code: def __init__(
      self,
      num_layers,
      input_size,
      output_size,
      n_etypes,
      n_class,
      direction_option,
      drop=0.0,
      use_edge_weight=False,
  ):
      super(GNNClassifier, self).__init__()
      self.direction_option = direction_option
      self.model = GGNN(
          num_layers,
          input_size,
          output_size,
          n_etypes=n_etypes,
          dropout=drop,
          direction_option=direction_option,
          use_edge_weight=use_edge_weight,
      )
  
      if self.direction_option == "bi_sep":
          self.fc = nn.Linear(2 * output_size, n_class)
      else:
          self.fc = nn.Linear(output_size, n_class)
  
imports: import argparse
  import os
  import time
  import dgl
  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 ...data.data import GraphData
  from ...modules.graph_embedding_learning.ggnn import GGNN
  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, output_size, n_etypes, n_class, direction_option, drop, use_edge_weight)