code: def cls_encode(self, data) -> torch.Tensor:
      edge_indexes_and_weights: _typing.Union[
          _typing.Sequence[
              _typing.Tuple[
                  torch.LongTensor, _typing.Optional[torch.Tensor]
              ]
          ],
          _typing.Tuple[
              torch.LongTensor, _typing.Optional[torch.Tensor]
          ],
      ] = self.__extract_edge_indexes_and_weights(data)
  
      if (
          not isinstance(edge_indexes_and_weights, tuple)
      ) and isinstance(edge_indexes_and_weights[0], tuple):
          """edge_indexes_and_weights is sequence of (edge_index, edge_weight)"""
          assert len(edge_indexes_and_weights) == len(
              self.__sequential_encoding_layers
          )
          x: torch.Tensor = getattr(data, "x")
          for _edge_index_and_weight, gcn in zip(
              edge_indexes_and_weights,
              self.__sequential_encoding_layers,
          ):
              _temp_data = autogl.data.Data(
                  x=x, edge_index=_edge_index_and_weight[0]
              )
              _temp_data.edge_weight = _edge_index_and_weight[1]
              x = gcn(_temp_data)
          return x
      else:
          """edge_indexes_and_weights is (edge_index, edge_weight)"""
          x = getattr(data, "x")
          for gcn in self.__sequential_encoding_layers:
              _temp_data = autogl.data.Data(
                  x=x, edge_index=edge_indexes_and_weights[0]
              )
              _temp_data.edge_weight = edge_indexes_and_weights[1]
              x = gcn(_temp_data)
          return x
  
imports: import torch
  import torch.nn.functional
  import typing
  from torch_geometric.nn.conv import GCNConv
  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.cls_encode(data) and returns: x, x