code: def __init__(self, gnn: GraphNeuralNetwork, num_target_classes: int):
      super().__init__()
      self.__gnn = gnn
      self.__output_representation_to_logits = nn.Linear(
          in_features=gnn.output_node_state_dim,
          out_features=num_target_classes,
      )
      torch.nn.init.xavier_uniform(
          self.__output_representation_to_logits.weight
      )
      torch.nn.init.zeros_(self.__output_representation_to_logits.bias)
      self.__output_representation_to_logits
  
imports: import numpy
  import torch
  from torch import nn
  from typing import Any, Dict, Iterable, NamedTuple, Optional, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel, ModuleWithMetrics
  from ptgnn.baseneuralmodel.utils.data import enforce_not_None
  from ptgnn.implementations.ppi.dataloader import PPIGraphSample
  from ptgnn.neuralmodels.gnn.graphneuralnetwork import GraphNeuralNetwork, GraphNeuralNetworkModel
  from ptgnn.neuralmodels.gnn.structs import GnnOutput, GraphData, TensorizedGraphData
label_desc: Use image node_id 1 to create a new PPIClassification object from inherited base classes: ModuleWithMetrics with example: obj = PPIClassification(gnn, num_target_classes)