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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 convert_numpy_str_to_uint16(data):
 """ Converts a numpy.unicode\_ to UTF-16 in numpy.uint16 form. Convert a ``numpy.unicode_`` or an array of them (they are UTF-32 strings) to UTF-16 in the equivalent array of ``numpy.uint16``. The conversion will throw an exception if any characters cannot be converted to UTF-16. Strings are expanded along rows (across columns) so a 2x3x4 array of 10 element strings will get turned into a 2x30x4 array of uint16's if every UTF-32 character converts easily to a UTF-16 singlet, as opposed to a UTF-16 doublet. Parameters data : numpy.unicode\_ or numpy.ndarray of numpy.unicode\_ The string or array of them to convert. Returns ------- array : numpy.ndarray of numpy.uint16 The result of the conversion. Raises ------ UnicodeEncodeError If a UTF-32 character has no UTF-16 representation. See Also -------- convert_numpy_str_to_uint32 convert_to_numpy_str """

    # An empty string should be an empty uint16
    if data.nbytes == 0:
        return np.uint16([])

    # We need to use the UTF-16 codec for our endianness. Using the
    # right one means we don't have to worry about removing the BOM.
    if sys.byteorder == 'little':
        codec = 'UTF-16LE'
    else:
        codec = 'UTF-16BE'

    # numpy.char.encode can do the conversion element wise. Then, we
    # just have convert to uin16 with the appropriate dimensions. The
    # dimensions are gotten from the shape of the converted data with
    # the number of column increased by the number of words (pair of
    # bytes) in the strings.
    cdata = np.char.encode(np.atleast_1d(data), codec)
    shape = list(cdata.shape)
    shape[-1] *= (cdata.dtype.itemsize // 2)
    return np.ndarray(shape=shape, dtype='uint16',
                      buffer=cdata.tostring())