code: def __init__(self, graph):
      super(OGC, self).__init__()
      self.linear_clf = LinearNeuralNetwork(
          nfeat=graph.ndata["feat"].shape[1],
          nclass=graph.ndata["label"].max().item() + 1,
          bias=False,
      )
  
      self.label = graph.ndata["label"]
      self.label_one_hot = F.one_hot(graph.ndata["label"]).float()
      # LIM trick, else use both train and val set to construct this matrix.
      self.label_idx_mat = dglsp.diag(graph.ndata["train_mask"]).float()
  
      self.test_mask = graph.ndata["test_mask"]
      self.tv_mask = graph.ndata["train_mask"] + graph.ndata["val_mask"]
  
file_path: dgl/examples/pytorch/ogc/ogc.py
imports: import dgl.sparse
  import torch.nn
  import torch.nn.functional
  from utils import LinearNeuralNetwork
label_desc: Use image node_id 1 to create a new OGC object from inherited base classes: nn with example: obj = OGC(graph)