code: def extend_minibatch_with(
      self,
      tensorized_datapoint: TensorizedVarMisuseSample,
      partial_minibatch: Dict[str, Any],
  ) -> bool:
      continue_adding = self.__gnn_model.extend_minibatch_with(
          tensorized_datapoint.graph, partial_minibatch["graph_data"]
      )
      partial_minibatch["correct_candidate_idxs"].append(
          tensorized_datapoint.target_candidate_node_idx
          + partial_minibatch["total_num_candidate_nodes"]
      )
      partial_minibatch[
          "total_num_candidate_nodes"
      ] += tensorized_datapoint.num_candidate_nodes
      return continue_adding
  
imports: from typing_extensions import Final, TypedDict
  import re
  import torch
  from dpu_utils.codeutils import split_identifier_into_parts
  from itertools import chain
  from torch import nn
  from torch_scatter import scatter_log_softmax, scatter_max
  from typing import Any, Dict, Iterator, List, NamedTuple, Optional, Tuple, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel, ModuleWithMetrics
  from ptgnn.neuralmodels.gnn.graphneuralnetwork import GraphNeuralNetwork, GraphNeuralNetworkModel
  from ptgnn.neuralmodels.gnn.structs import GnnOutput, GraphData, TensorizedGraphData
label_desc: Use image node_id 7 for calling the VarMisuseModel obj's underlying member method code with example usage: obj.extend_minibatch_with(tensorized_datapoint, partial_minibatch) and returns: continue_adding