//  [python]
def make_new_dset(parent, shape=None, dtype=None, data=None, name=None,
                  chunks=None, compression=None, shuffle=None,
                  fletcher32=None, maxshape=None, compression_opts=None,
                  fillvalue=None, scaleoffset=None, track_times=None,
                  external=None, track_order=None, dcpl=None,
                  allow_unknown_filter=False):
    """ Return a new low-level dataset identifier """

    # Convert data to a C-contiguous ndarray
    if data is not None and not isinstance(data, Empty):
        from . import base
        data = base.array_for_new_object(data, specified_dtype=dtype)

    # Validate shape
    if shape is None:
        if data is None:
            if dtype is None:
                raise TypeError("One of data, shape or dtype must be specified")
            data = Empty(dtype)
        shape = data.shape
    else:
        shape = (shape,) if isinstance(shape, int) else tuple(shape)
        if data is not None and (numpy.product(shape, dtype=numpy.ulonglong) != numpy.product(data.shape, dtype=numpy.ulonglong)):
            raise ValueError("Shape tuple is incompatible with data")

    if isinstance(maxshape, int):
        maxshape = (maxshape,)
    tmp_shape = maxshape if maxshape is not None else shape

    # Validate chunk shape
    if isinstance(chunks, int) and not isinstance(chunks, bool):
        chunks = (chunks,)
    if isinstance(chunks, tuple) and any(
        chunk > dim for dim, chunk in zip(tmp_shape, chunks) if dim is not None
    ):
        errmsg = "Chunk shape must not be greater than data shape in any dimension. "\
                 "{} is not compatible with {}".format(chunks, shape)
        raise ValueError(errmsg)

    if isinstance(dtype, Datatype):
        # Named types are used as-is
        tid = dtype.id
        dtype = tid.dtype  # Following code needs this
    else:
        # Validate dtype
        if dtype is None and data is None:
            dtype = numpy.dtype("=f4")
        elif dtype is None and data is not None:
            dtype = data.dtype
        else:
            dtype = numpy.dtype(dtype)
        tid = h5t.py_create(dtype, logical=1)

    # Legacy
    if any((compression, shuffle, fletcher32, maxshape, scaleoffset)) and chunks is False:
        raise ValueError("Chunked format required for given storage options")

    # Legacy
    if compression is True:
        if compression_opts is None:
            compression_opts = 4
        compression = 'gzip'

    # Legacy
    if compression in _LEGACY_GZIP_COMPRESSION_VALS:
        if compression_opts is not None:
            raise TypeError("Conflict in compression options")
        compression_opts = compression
        compression = 'gzip'
    dcpl = filters.fill_dcpl(
        dcpl or h5p.create(h5p.DATASET_CREATE), shape, dtype,
        chunks, compression, compression_opts, shuffle, fletcher32,
        maxshape, scaleoffset, external, allow_unknown_filter)

    if fillvalue is not None:
        fillvalue = numpy.array(fillvalue)
        dcpl.set_fill_value(fillvalue)

    if track_times in (True, False):
        dcpl.set_obj_track_times(track_times)
    elif track_times is not None:
        raise TypeError("track_times must be either True or False")
    if track_order is True:
        dcpl.set_attr_creation_order(
            h5p.CRT_ORDER_TRACKED | h5p.CRT_ORDER_INDEXED)
    elif track_order is False:
        dcpl.set_attr_creation_order(0)
    elif track_order is not None:
        raise TypeError("track_order must be either True or False")

    if maxshape is not None:
        maxshape = tuple(m if m is not None else h5s.UNLIMITED for m in maxshape)

    if isinstance(data, Empty):
        sid = h5s.create(h5s.NULL)
    else:
        sid = h5s.create_simple(shape, maxshape)


    dset_id = h5d.create(parent.id, name, tid, sid, dcpl=dcpl)

    if (data is not None) and (not isinstance(data, Empty)):
        dset_id.write(h5s.ALL, h5s.ALL, data)

    return dset_id