class_docstr: >>> ImageNetLightningModel(data_path='missing')  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  ImageNetLightningModel(
    (model): ResNet(...)
  )
class_docstr_tok: [">",">",">","ImageNetLightningModel","(","data_path='missing","'",")","#","doctest",":","+ELLIPSIS","+NORMALIZE_WHITESPACE","ImageNetLightningModel","(","(","model",")",":","ResNet","(","...",")",")"]
code: def val_dataloader(self):
      return torch.utils.data.DataLoader(
          self.eval_dataset,
          batch_size=self.batch_size,
          num_workers=self.workers,
          pin_memory=True,
      )
  
imports: import os
  from typing import Optional
  import torch
  import torch.nn.functional
  import torch.nn.parallel
  import torch.optim
  import torch.optim.lr_scheduler
  import torch.utils.data
  import torch.utils.data.distributed
  import torchvision.datasets
  import torchvision.transforms
  from lightning.pytorch import LightningModule
  from lightning.pytorch.callbacks import ModelCheckpoint, TQDMProgressBar
  from lightning.pytorch.cli import LightningCLI
  from lightning.pytorch.strategies import ParallelStrategy
  from lightning.pytorch.utilities.model_helpers import get_torchvision_model
  from torch.utils.data import Dataset
  from torchmetrics import Accuracy
label_desc: Use image node_id 10 for calling the ImageNetLightningModel obj's underlying member method code with example usage: obj.val_dataloader() and returns: torch