class_docstr: Stacked version of GarNet. First three arguments to the constructor must be lists of integers.
  Basically offers no performance advantage, but the configuration is consolidated (and is useful
  when e.g. converting the layer to HLS)
code: def _add_transform_config(self, config):
      config.update(
          {
              "n_propagate": list(
                  ll[0].units for ll in self._transform_layers
              ),
              "n_aggregators": list(
                  ll[1].units for ll in self._transform_layers
              ),
              "n_filters": list(
                  ll[2].units for ll in self._transform_layers
              ),
              "n_sublayers": len(self._transform_layers),
          }
      )
  
imports: import tensorflow.keras
  from qkeras import QActivation, QDense, ternary
label_desc: Use image node_id 5 for calling the GarNetStack obj's underlying member method code with example usage: obj._add_transform_config(config) without return types