code: def lp_encode(self, data):
      # discard the last layer, only use the layer before
  
      x = data.ndata["feat"]
      for i in range(len(self.convs) - 1):
          if i != 0:
              x = F.dropout(
                  x, p=self.args["dropout"], training=self.training
              )
          x = self.convs[i](data, x)
  
          if i != len(self.convs) - 2:
              x = activate_func(x, self.args["act"])
  
      return x
  
imports: import torch
  import torch.nn.functional
  from typing import Sequence, Optional, Union, Tuple
  from numbers import Real
  from dgl.nn.pytorch.conv import GraphConv
  from dgl import remove_self_loop, add_self_loop
  import autogl.data
  from .None import register_model
  from .base import BaseAutoModel, activate_func, ClassificationSupportedSequentialModel
  from ....utils import get_logger
label_desc: Use image node_id 5 for calling the GCN obj's underlying member method code with example usage: obj.lp_encode(data) and returns: x