def masked_all_like(arr):
a = np.empty_like(arr).view(MaskedArray)<EOL>a._mask = np.ones(a.shape, dtype=make_mask_descr(a.dtype))<EOL>return a<EOL>
Empty masked array with the properties of an existing array.

Return an empty masked array of the same shape and dtype as
the array `arr`, where all the data are masked.

Parameters
----------
arr : ndarray
    An array describing the shape and dtype of the required MaskedArray.

Returns
-------
a : MaskedArray
    A masked array with all data masked.

Raises
------
AttributeError
    If `arr` doesn't have a shape attribute (i.e. not an ndarray)

See Also
--------
masked_all : Empty masked array with all elements masked.

Examples
--------
>>> import numpy.ma as ma
>>> arr = np.zeros((2, 3), dtype=np.float32)
>>> arr
array([[ 0.,  0.,  0.],
       [ 0.,  0.,  0.]], dtype=float32)
>>> ma.masked_all_like(arr)
masked_array(data =
 [[-- -- --]
 [-- -- --]],
      mask =
 [[ True  True  True]
 [ True  True  True]],
      fill_value=1e+20)

The dtype of the masked array matches the dtype of `arr`.

>>> arr.dtype
dtype('float32')
>>> ma.masked_all_like(arr).dtype
dtype('float32')