code: def load_data(dataset):
      global n_node_feats, n_classes
  
      data = DglNodePropPredDataset(name=dataset)
      evaluator = Evaluator(name=dataset)
  
      splitted_idx = data.get_idx_split()
      train_idx, val_idx, test_idx = (
          splitted_idx["train"],
          splitted_idx["valid"],
          splitted_idx["test"],
      )
      graph, labels = data[0]
  
      n_node_feats = graph.ndata["feat"].shape[1]
      n_classes = (labels.max() + 1).item()
  
      return graph, labels, train_idx, val_idx, test_idx, evaluator
  
file_path: dgl/examples/pytorch/ogb/ogbn-arxiv/correct_and_smooth.py
imports: import argparse
  import glob
  import numpy
  import torch
  import torch.nn.functional
  from dgl import function
  from ogb.nodeproppred import DglNodePropPredDataset, Evaluator
label_desc: Use image node_id 1 for calling a global function with example usage: load_data(dataset) and returns: graph,  labels,  train_idx,  val_idx,  test_idx,  evaluator