code: def __init__(
      self, in_feats, n_hidden, n_classes, n_layers, activation, dropout
  ):
      super(GCN, self).__init__()
      self.layers = nn.ModuleList()
      # input layer
      self.layers.append(
          GraphConv(in_feats, n_hidden, activation=activation)
      )
      # hidden layers
      for i in range(n_layers - 1):
          self.layers.append(
              GraphConv(n_hidden, n_hidden, activation=activation)
          )
      # output layer
      self.layers.append(GraphConv(n_hidden, n_classes))
      self.dropout = nn.Dropout(p=dropout)
  
file_path: dgl/benchmarks/benchmarks/model_speed/bench_gcn_udf.py
imports: import time
  import dgl
  import torch
  import torch.nn
  import torch.nn.functional
  from ..None import utils
label_desc: Use image node_id 1 to create a new GCN object from inherited base classes: nn with example: obj = GCN(in_feats, n_hidden, n_classes, n_layers, activation, dropout)