code: def __init__(
      self,
      num_nodes,
      num_layers,
      input_size,
      hidden_size,
      output_size,
      alpha=0.1,
      lambda_l=0.5,
      dropout=0.0,
      drop_input=True,
      pool_size=None,
      buffer_size=None,
      **kwargs,
  ):
      super().__init__(
          num_nodes, num_layers, hidden_size, pool_size, buffer_size
      )
  
      self.input_size = input_size
      self.output_size = output_size
      self.alpha = alpha
      self.lambda_l = lambda_l
      self.dropout_fn = nn.Dropout(p=dropout)
      self.drop_input = drop_input
  
      self.linears = nn.LayerList()
      self.linears.append(nn.Linear(input_size, hidden_size))
      self.linears.append(nn.Linear(hidden_size, output_size))
  
      self.mlps = nn.LayerList()
      for _ in range(num_layers):
          self.mlps.append(nn.Linear(hidden_size, hidden_size))
  
file_path: PGL/apps/GNNAutoScale/gnn_models/gcnii.py
imports: import numpy
  import paddle
  import pgl
  import paddle.nn
  from .base_model import ScalableGNN
label_desc: Use image node_id 1 to create a new GCNII object from inherited base classes: ScalableGNN with example: obj = GCNII(num_nodes, num_layers, input_size, hidden_size, output_size, alpha, lambda_l, dropout, drop_input, pool_size, buffer_size)