: scalar, optional
        Value used to fill in the masked values when necessary.
        If None, a default based on the data-type is used.
    keep_mask : bool, optional
        Whether to combine `mask` with the mask of the input data, if any
        (True), or to use only `mask` for the output (False). Default is True.
    hard_mask : bool, optional
        Whether to use a hard mask or not. With a hard mask, masked values
        cannot be unmasked. Default is False.
    shrink : bool, optional
        Whether to force compression of an empty mask. Default is True.
    order : {'C', 'F', 'A'}, optional
        Specify the order of the array.  If order is 'C', then the array
        will be in C-contiguous order (last-index varies the fastest).
        If order is 'F', then the returned array will be in
        Fortran-contiguous order (first-index varies the fastest).
        If order is 'A' (default), then the returned array may be
        in any order (either C-, Fortran-contiguous, or even discontiguous),
        unless a copy is required, in which case it will be C-contiguous.

    Examples
    --------

    The ``mask`` can be initialized with an array of boolean values
    with the same shape as ``data``.

    >>> data = np.arange(6).reshape((2, 3))
    >>> np.ma.MaskedArray(data, mask=[[False, True, False],
    ...                               [False, False, True]])
    masked_array(
      data=[[0, --, 2],
            [3, 4, --]],
      mask=[[False,  True, False],
            [False, False,  True]],
      fill_value=999999)

    Alternatively, the ``mask`` can be initialized to homogeneous boolean
    array with the same shape as ``data`` by passing in a scalar
    boolean value:

    >>> np.ma.MaskedArray(data, mask=False)
    masked_array(
      data=[[0, 1, 2],
            [3, 4, 5]],
      mask=[[False, False, False],
            [False, False, False]],
      fill_value=999999)

    >>> np.ma.MaskedArray(data, mask=True)
    masked_array(
      data=[[--, --, --],
            [--, --, --]],
      mask=[[ True,  True,  True],
            [ True,  True,  True]],
      fill_value=999999,
      dtype=int64)

    .. note::
        The recommended practice for initializing ``mask`` with a scalar
        boolean value is to use ``True``/``False`` rather than
        ``np.True_``/``np.False_``. The reason is :attr:`nomask`
        is represented internally as ``np.False_``.

        >>> np.False_ is np.ma.nomask
        True

    é