code: def cls_decode(self, x: torch.Tensor) -> torch.Tensor:
      if self.__apply_normalize:
          x: torch.Tensor = torch.nn.functional.normalize(x, p=2, dim=1)
      return torch.nn.functional.log_softmax(
          self.__linear_transform(x), dim=1
      )
  
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 2 for calling the GraphSAINTMultiOrderAggregationModel obj's underlying member method code with example usage: obj.cls_decode(x) and returns: torch