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 _sample_von_mises(self, loc, concentration):
      # Generate uniformly on sphere
      v = torch.randn_like(loc[:, 1:])
      v = v / v.norm(dim=-1, keepdim=True)
  
      w = self._acc_rej_steps(m=loc.shape[-1], k=concentration)
  
      w_ = torch.sqrt(torch.clamp(1 - (w**2), 1e-10))
      z = torch.cat((w, w_ * v), dim=-1)
  
      return self._householder_rotation(loc, z)
  
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 5 for calling the SVAE obj's underlying member method code with example usage: obj._sample_von_mises(loc, concentration) and returns: self