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 _householder_rotation(self, loc, z):
      e1 = torch.zeros(z.shape[-1]).to(z.device)
      e1[0] = 1
      u = e1 - loc
      u = u / (u.norm(dim=-1, keepdim=True) + 1e-8)
  
      return z - 2 * u * (u * z).sum(dim=-1, keepdim=True)
  
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 6 for calling the SVAE obj's underlying member method code with example usage: obj._householder_rotation(loc, z) and returns: unknown