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 forward(self, x):
      return self.model(x)
  
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 2 for calling the ImageNetLightningModel obj's underlying member method code with example usage: obj.forward(x) and returns: self