code: def set_train_sampler_loader(g, k):
      fanouts = [k - 1 for i in range(args.num_conv + 1)]
      sampler = dgl.dataloading.MultiLayerNeighborSampler(fanouts)
      # fix the number of edges
      train_dataloader = dgl.dataloading.DataLoader(
          g,
          torch.arange(g.num_nodes()),
          sampler,
          batch_size=args.batch_size,
          shuffle=True,
          drop_last=False,
          num_workers=args.num_workers,
      )
      return train_dataloader
  
file_path: dgl/examples/pytorch/hilander/PSS/train_subg_inat.py
imports: import argparse, os, pickle, time
  import random
  import sys
  import dgl
  import numpy
  import torch
  import torch.optim
  from dataset import LanderDataset
  from models import LANDER
label_desc: Use image node_id 2 for calling a global function with example usage: set_train_sampler_loader(g, k) and returns: train_dataloader