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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _maybe_null_out(result, axis, mask, min_count=1):
 """ xarray version of pandas.core.nanops._maybe_null_out """

    if hasattr(axis, '__len__'):  # if tuple or list
        raise ValueError('min_count is not available for reduction '
                         'with more than one dimensions.')

    if axis is not None and getattr(result, 'ndim', False):
        null_mask = (mask.shape[axis] - mask.sum(axis) - min_count) < 0
        if null_mask.any():
            dtype, fill_value = dtypes.maybe_promote(result.dtype)
            result = result.astype(dtype)
            result[null_mask] = fill_value

    elif getattr(result, 'dtype', None) not in dtypes.NAT_TYPES:
        null_mask = mask.size - mask.sum()
        if null_mask < min_count:
            result = np.nan

    return result