code: def load_data():
      dataset = CoraGraphDataset()
  
      graph = dataset[0]
      # The paper created a hypergraph from the original graph. For each node in
      # the original graph, a hyperedge in the hypergraph is created to connect
      # its neighbors and itself. In this case, the incidence matrix of the
      # hypergraph is the same as the adjacency matrix of the original graph (with
      # self-loops).
      # We follow the paper and assume that the rows of the incidence matrix
      # are for nodes and the columns are for edges.
      indices = torch.stack(graph.edges())
      H = dglsp.spmatrix(indices)
      H = H + dglsp.identity(H.shape)
  
      X = graph.ndata["feat"]
      Y = graph.ndata["label"]
      train_mask = graph.ndata["train_mask"]
      val_mask = graph.ndata["val_mask"]
      test_mask = graph.ndata["test_mask"]
      return (
          H,
          X,
          Y,
          dataset.num_classes,
          train_mask,
          val_mask,
          test_mask,
      )
  
imports: import argparse
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.data import CoraGraphDataset
  from torchmetrics.functional import accuracy
label_desc: Use image node_id 4 for calling a global function with example usage: load_data() and returns: H,  X,  Y,  dataset,  train_mask,  val_mask,  test_mask