class_docstr: :math:`\beta`-TCVAE model.
  
  Args:
      model_config (BetaTCVAEConfig): 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``.
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docstr: Compute importance weigth matrix for MSS
  Code from (https://github.com/rtqichen/beta-tcvae/blob/master/vae_quant.py)
docstr_tok: ["Compute","importance","weigth","matrix","for","MSS","Code","from","(","https",":","\/\/github.com\/rtqichen\/beta-tcvae\/blob\/master\/vae_quant.py",")"]
code: def _log_importance_weight_matrix(self, batch_size, dataset_size):
      """Compute importance weigth matrix for MSS
      Code from (https://github.com/rtqichen/beta-tcvae/blob/master/vae_quant.py)
      """
  
      N = dataset_size
      M = batch_size - 1
      strat_weight = (N - M) / (N * M)
      W = torch.Tensor(batch_size, batch_size).fill_(1 / M)
      W.view(-1)[:: M + 1] = 1 / N
      W.view(-1)[1 :: M + 1] = strat_weight
      W[M - 1, 0] = strat_weight
      return W.log()
  
file_path: benchmark_VAE/src/pythae/models/beta_tc_vae/beta_tc_vae_model.py
imports: import os
  from typing import Optional
  import numpy
  import torch
  import torch.nn.functional
  from ...data.datasets import BaseDataset
  from ..base.base_utils import ModelOutput
  from ..nn import BaseDecoder, BaseEncoder
  from ..vae import VAE
  from .beta_tc_vae_config import BetaTCVAEConfig
label_desc: Use image node_id 6 for calling the BetaTCVAE obj's underlying member method code with example usage: obj._log_importance_weight_matrix(batch_size, dataset_size) and returns: W