code: def preprocess(graph, labels):
      global n_node_feats, n_classes
      n_node_feats = graph.ndata["feat"].shape[1]
      n_classes = (labels.max() + 1).item()
  
      # graph = graph.remove_self_loop().add_self_loop()
      n_node_feats = graph.ndata["feat"].shape[-1]
  
      return graph, labels
  
imports: import argparse
  import math
  import random
  import time
  from collections import OrderedDict
  import dgl.function
  import matplotlib.pyplot
  import numpy
  import torch
  import torch.nn.functional
  import torch.optim
  from dgl.dataloading import DataLoader, MultiLayerFullNeighborSampler, MultiLayerNeighborSampler
  from matplotlib.ticker import AutoMinorLocator, MultipleLocator
  from models import MLP
  from ogb.nodeproppred import DglNodePropPredDataset, Evaluator
  from torch import nn
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: preprocess(graph, labels) and returns: graph,  labels