code: def train_dataloader(self):
      return NeighborSampler(
          self.adj_t,
          node_idx=self.train_idx,
          sizes=self.sizes,
          return_e_id=False,
          transform=self.convert_batch,
          batch_size=self.batch_size,
          shuffle=True,
          num_workers=4,
      )
  
imports: import os
  import time
  import glob
  import argparse
  import os.path
  from tqdm.auto import tqdm
  from typing import Optional, List, NamedTuple
  import torch
  from torch import Tensor
  import torch.nn.functional
  from torch.nn import ModuleList, Sequential, Linear, BatchNorm1d, ReLU, Dropout
  from torch.optim.lr_scheduler import StepLR
  import pytorch_lightning
  from torchmetrics import Accuracy
  from pytorch_lightning.callbacks import ModelCheckpoint
  from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything
  from torch_sparse import SparseTensor
  from torch_geometric.nn import SAGEConv, GATConv
  from torch_geometric.data import NeighborSampler
  from ogb.lsc import MAG240MDataset, MAG240MEvaluator
  from root import ROOT
label_desc: Use image node_id 6 for calling the MAG240M obj's underlying member method code with example usage: obj.train_dataloader() and returns: NeighborSampler