'%(super)s
This function (norm_gen.fit) uses explicit formulas for the maximum
likelihood estimation of the parameters, so the `optimizer` argument
is ignored.'

@inherit_docstring_from(rv_continuous)
def fit(self, data, **kwds):

    floc = kwds.get('floc', None)
    fscale = kwds.get('fscale', None)
    if ((floc is not None) and (fscale is not None)):
        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(((data - loc) ** 2).mean())
    else:
        scale = fscale
    return (loc, scale)
