class_docstr: Poincaré Variational Autoencoder model.
  
  Args:
      model_config (PoincareVAEConfig): The Poincaré 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``.
class_docstr_tok: ["Poincar\u00e9","Variational","Autoencoder","model",".","Args",":","model_config","(","PoincareVAEConfig",")",":","The","Poincar\u00e9","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","``","."]
docstr: The VAE model
  
  Args:
      inputs (BaseDataset): The training dataset with labels
  
  Returns:
      ModelOutput: An instance of ModelOutput containing all the relevant parameters
docstr_tok: ["The","VAE","model","Args",":","inputs","(","BaseDataset",")",":","The","training","dataset","with","labels","Returns",":","ModelOutput",":","An","instance","of","ModelOutput","containing","all","the","relevant","parameters"]
code: def forward(self, inputs: BaseDataset, **kwargs):
      """
      The VAE model
  
      Args:
          inputs (BaseDataset): The training dataset with labels
  
      Returns:
          ModelOutput: An instance of ModelOutput containing all the relevant parameters
  
      """
  
      x = inputs["data"]
  
      encoder_output = self.encoder(x)
  
      if (
          self.model_config.posterior_distribution
          == "riemannian_normal"
      ):
          mu, log_var = (
              encoder_output.embedding,
              encoder_output.log_concentration,
          )
      else:
          mu, log_var = (
              encoder_output.embedding,
              encoder_output.log_covariance,
          )
  
      std = torch.exp(0.5 * log_var)
  
      qz_x = self.posterior(
          loc=mu, scale=std, manifold=self.latent_manifold
      )
      z = qz_x.rsample(torch.Size([1]))
  
      recon_x = self.decoder(z.squeeze(0))["reconstruction"]
  
      loss, recon_loss, kld = self.loss_function(recon_x, x, z, qz_x)
  
      output = ModelOutput(
          recon_loss=recon_loss,
          reg_loss=kld,
          loss=loss,
          recon_x=recon_x,
          z=z.squeeze(0),
      )
  
      return output
  
file_path: benchmark_VAE/src/pythae/models/pvae/pvae_model.py
imports: import warnings
  from typing import Optional
  import numpy
  import torch
  import torch.nn
  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, Encoder_VAE_MLP
  from ..vae import VAE
  from .pvae_config import PoincareVAEConfig
  from .pvae_utils import PoincareBall, RiemannianNormal, WrappedNormal
label_desc: Use image node_id 2 for calling the PoincareVAE obj's underlying member method code with example usage: obj.forward(inputs) and returns: output