code: def forward(self, data):
      # x, edges = data.x, data.edge_index  # x [2708,1433] ,[2, 10556]
      x = bk_feat(data)
  
      x = F.dropout(x, p=self.dropout, training=self.training)
      pprev_, prev_ = self.preproc0(x), self.preproc1(x)
      prev_nodes_out = [pprev_, prev_]
      for layer in range(2, self.layer_number + 2):
          node_in = getattr(self, f"in_{layer}")(prev_nodes_out)
          op = getattr(self, f"op_{layer}")
          node_out = bk_gconv(op, data, node_in)
          prev_nodes_out.append(node_out)
      act = getattr(self, "act")
      if len(self.con_ops) > 1:
          con = getattr(self, "concat")()
      elif len(self.con_ops) == 1:
          con = self.con_ops[0]
      else:
          con = "concat"
  
      states = prev_nodes_out
      if con == "concat":
          x = torch.cat(states[2:], dim=1)
      else:
          tmp = states[2]
          for i in range(3, len(states)):
              if con == "add":
                  tmp = torch.add(tmp, states[i])
              elif con == "product":
                  tmp = torch.mul(tmp, states[i])
          x = tmp
      x = act(x)
      if con == "concat":
          x = self.classifier1(x)
      else:
          x = self.classifier2(x)
      return F.log_softmax(x, dim=1)
  
imports: import typing
  import torch
  import torch.nn.functional
  from nni.nas.pytorch import mutables
  from .None import register_nas_space
  from .base import BaseSpace
  from ...model import BaseAutoModel
  from ..utils import count_parameters, measure_latency
  from torch import nn
  from .operation import act_map, gnn_map
  from ..backend import *
label_desc: Use image node_id 3 for calling the GraphNasNodeClassificationSpace obj's underlying member method code with example usage: obj.forward(data) and returns: F