code: def extend_minibatch_with(
      self,
      tensorized_datapoint: TensorizedGraph2ClassSample,
      partial_minibatch: Dict[str, Any],
  ) -> bool:
      partial_minibatch["target_classes"].extend(
          tensorized_datapoint.supernode_target_classes
      )
      if self.__tensorize_keep_original_supernode_idx:
          partial_minibatch["original_supernode_idxs"].extend(
              tensorized_datapoint.graph.reference_nodes["supernodes"]
          )
      return self.__gnn_model.extend_minibatch_with(
          tensorized_datapoint.graph, partial_minibatch["graph_mb_data"]
      )
  
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 9 for calling the Graph2Class obj's underlying member method code with example usage: obj.extend_minibatch_with(tensorized_datapoint, partial_minibatch) and returns: self