class_docstr: >>> LitAutoEncoder()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  LitAutoEncoder(
    (encoder): ...
    (decoder): ...
  )
class_docstr_tok: [">",">",">","LitAutoEncoder","(",")","#","doctest",":","+ELLIPSIS","+NORMALIZE_WHITESPACE","LitAutoEncoder","(","(","encoder",")",":","...","(","decoder",")",":","...",")"]
code: def configure_optimizers(self):
      return torch.optim.Adam(
          self.parameters(), lr=self.hparams.learning_rate
      )
  
imports: from os import path
  from typing import Optional, Tuple
  import torch
  import torch.nn.functional
  from lightning.pytorch import LightningDataModule, LightningModule, Trainer, callbacks, cli_lightning_logo
  from lightning.pytorch.cli import LightningCLI
  from lightning.pytorch.demos.mnist_datamodule import MNIST
  from lightning.pytorch.utilities import rank_zero_only
  from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE
  from torch import nn
  from torch.utils.data import DataLoader, random_split
label_desc: Use image node_id 7 for calling the LitAutoEncoder obj's underlying member method code with example usage: obj.configure_optimizers() and returns: torch