class_docstr: Implement of GCNII
class_docstr_tok: ["Implement","of","GCNII"]
code: def __init__(
      self,
      input_size,
      num_class,
      num_layers=1,
      hidden_size=64,
      dropout=0.6,
      lambda_l=0.5,
      alpha=0.1,
      k_hop=64,
      **kwargs,
  ):
      super(GCNII, self).__init__()
      self.num_class = num_class
      self.num_layers = num_layers
      self.hidden_size = hidden_size
      self.dropout = dropout
      self.hidden_size = hidden_size
      self.lambda_l = lambda_l
      self.alpha = alpha
      self.k_hop = k_hop
  
      self.mlps = nn.LayerList()
      self.mlps.append(nn.Linear(input_size, self.hidden_size))
      self.drop_fn = nn.Dropout(self.dropout)
      for _ in range(self.num_layers - 1):
          self.mlps.append(
              nn.Linear(self.hidden_size, self.hidden_size)
          )
  
      self.output = nn.Linear(self.hidden_size, num_class)
      self.gcnii = pgl.nn.GCNII(
          hidden_size=self.hidden_size,
          activation="relu",
          lambda_l=self.lambda_l,
          alpha=self.alpha,
          k_hop=self.k_hop,
          dropout=self.dropout,
      )
  
imports: import pgl
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new GCNII object from inherited base classes: nn with example: obj = GCNII(input_size, num_class, num_layers, hidden_size, dropout, lambda_l, alpha, k_hop)