to_use     : number of threads to use in fit as determined by n_jobs.
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docstr: Scores predicted labels against ground truth labels on X.
  
  Parameters
  ----------
  X : sktime compatible time series panel data container, Panel scitype, e.g.,
      pd-multiindex: pd.DataFrame with columns = variables,
      index = pd.MultiIndex with first level = instance indices,
      second level = time indices
      numpy3D: 3D np.array (any number of dimensions, equal length series)
      of shape [n_instances, n_dimensions, series_length]
      or of any other supported Panel mtype
      for list of mtypes, see datatypes.SCITYPE_REGISTER
      for specifications, see examples/AA_datatypes_and_datasets.ipynb
  y : 2D np.array of int, of shape [n_instances, n_dimensions] - regression labels
      for fitting indices correspond to instance indices in X
      or 1D np.array of int, of shape [n_instances] - regression labels for
      fitting indices correspond to instance indices in X
  multioutput : str, optional (default="uniform_average")
      {"raw_values", "uniform_average", "variance_weighted"}, array-like of shape
      (n_outputs,) or None, default="uniform_average".
      Defines aggregating of multiple output scores. Array-like value defines
      weights used to average scores.
  
  Returns
  -------
  float, R-squared score of predict(X) vs y
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code: def score(self, X, y, multioutput="uniform_average") -> float:
      """Scores predicted labels against ground truth labels on X.
  
      Parameters
      ----------
      X : sktime compatible time series panel data container, Panel scitype, e.g.,
          pd-multiindex: pd.DataFrame with columns = variables,
          index = pd.MultiIndex with first level = instance indices,
          second level = time indices
          numpy3D: 3D np.array (any number of dimensions, equal length series)
          of shape [n_instances, n_dimensions, series_length]
          or of any other supported Panel mtype
          for list of mtypes, see datatypes.SCITYPE_REGISTER
          for specifications, see examples/AA_datatypes_and_datasets.ipynb
      y : 2D np.array of int, of shape [n_instances, n_dimensions] - regression labels
          for fitting indices correspond to instance indices in X
          or 1D np.array of int, of shape [n_instances] - regression labels for
          fitting indices correspond to instance indices in X
      multioutput : str, optional (default="uniform_average")
          {"raw_values", "uniform_average", "variance_weighted"}, array-like of shape
          (n_outputs,) or None, default="uniform_average".
          Defines aggregating of multiple output scores. Array-like value defines
          weights used to average scores.
  
      Returns
      -------
      float, R-squared score of predict(X) vs y
      """
      from sklearn.metrics import r2_score
  
      self.check_is_fitted()
  
      return r2_score(
          y, self.predict(X), normalize=True, multioutput=multioutput
      )
  
file_path: sktime/sktime/regression/base.py
imports: import time
  import numpy
  from sktime.base import BasePanelMixin
  from sktime.datatypes import VectorizedDF
  from sktime.utils.sklearn import is_sklearn_transformer
  from sktime.utils.validation import check_n_jobs
label_desc: Use image node_id 5 for calling the BaseRegressor obj's underlying member method code with example usage: obj.score(X, y, multioutput) and returns: r2_score