class_docstr: Multi target regression
  
  This strategy consists of fitting one regressor per target. This is a
  simple strategy for extending regressors that do not natively support
  multi-target regression.
  
  Parameters
  ----------
  estimator : estimator object
      An estimator object implementing `fit` and `predict`.
  
  n_jobs : int, optional, default=1
      The number of jobs to run in parallel for `fit`. If -1,
      then the number of jobs is set to the number of cores.
      When individual estimators are fast to train or predict
      using `n_jobs>1` can result in slower performance due
      to the overhead of spawning processes.
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docstr: Returns the coefficient of determination R^2 of the prediction.
  
  The coefficient R^2 is defined as (1 - u/v), where u is the regression
  sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual
  sum of squares ((y_true - y_true.mean()) ** 2).sum().
  Best possible score is 1.0 and it can be negative (because the
  model can be arbitrarily worse). A constant model that always
  predicts the expected value of y, disregarding the input features,
  would get a R^2 score of 0.0.
  
  Notes
  -----
  R^2 is calculated by weighting all the targets equally using
  `multioutput='uniform_average'`.
  
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
      Test samples.
  
  y : array-like, shape (n_samples) or (n_samples, n_outputs)
      True values for X.
  
  sample_weight : array-like, shape [n_samples], optional
      Sample weights.
  
  Returns
  -------
  score : float
      R^2 of self.predict(X) wrt. y.
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code: def score(self, X, y, sample_weight=None):
      """Returns the coefficient of determination R^2 of the prediction.
  
      The coefficient R^2 is defined as (1 - u/v), where u is the regression
      sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual
      sum of squares ((y_true - y_true.mean()) ** 2).sum().
      Best possible score is 1.0 and it can be negative (because the
      model can be arbitrarily worse). A constant model that always
      predicts the expected value of y, disregarding the input features,
      would get a R^2 score of 0.0.
  
      Notes
      -----
      R^2 is calculated by weighting all the targets equally using
      `multioutput='uniform_average'`.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
          Test samples.
  
      y : array-like, shape (n_samples) or (n_samples, n_outputs)
          True values for X.
  
      sample_weight : array-like, shape [n_samples], optional
          Sample weights.
  
      Returns
      -------
      score : float
          R^2 of self.predict(X) wrt. y.
      """
      # XXX remove in 0.19 when r2_score default for multioutput changes
      from .metrics import r2_score
  
      return r2_score(
          y,
          self.predict(X),
          sample_weight=sample_weight,
          multioutput="uniform_average",
      )
  
imports: import numpy
  from abc import ABCMeta
  from .base import BaseEstimator, clone
  from .base import RegressorMixin, ClassifierMixin
  from .utils import check_array, check_X_y
  from .utils.fixes import parallel_helper
  from .utils.validation import check_is_fitted, has_fit_parameter
  from .externals.joblib import Parallel, delayed
  from .externals import six
label_desc: Use image node_id 2 for calling the MultiOutputRegressor obj's underlying member method code with example usage: obj.score(X, y, sample_weight) and returns: r2_score