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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def score(self, X, y, compute=True):
 """Returns the score on the given data. Parameters X : array-like, shape = [n_samples, n_features] Input data, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] or [n_samples, n_output], optional Target relative to X for classification or regression; None for unsupervised learning. Returns ------- score : float return self.estimator.score(X, y) """

        scoring = self.scoring
        X = self._check_array(X)
        y = self._check_array(y)

        if not scoring:
            if type(self._postfit_estimator).score == sklearn.base.RegressorMixin.score:
                scoring = "r2"
            elif (
                type(self._postfit_estimator).score
                == sklearn.base.ClassifierMixin.score
            ):
                scoring = "accuracy"
        else:
            scoring = self.scoring

        if scoring:
            if not dask.is_dask_collection(X) and not dask.is_dask_collection(y):
                scorer = sklearn.metrics.get_scorer(scoring)
            else:
                scorer = get_scorer(scoring, compute=compute)
            return scorer(self, X, y)
        else:
            return self._postfit_estimator.score(X, y)