AttributeError: 'numpy.float64' object has no attribute '_mask'

self = <numpy.ma.tests.test_regression.TestRegression object at 0x0000000012B264E0>

    def test_var_sets_maskedarray_scalar(self):
        # Issue gh-2757
        a = np.ma.array(np.arange(5), mask=True)
        mout = np.ma.array(-1, dtype=float)
>       a.var(out=mout)

numpy\ma\tests\test_regression.py:64: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
numpy\ma\core.py:5219: in var
    dvar = divide(danom.sum(axis, **kwargs), cnt).view(type(self))
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <numpy.ma.core._DomainedBinaryOperation object at 0x0000000006CA1048>
a = masked, b = 0, args = (), kwargs = {}, da = array(0.)
db = array(0, dtype=int64), result = nan, m = array([ True])
domain = <numpy.ma.core._DomainSafeDivide object at 0x0000000006CA1240>
masked_result = nan

    def __call__(self, a, b, *args, **kwargs):
        "Execute the call behavior."
        # Get the data
        (da, db) = (getdata(a), getdata(b))
        # Get the result
        with np.errstate(divide='ignore', invalid='ignore'):
            result = self.f(da, db, *args, **kwargs)
        # Get the mask as a combination of the source masks and invalid
        m = ~umath.isfinite(result)
        m |= getmask(a)
        m |= getmask(b)
        # Apply the domain
        domain = ufunc_domain.get(self.f, None)
        if domain is not None:
            m |= domain(da, db)
        # Take care of the scalar case first
        if (not m.ndim):
            if m:
                return masked
            else:
                return result
        # When the mask is True, put back da if possible
        # any errors, just abort; impossible to guarantee masked values
        try:
            np.copyto(result, 0, casting='unsafe', where=m)
            # avoid using "*" since this may be overlaid
            masked_da = umath.multiply(m, da)
            # only add back if it can be cast safely
            if np.can_cast(masked_da.dtype, result.dtype, casting='safe'):
                result += masked_da
        except Exception:
            pass
    
        # Transforms to a (subclass of) MaskedArray
        masked_result = result.view(get_masked_subclass(a, b))
>       masked_result._mask = m
E       AttributeError: 'numpy.float64' object has no attribute '_mask'