code: def __init__(
      self,
      input_size,
      hidden_size,
      output_size,
      feat_drop=0,
      layer_drop=0.5,
      activation=F.relu,
  ):
      super(GNNClassifier, self).__init__()
      self.feat_drop = feat_drop
      self.layer_drop = layer_drop
      self.activation = activation
      self.gnn_layer1 = GNNLayer(input_size, hidden_size)
      self.gnn_layer2 = GNNLayer(hidden_size, output_size)
  
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 1 to create a new GNNClassifier object from inherited base classes: nn with example: obj = GNNClassifier(input_size, hidden_size, output_size, feat_drop, layer_drop, activation)