code: def forward(self, A_norm, feature):
      H = feature
      H = F.dropout(H, self.dropout, training=self.training)
      H = self.layers[0](H)
      H = self.activation(H)
      H0 = H
  
      # The GCNII convolution forward.
      for i, conv in enumerate(self.layers[1:-1]):
          H = F.dropout(H, self.dropout, training=self.training)
          H = conv(A_norm, H, H0, self.lamda, self.alpha, i + 1)
          H = self.activation(H)
  
      H = F.dropout(H, self.dropout, training=self.training)
      H = self.layers[-1](H)
  
      return H
  
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 2 for calling the GCNII obj's underlying member method code with example usage: obj.forward(A_norm, feature) and returns: H