code: def to(self, *args, **kwargs):
      return Batch(
          x=self.x.to(*args, **kwargs),
          y=self.y.to(*args, **kwargs),
          adjs_t=[adj_t.to(*args, **kwargs) for adj_t in self.adjs_t],
      )
  
imports: import argparse
  import glob
  import os
  import os.path
  import time
  from typing import List, NamedTuple, Optional
  import numpy
  import torch
  import torch.nn.functional
  from ogb.lsc import MAG240MDataset, MAG240MEvaluator
  import pytorch_lightning
  from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything
  from pytorch_lightning.callbacks import ModelCheckpoint
  from torchmetrics import Accuracy
  from torch import Tensor
  from torch.nn import BatchNorm1d, Dropout, Linear, ModuleList, ReLU, Sequential
  from torch.optim.lr_scheduler import StepLR
  from torch_geometric.data import NeighborSampler
  from torch_geometric.nn import GATConv, SAGEConv
  from torch_sparse import SparseTensor
  from tqdm.auto import tqdm
  from root import ROOT
label_desc: Use image node_id 1 for calling the Batch obj's underlying member method code with example usage: obj.to() and returns: Batch