code: def forward(self, A_norm, H, H0, lamda, alpha, l):
      beta = math.log(lamda / l + 1)
  
      # Multiply a sparse matrix by a dense matrix.
      H = A_norm @ H
      H = (1 - alpha) * H + alpha * H0
      H = (1 - beta) * H + beta * self.weight(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 GCNIIConvolution obj's underlying member method code with example usage: obj.forward(A_norm, H, H0, lamda, alpha, l) and returns: H