code: def __init__(
      self,
      num_layers,
      input_size,
      hidden_size,
      output_size,
      num_class,
      aggreagte_type,
      direction_option,
  ):
      super(GNNClassifier, self).__init__()
      self.direction_option = direction_option
  
      self.model = GraphSAGE(
          num_layers,
          input_size,
          hidden_size,
          output_size,
          aggreagte_type,
          direction_option=direction_option,
          feat_drop=0.6,
          bias=True,
          activation=nn.ReLU(),
      )
      if self.direction_option == "bi_sep":
          self.fc = nn.Linear(output_size * 2, num_class)
      else:
          self.fc = nn.Linear(output_size, num_class)
  
imports: import argparse
  import os
  import time
  import dgl
  import networkx
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.nn
  from dgl import DGLGraph
  from dgl.data import load_data, register_data_args
  from ...data.data import GraphData
  from ...modules.graph_embedding_learning.graphsage import GraphSAGE
  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(num_layers, input_size, hidden_size, output_size, num_class, aggreagte_type, direction_option)