code: def parse_model(self, selection, device) -> BaseAutoModel:
      sel_list = []
      for i in range(self.layer_number):
          sel_list.append(
              [
                  "gat",
                  "gcn",
                  "cos",
                  "const",
                  "gat_sym",
                  "linear",
                  "generalized_linear",
              ][selection[f"attention_{i}"]]
          )
          sel_list.append(
              [
                  "sum",
                  "mean",
                  "max",
                  "mlp",
              ][selection[f"aggregator_{i}"]]
          )
          sel_list.append(
              [
                  "sigmoid",
                  "tanh",
                  "relu",
                  "linear",
                  "softplus",
                  "leaky_relu",
                  "relu6",
                  "elu",
              ][selection[f"act_{i}"]]
          )
          sel_list.append([1, 2, 4, 6, 8, 16][selection[f"head_{i}"]])
          if i < self.layer_number - 1:
              sel_list.append(
                  [4, 8, 16, 32, 64, 128, 256][
                      selection[f"out_channels_{i}"]
                  ]
              )
      sel_list.append(self.output_dim)
      # sel_list = ['const', 'sum', 'relu6', 2, 128, 'gat', 'sum', 'linear', 2, 7]
      model = GraphNet(
          sel_list,
          self.input_dim,
          self.output_dim,
          self.dropout,
          multi_label=False,
          batch_normal=False,
          layers=self.layer_number,
      ).wrap()
      return model
  
imports: import torch
  import typing
  import torch.nn
  import torch.nn.functional
  from .None import register_nas_space
  from .base import BaseSpace, map_nn
  from ...model import BaseAutoModel
  from .operation import act_map
  from ..utils import count_parameters, measure_latency
  from ..backend import *
  from operator import *
  from .operation import *
label_desc: Use image node_id 3 for calling the GraphNasMacroNodeClassificationSpace obj's underlying member method code with example usage: obj.parse_model(selection, device) and returns: model