class_docstr: Vanilla Autoencoder model.
  
  Args:
      model_config (AEConfig): The Autoencoder configuration setting the main parameters of the
          model.
  
      encoder (BaseEncoder): An instance of BaseEncoder (inheriting from `torch.nn.Module` which
          plays the role of encoder. This argument allows you to use your own neural networks
          architectures if desired. If None is provided, a simple Multi Layer Preception
          (https://en.wikipedia.org/wiki/Multilayer_perceptron) is used. Default: None.
  
      decoder (BaseDecoder): An instance of BaseDecoder (inheriting from `torch.nn.Module` which
          plays the role of decoder. This argument allows you to use your own neural networks
          architectures if desired. If None is provided, a simple Multi Layer Preception
          (https://en.wikipedia.org/wiki/Multilayer_perceptron) is used. Default: None.
  
  .. note::
      For high dimensional data we advice you to provide you own network architectures. With the
      provided MLP you may end up with a ``MemoryError``.
code: def loss_function(self, recon_x, x):
      MSE = F.mse_loss(
          recon_x.reshape(x.shape[0], -1),
          x.reshape(x.shape[0], -1),
          reduction="none",
      ).sum(dim=-1)
      return MSE.mean(dim=0)
  
file_path: benchmark_VAE/src/pythae/models/ae/ae_model.py
imports: import os
  from typing import Optional
  import torch.nn.functional
  from ...data.datasets import BaseDataset
  from ..base import BaseAE
  from ..base.base_utils import ModelOutput
  from ..nn import BaseDecoder, BaseEncoder
  from ..nn.default_architectures import Encoder_AE_MLP
  from .ae_config import AEConfig
label_desc: Use image node_id 3 for calling the AE obj's underlying member method code with example usage: obj.loss_function(recon_x, x) and returns: MSE