How to use the scikit-learn.sklearn.utils.fixes.astype function in scikit-learn

def _partial_fit(self, X, y, alpha, C, loss, learning_rate,
                     n_iter, sample_weight,
                     coef_init, intercept_init):
        X, y = check_X_y(X, y, "csr", copy=False, order='C', dtype=np.float64)
        y = astype(y, np.float64, copy=False)

        n_samples, n_features = X.shape

        self._validate_params()

        # Allocate datastructures from input arguments
        sample_weight = self._validate_sample_weight(sample_weight, n_samples)

        if self.coef_ is None:
            self._allocate_parameter_mem(1, n_features,
                                         coef_init, intercept_init)
        elif n_features != self.coef_.shape[-1]:
            raise ValueError("Number of features %d does not match previous "
                             "data %d." % (n_features, self.coef_.shape[-1]))
        if self.average > 0 and self.average_coef_ is None:
            self.average_coef_ = np.zeros(n_features,