code: def forward(self, X_sign):
      results = []
      for i in range(len(X_sign)):
          results.append(self.theta[i](X_sign[i]))
      Z = F.relu(torch.cat(results, dim=1))
      return self.omega(Z)
  
file_path: dgl/examples/sparse/sign.py
imports: 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 SIGN obj's underlying member method code with example usage: obj.forward(X_sign) and returns: self