code: def __init__(self, gnn: GraphNeuralNetwork, num_target_classes: int):
      super().__init__()
      self.__gnn = gnn
      self.__node_to_class = nn.Linear(
          in_features=gnn.output_node_state_dim,
          out_features=num_target_classes,
      )
      nn.init.uniform_(self.__node_to_class.weight)
      nn.init.zeros_(self.__node_to_class.bias)
      self.__loss = nn.CrossEntropyLoss()
  
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 1 to create a new Graph2ClassModule object from inherited base classes: ModuleWithMetrics with example: obj = Graph2ClassModule(gnn, num_target_classes)