class_docstr: :math:`\mathcal{S}`-VAE model.
  
  Args:
      model_config (SVAEConfig): The Variational 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 __init__(
      self,
      model_config: SVAEConfig,
      encoder: Optional[BaseEncoder] = None,
      decoder: Optional[BaseDecoder] = None,
  ):
      VAE.__init__(
          self,
          model_config=model_config,
          encoder=encoder,
          decoder=decoder,
      )
  
      self.model_name = "SVAE"
  
      if encoder is None:
          encoder = Encoder_SVAE_MLP(model_config)
          self.model_config.uses_default_encoder = True
  
      else:
          self.model_config.uses_default_encoder = False
  
      self.set_encoder(encoder)
  
imports: from typing import Optional
  import numpy
  import torch
  import torch.distributions
  import torch.nn.functional
  from ...data.datasets import BaseDataset
  from ..base.base_utils import ModelOutput
  from ..nn import BaseDecoder, BaseEncoder
  from ..nn.default_architectures import Encoder_SVAE_MLP
  from ..vae import VAE
  from .svae_config import SVAEConfig
  from .svae_utils import ive
label_desc: Use image node_id 1 to create a new SVAE object from inherited base classes: VAE with example: obj = SVAE(model_config, encoder, decoder)