def fit(self, X, y=None):
        X = check_array(X)
        # Not calling the parent object to fit, to avoid a potential
        # matrix inversion when setting the precision
        if self.assume_centered:
            self.location_ = np.zeros(X.shape[1])
        else:
            self.location_ = X.mean(0)
        covariance = empirical_covariance(
            X, assume_centered=self.assume_centered)
        covariance = shrunk_covariance(covariance, self.shrinkage)
        self._set_covariance(covariance)

        return self


# Ledoit-Wolf estimator
path of this node seen from the other
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/covariance/shrunk_covariance_.py/ShrunkCovariance.fit--crossover with--dataset/ETHPy150Open cournape/Bento/bento/core/node.py/Node.path_from