class_docstr: >>> LitAutoEncoder()  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  LitAutoEncoder(
    (encoder): ...
    (decoder): ...
  )
class_docstr_tok: [">",">",">","LitAutoEncoder","(",")","#","doctest",":","+ELLIPSIS","+NORMALIZE_WHITESPACE","LitAutoEncoder","(","(","encoder",")",":","...","(","decoder",")",":","...",")"]
code: def __init__(self, hidden_dim: int = 64, learning_rate=10e-3):
      super().__init__()
      self.save_hyperparameters()
      self.encoder = nn.Sequential(
          nn.Linear(28 * 28, hidden_dim),
          nn.ReLU(),
          nn.Linear(hidden_dim, 3),
      )
      self.decoder = nn.Sequential(
          nn.Linear(3, hidden_dim),
          nn.ReLU(),
          nn.Linear(hidden_dim, 28 * 28),
      )
  
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 1 to create a new LitAutoEncoder object from inherited base classes: LightningModule with example: obj = LitAutoEncoder(hidden_dim, learning_rate)