code: def forward(self, graph):
      node_feat = graph.ndata["node_feat"]
      node_feat = F.dropout(
          node_feat, self.feat_drop, training=self.training
      )
  
      node_emb = self.activation(self.gnn_layer1(graph, node_feat))
      node_emb = F.dropout(
          node_emb, self.layer_drop, training=self.training
      )
  
      logits = self.gnn_layer2(graph, node_emb)
  
      return logits
  
imports: import argparse
  import os
  import time
  import dgl
  import dgl.function
  import networkx
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.nn
  import torch.nn.functional
  from dgl import DGLGraph
  from dgl.data import load_data, register_data_args
  from scipy import sparse
  from ...modules.graph_construction import NodeEmbeddingBasedGraphConstruction, NodeEmbeddingBasedRefinedGraphConstruction
  from ...modules.utils.vocab_utils import VocabModel
  from ..utils import EarlyStopping
label_desc: Use image node_id 2 for calling the GNNClassifier obj's underlying member method code with example usage: obj.forward(graph) and returns: logits