How to use the neuroglancer.LocalAnnotationLayer function in neuroglancer

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',
                    layers=['partners', 'synapses'],
                ),
            ])

def _start_flood_fill(self, pos):
        self._stop_flood_fill()
        inf_results = zarr.zeros(
            self.gt_vol.bounds.to_list()[3:], chunks=(64, 64, 64), dtype=np.uint8)
        inf_volume = neuroglancer.LocalVolume(
            data=inf_results, dimensions=self.dimensions)

        with self.viewer.txn() as s:
            s.layers['points'] = neuroglancer.LocalAnnotationLayer(self.dimensions)
            s.layers['inference'] = neuroglancer.ImageLayer(
                source=inf_volume,
                shader='''
void main() {
  float v = toNormalized(getDataValue(0));
  vec4 rgba = vec4(0,0,0,0);
  if (v != 0.0) {
    rgba = vec4(colormapJet(v), 1.0);
  }
  emitRGBA(rgba);
}
''',
            )
        self.flood_fill_event = threading.Event()
        t = threading.Thread(
            target=self._do_flood_fill,