def fit(self, y):
        """Fit label binarizer

        Parameters
        ----------
        y : numpy array of shape (n_samples,) or (n_samples, n_classes)
            Target values. The 2-d matrix should only contain 0 and 1,
            represents multilabel classification.

        Returns
        -------
        self : returns an instance of self.
        """
        self.y_type_ = type_of_target(y)
        if 'multioutput' in self.y_type_:
            raise ValueError("Multioutput target data is not supported with "
                             "label binarization")
        if _num_samples(y) == 0:
            raise ValueError('y has 0 samples: %r' % y)

        self.sparse_input_ = sp.issparse(y)
        self.classes_ = unique_labels(y)
        return self
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/preprocessing/label.py/LabelBinarizer.fit/original