code: def __init__(
      self,
      in_size,
      out_size,
      hidden_size,
      n_layers,
      lamda,
      alpha,
      dropout=0.5,
  ):
      super().__init__()
      self.hidden_size = hidden_size
      self.n_layers = n_layers
      self.lamda = lamda
      self.alpha = alpha
  
      # The GCNII model.
      self.layers = nn.ModuleList()
      self.layers.append(nn.Linear(in_size, hidden_size))
      for _ in range(n_layers):
          self.layers.append(GCNIIConvolution(hidden_size, hidden_size))
      self.layers.append(nn.Linear(hidden_size, out_size))
  
      self.activation = nn.ReLU()
      self.dropout = dropout
  
file_path: dgl/examples/sparse/gcnii.py
imports: import math
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.data import CoraGraphDataset
  from torch.optim import Adam
label_desc: Use image node_id 1 to create a new GCNII object from inherited base classes: nn with example: obj = GCNII(in_size, out_size, hidden_size, n_layers, lamda, alpha, dropout)