f9aea094643fb3240193b0bc0f1908d5b84d82ab,dask_image/ndmeasure/__init__.py,,center_of_mass,#Any#Any#Any#,24

Before Change

    // This only matters if index is some array.
    index = index.T

    input_mtch_sum = sum(input, labels, index)

    input_wt_mtch_sum = []
    for i in _pycompat.irange(input.ndim):
        sl = input.ndim * [None]
        sl[i] = slice(None)
        sl = tuple(sl)

        input_i = dask.array.arange(input.shape[i], chunks=input.chunks[i])
        input_wt = input * input_i[sl]

        input_wt_mtch_sum.append(sum(input_wt, labels, index))

    input_wt_mtch_sum = dask.array.stack(input_wt_mtch_sum, axis=-1)

    com_lbl = input_wt_mtch_sum / input_mtch_sum[..., None]

    return com_lbl

After Change

    // This only matters if index is some array.
    index = index.T

    type_mapping = collections.OrderedDict([
        (("%i" % i), input.dtype) for i in _pycompat.irange(input.ndim)
    ])
    out_dtype = numpy.dtype(list(type_mapping.items()))

    default_1d = numpy.full((1,), numpy.nan, dtype=out_dtype)

    func = functools.partial(
        _utils._center_of_mass, shape=input.shape, dtype=out_dtype
    )
    com_lbl = labeled_comprehension(
        input, labels, index,
        func, out_dtype, default_1d[0], pass_positions=True
    )

    com_lbl = dask.array.stack([com_lbl[k] for k in type_mapping], axis=-1)

Instances