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 loss_function(self, recon_x, x, loc, concentration, z):
      if self.model_config.reconstruction_loss == "mse":
          recon_loss = 0.5 * F.mse_loss(
              recon_x.reshape(x.shape[0], -1),
              x.reshape(x.shape[0], -1),
              reduction="none",
          ).sum(dim=-1)
  
      elif self.model_config.reconstruction_loss == "bce":
          recon_loss = F.binary_cross_entropy(
              recon_x.reshape(x.shape[0], -1),
              x.reshape(x.shape[0], -1),
              reduction="none",
          ).sum(dim=-1)
  
      KLD = self._compute_kl(
          m=loc.shape[-1], concentration=concentration
      )
  
      return (
          (recon_loss + KLD).mean(dim=0),
          recon_loss.mean(dim=0),
          KLD.mean(dim=0),
      )
  
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 3 for calling the SVAE obj's underlying member method code with example usage: obj.loss_function(recon_x, x, loc, concentration, z) and returns: unknown,  recon_loss,  KLD