code: def __init__(self, in_size, out_size, r, hidden_size=256):
      super().__init__()
      # Note that theta and omega refer to the learnable matrices in the
      # original paper correspondingly. The variable r refers to subscript to
      # theta.
      self.theta = nn.ModuleList(
          [nn.Linear(in_size, hidden_size) for _ in range(r + 1)]
      )
      self.omega = nn.Linear(hidden_size * (r + 1), out_size)
  
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 1 to create a new SIGN object from inherited base classes: nn with example: obj = SIGN(in_size, out_size, r, hidden_size)