class_docstr: FactorVAE model.
  
  Args:
      model_config (FactorVAEConfig): 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``.
class_docstr_tok: ["FactorVAE","model",".","Args",":","model_config","(","FactorVAEConfig",")",":","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","``","."]
docstr: This function returns the reconstructions of given input data.
  
  Args:
      inputs (torch.Tensor): The inputs data to be reconstructed of shape [B x input_dim]
      ending_inputs (torch.Tensor): The starting inputs in the interpolation of shape
  
  Returns:
      torch.Tensor: A tensor of shape [B x input_dim] containing the reconstructed samples.
docstr_tok: ["This","function","returns","the","reconstructions","of","given","input","data",".","Args",":","inputs","(","torch.Tensor",")",":","The","inputs","data","to","be","reconstructed","of","shape","[","B","x","input_dim","]","ending_inputs","(","torch.Tensor",")",":","The","starting","inputs","in","the","interpolation","of","shape","Returns",":","torch.Tensor",":","A","tensor","of","shape","[","B","x","input_dim","]","containing","the","reconstructed","samples","."]
code: def reconstruct(self, inputs: torch.Tensor):
      """This function returns the reconstructions of given input data.
  
      Args:
          inputs (torch.Tensor): The inputs data to be reconstructed of shape [B x input_dim]
          ending_inputs (torch.Tensor): The starting inputs in the interpolation of shape
  
      Returns:
          torch.Tensor: A tensor of shape [B x input_dim] containing the reconstructed samples.
      """
      encoder_output = self.encoder(inputs)
  
      mu, log_var = (
          encoder_output.embedding,
          encoder_output.log_covariance,
      )
  
      std = torch.exp(0.5 * log_var)
      z, _ = self._sample_gauss(mu, std)
      recon_x = self.decoder(z)["reconstruction"]
      return recon_x
  
imports: import os
  from typing import Optional
  import torch
  import torch.nn.functional
  from ...customexception import BadInheritanceError
  from ...data.datasets import BaseDataset
  from ..base.base_utils import ModelOutput
  from ..nn import BaseDecoder, BaseDiscriminator, BaseEncoder
  from ..vae import VAE
  from .factor_vae_config import FactorVAEConfig
  from .factor_vae_utils import FactorVAEDiscriminator
label_desc: Use image node_id 5 for calling the FactorVAE obj's underlying member method code with example usage: obj.reconstruct(inputs) and returns: recon_x