How to use the neuroglancer.SegmentationLayer function in neuroglancer

def display_split_result(graph, agglo_id, cur_eqs, supervoxel_map, split_seeds, image_url,
                         segmentation_url):

    agglo_members = set(graph.get_agglo_members(agglo_id))
    state = neuroglancer.ViewerState()
    state.layers.append(name='image', layer=neuroglancer.ImageLayer(source=image_url))
    state.layers.append(
        name='original',
        layer=neuroglancer.SegmentationLayer(
            source=segmentation_url,
            segments=agglo_members,
        ),
        visible=False,
    )
    state.layers.append(
        name='isolated-supervoxels',
        layer=neuroglancer.SegmentationLayer(
            source=segmentation_url,
            segments=set(x for x, seeds in six.viewitems(supervoxel_map) if len(seeds) > 1),
        ),
        visible=False,
    )
    state.layers.append(
        name='split',
        layer=neuroglancer.SegmentationLayer(

s.layers.append(
                name='original',
                layer=neuroglancer.SegmentationLayer(
                    source=self.segmentation_url,
                    segments=self.agglo_members,
                ),
            )
            s.layers.append(
                name='unused',
                layer=neuroglancer.SegmentationLayer(source=self.segmentation_url,
                                                     ),
                visible=False,
            )
            s.layers.append(
                name='split-result',
                layer=neuroglancer.SegmentationLayer(
                    source=self.segmentation_url,
                    segments=self.agglo_members,
                ),
            )
            s.concurrent_downloads = 256
            self._update_state(s)

        with viewer.config_state.txn() as s:
            s.status_messages['help'] = ('KEYS: ' + ' | '.join('%s=%s' % (key, command)
                                                               for key, command in key_bindings))
            for key, command in key_bindings:
                s.input_event_bindings.viewer[key] = command
                s.input_event_bindings.slice_view[key] = command
                s.input_event_bindings.perspective_view[key] = command
            self._update_config_state(s)

viewer.actions.add('start-fill', self._start_fill_action)
        viewer.actions.add('stop-fill', self._stop_fill_action)
        self.dimensions = neuroglancer.CoordinateSpace(
            names=['x', 'y', 'z'],
            units='nm',
            scales=[8, 8, 8],
        )
        with viewer.config_state.txn() as s:
            s.input_event_bindings.data_view['shift+mousedown0'] = 'start-fill'
            s.input_event_bindings.data_view['keyt'] = 'stop-fill'

        with viewer.txn() as s:
            s.layers['image'] = neuroglancer.ImageLayer(
                source='precomputed://gs://neuroglancer-public-data/flyem_fib-25/image',
            )
            s.layers['ground_truth'] = neuroglancer.SegmentationLayer(
                source='precomputed://gs://neuroglancer-public-data/flyem_fib-25/ground_truth',
            )
            s.layers['ground_truth'].visible = False
            self.flood_fill_event = None

def __init__(self, state_path, bodies, labels, segmentation_url, image_url, num_to_prefetch):
        self.state = State(state_path)
        self.num_to_prefetch = num_to_prefetch
        self.viewer = neuroglancer.Viewer()
        self.bodies = bodies
        self.state.load()
        self.total_voxels = sum(x.num_voxels for x in bodies)
        self.cumulative_voxels = np.cumsum([x.num_voxels for x in bodies])

        with self.viewer.txn() as s:
            s.layers['image'] = neuroglancer.ImageLayer(source=image_url)
            s.layers['segmentation'] = neuroglancer.SegmentationLayer(source=segmentation_url)
            s.show_slices = False
            s.concurrent_downloads = 256
            s.gpu_memory_limit = 2 * 1024 * 1024 * 1024
            s.layout = '3d'

        key_bindings = [
            ['bracketleft', 'prev-index'],
            ['bracketright', 'next-index'],
            ['home', 'first-index'],
            ['end', 'last-index'],
            ['control+keys', 'save'],
        ]
        label_keys = ['keyd', 'keyf', 'keyg', 'keyh']
        for label, label_key in zip(labels, label_keys):
            key_bindings.append([label_key, 'label-%s' % label])

source=segmentation_url,
            segments=agglo_members,
        ),
        visible=False,
    )
    state.layers.append(
        name='isolated-supervoxels',
        layer=neuroglancer.SegmentationLayer(
            source=segmentation_url,
            segments=set(x for x, seeds in six.viewitems(supervoxel_map) if len(seeds) > 1),
        ),
        visible=False,
    )
    state.layers.append(
        name='split',
        layer=neuroglancer.SegmentationLayer(
            source=segmentation_url,
            equivalences=cur_eqs,
            segments=set(cur_eqs[x] for x in agglo_members),
        ))
    for label, component in six.viewitems(split_seeds):
        state.layers.append(
            name='seed%d' % label,
            layer=neuroglancer.PointAnnotationLayer(
                points=[seed['position'] for seed in component],
            ),
        )

    state.show_slices = False
    state.layout = '3d'
    all_seed_points = [
        seed['position'] for component in six.viewvalues(split_seeds) for seed in component

self.viewer.actions.add('prev-batch', lambda s: self.prev_batch())

    with self.viewer.config_state.txn() as s:
      s.input_event_bindings.viewer['keyj'] = 'next-batch'
      s.input_event_bindings.viewer['keyk'] = 'prev-batch'
      s.input_event_bindings.viewer['keyc'] = 'add-ccs'
      s.input_event_bindings.viewer['keya'] = 'clear-splits'
      s.input_event_bindings.viewer['keym'] = 'merge-segments'
      s.input_event_bindings.viewer['bracketleft'] = 'split-dec'
      s.input_event_bindings.viewer['bracketright'] = 'split-inc'
      s.input_event_bindings.viewer['keys'] = 'accept-split'
      s.input_event_bindings.data_view['shift+mousedown0'] = 'add-split'
      s.input_event_bindings.viewer['keyv'] = 'mark-bad'

    with self.viewer.txn() as s:
      s.layers['split'] = neuroglancer.SegmentationLayer(
          source=s.layers['seg'].source)
      s.layers['split'].visible = False

units='nm',
            scales=[8, 8, 8],
        )

        viewer = self.viewer = neuroglancer.Viewer()
        viewer.actions.add('select-custom', self._handle_select)
        with viewer.config_state.txn() as s:
            s.input_event_bindings.data_view['dblclick0'] = 'select-custom'
        with viewer.txn() as s:
            s.projection_orientation = [0.63240087, 0.01582051, 0.05692779, 0.77238464]
            s.dimensions = dimensions
            s.position = [3000, 3000, 3000]
            s.layers['image'] = neuroglancer.ImageLayer(
                source='precomputed://gs://neuroglancer-public-data/flyem_fib-25/image',
            )
            s.layers['ground_truth'] = neuroglancer.SegmentationLayer(
                source='precomputed://gs://neuroglancer-public-data/flyem_fib-25/ground_truth',
            )
            s.layers['partners'] = neuroglancer.SegmentationLayer(
                source='precomputed://gs://neuroglancer-public-data/flyem_fib-25/ground_truth',
            )
            s.layers['synapses'] = neuroglancer.LocalAnnotationLayer(
                dimensions=dimensions,
                linked_segmentation_layer='ground_truth')
            s.layout = neuroglancer.row_layout([
                neuroglancer.LayerGroupViewer(
                    layout='xy',
                    layers=['image', 'ground_truth', 'partners', 'synapses'],
                ),
                neuroglancer.LayerGroupViewer(
                    layout='3d',
                    layers=['ground_truth', 'synapses'],

viewer = self.viewer = neuroglancer.Viewer()
        viewer.actions.add('select-custom', self._handle_select)
        with viewer.config_state.txn() as s:
            s.input_event_bindings.data_view['dblclick0'] = 'select-custom'
        with viewer.txn() as s:
            s.projection_orientation = [0.63240087, 0.01582051, 0.05692779, 0.77238464]
            s.dimensions = dimensions
            s.position = [3000, 3000, 3000]
            s.layers['image'] = neuroglancer.ImageLayer(
                source='precomputed://gs://neuroglancer-public-data/flyem_fib-25/image',
            )
            s.layers['ground_truth'] = neuroglancer.SegmentationLayer(
                source='precomputed://gs://neuroglancer-public-data/flyem_fib-25/ground_truth',
            )
            s.layers['partners'] = neuroglancer.SegmentationLayer(
                source='precomputed://gs://neuroglancer-public-data/flyem_fib-25/ground_truth',
            )
            s.layers['synapses'] = neuroglancer.LocalAnnotationLayer(
                dimensions=dimensions,
                linked_segmentation_layer='ground_truth')
            s.layout = neuroglancer.row_layout([
                neuroglancer.LayerGroupViewer(
                    layout='xy',
                    layers=['image', 'ground_truth', 'partners', 'synapses'],
                ),
                neuroglancer.LayerGroupViewer(
                    layout='3d',
                    layers=['ground_truth', 'synapses'],
                ),
                neuroglancer.LayerGroupViewer(
                    layout='3d',