@inherit_docstring_from(rv_continuous)
    def fit(self, data, **kwds):
        """%(super)s
        This function (norm_gen.fit) uses explicit formulas for the maximum
        likelihood estimation of the parameters, so the `optimizer` argument
        is ignored.
        """
        floc = kwds.get('floc', None)
        fscale = kwds.get('fscale', None)

        if floc is not None and fscale is not None:
            # This check is for consistency with `rv_continuous.fit`.
            # Without this check, this function would just return the
            # parameters that were given.
            raise ValueError("All parameters fixed. There is nothing to "
                             "optimize.")

        data = np.asarray(data)

        if floc is None:
            loc = data.mean()
        else:
            loc = floc

        if fscale is None:
            scale = np.sqrt(((loc - data)**2).mean())
        else:
            scale = fscale

        return loc, scale
dataset/ETHPy150Open scipy/scipy/scipy/stats/_continuous_distns.py/norm_gen.fit/swapped operands of `-`