code: def cls_encode(self, data) -> torch.Tensor:
      if type(getattr(data, "x")) != torch.Tensor:
          raise TypeError
      if type(getattr(data, "edge_index")) != torch.Tensor:
          raise TypeError
      if (
          getattr(data, "edge_weight", None) is not None
          and type(getattr(data, "edge_weight")) != torch.Tensor
      ):
          raise TypeError
      for encoding_layer in self.__sequential_encoding_layers:
          setattr(data, "x", encoding_layer(data))
      return getattr(data, "x")
  
file_path: AutoGL/autogl/module/model/pyg/graph_saint.py
imports: import typing
  import torch.nn.functional
  from torch_geometric.nn.conv import MessagePassing
  from torch_sparse import SparseTensor, matmul
  from .None import register_model
  from .base import ClassificationModel, ClassificationSupportedSequentialModel
label_desc: Use image node_id 3 for calling the GraphSAINTMultiOrderAggregationModel obj's underlying member method code with example usage: obj.cls_encode(data) and returns: getattr