code: def create_var_misuse_gnn_model(hidden_state_size: int = 64):
      def create_mlp_mp_layers(num_edges: int):
          mlp_mp_constructor = lambda: MlpMessagePassingLayer(
              input_state_dimension=hidden_state_size,
              message_dimension=hidden_state_size,
              output_state_dimension=hidden_state_size,
              num_edge_types=num_edges,
              message_aggregation_function="max",
              dropout_rate=0.1,
          )
          mlp_mp_after_res_constructor = lambda: MlpMessagePassingLayer(
              input_state_dimension=2 * hidden_state_size,
              message_dimension=2 * hidden_state_size,
              output_state_dimension=hidden_state_size,
              num_edge_types=num_edges,
              message_aggregation_function="max",
              dropout_rate=0.1,
          )
          r1 = ConcatResidualLayer(hidden_state_size)
          r2 = ConcatResidualLayer(hidden_state_size)
          return [
              r1.pass_through_dummy_layer(),
              mlp_mp_constructor(),
              mlp_mp_constructor(),
              mlp_mp_constructor(),
              r1,
              mlp_mp_after_res_constructor(),
              r2.pass_through_dummy_layer(),
              mlp_mp_constructor(),
              mlp_mp_constructor(),
              mlp_mp_constructor(),
              r2,
              mlp_mp_after_res_constructor(),
          ]
  
      def create_ggnn_mp_layers(num_edges: int):
          ggnn_mp = GatedMessagePassingLayer(
              state_dimension=hidden_state_size,
              message_dimension=hidden_state_size,
              num_edge_types=num_edges,
              message_aggregation_function="sum",
              dropout_rate=0.01,
          )
          r1 = MeanResidualLayer(hidden_state_size)
          r2 = MeanResidualLayer(hidden_state_size)
          global_update = lambda: GruGlobalStateUpdate(
              global_graph_representation_module=WeightedSumVarSizedElementReduce(
                  hidden_state_size
              ),
              input_state_size=hidden_state_size,
              summarized_state_size=hidden_state_size,
              dropout_rate=0.1,
          )
          return [
              r1.pass_through_dummy_layer(),
              r2.pass_through_dummy_layer(),
              ggnn_mp,
              ggnn_mp,
              ggnn_mp,
              global_update(),
              ggnn_mp,
              r1,
              ggnn_mp,
              ggnn_mp,
              ggnn_mp,
              global_update(),
              ggnn_mp,
              r2,
          ]
  
      return VarMisuseModel(
          gnn_model=GraphNeuralNetworkModel(
              node_representation_model=CandidateNodeAnnotationModel(
                  embedding_size=hidden_state_size,
                  token_splitting="char",
              ),
              message_passing_layer_creator=create_mlp_mp_layers,
              max_nodes_per_graph=50000,
              max_graph_edges=500000,
              introduce_backwards_edges=True,
              add_self_edges=True,
              stop_extending_minibatch_after_num_nodes=80000,
          ),
      )
  
file_path: ptgnn/ptgnn/implementations/varmisuse/train.py
imports: import random
  import torch
  from docopt import docopt
  from dpu_utils.utils import RichPath, run_and_debug
  from pathlib import Path
  from ptgnn.baseneuralmodel import AbstractNeuralModel, ModelTrainer
  from ptgnn.baseneuralmodel.utils.amlutils import configure_logging, log_run
  from ptgnn.baseneuralmodel.utils.data import LazyDataIterable
  from ptgnn.implementations.varmisuse.candidateannotatedembeddings import CandidateNodeAnnotationModel
  from ptgnn.implementations.varmisuse.varmisuse import VarMisuseModel
  from ptgnn.neuralmodels.gnn import GraphNeuralNetworkModel
  from ptgnn.neuralmodels.gnn.messagepassing import GatedMessagePassingLayer, GruGlobalStateUpdate, MeanResidualLayer, MlpMessagePassingLayer
  from ptgnn.neuralmodels.gnn.messagepassing.residuallayers import ConcatResidualLayer
  from ptgnn.neuralmodels.reduceops import WeightedSumVarSizedElementReduce
label_desc: Use image node_id 1 for calling a global function with example usage: create_var_misuse_gnn_model(hidden_state_size) and returns: VarMisuseModel, list, list