class_docstr: EfficientAd Autoencoder.
  
  Args:
     out_channels (int): number of convolution output channels
     img_size (tuple): size of input images
code: def __init__(
      self, out_channels, padding, img_size, *args, **kwargs
  ) -> None:
      super().__init__(*args, **kwargs)
      self.encoder = Encoder()
      self.decoder = Decoder(out_channels, padding, img_size)
  
imports: from __future__ import annotations
  import logging
  import random
  from enum import Enum
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
  from torchvision import transforms
label_desc: Use image node_id 1 to create a new AutoEncoder object from inherited base classes: nn with example: obj = AutoEncoder(out_channels, padding, img_size)