code: def predict(
      self, graph_mb_data
  ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
      with torch.no_grad():
          logits, supernode_graph_idx = self._logits(graph_mb_data)
          probs = torch.softmax(logits, dim=-1)
          return torch.max(probs, dim=-1) + (supernode_graph_idx,)
  
imports: from typing_extensions import TypedDict
  import numpy
  import torch
  from dpu_utils.mlutils import Vocabulary
  from torch import nn
  from typing import Any, Counter, Dict, Iterator, List, NamedTuple, Optional, Tuple, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel, ModuleWithMetrics
  from ptgnn.baseneuralmodel.utils.data import enforce_not_None
  from ptgnn.neuralmodels.gnn import GnnOutput, GraphData, TensorizedGraphData
  from ptgnn.neuralmodels.gnn.graphneuralnetwork import GraphNeuralNetwork, GraphNeuralNetworkModel
label_desc: Use image node_id 5 for calling the Graph2ClassModule obj's underlying member method code with example usage: obj.predict(graph_mb_data) and returns: unknown