class_docstr: Base class shared by Tacotron and Tacotron2
docstr: Capacitron Variational Autoencoder
docstr_tok: ["Capacitron","Variational","Autoencoder"]
code: def compute_capacitron_VAE_embedding(
      self,
      inputs,
      reference_mel_info,
      text_info=None,
      speaker_embedding=None,
  ):
      """Capacitron Variational Autoencoder"""
      (
          VAE_outputs,
          posterior_distribution,
          prior_distribution,
          capacitron_beta,
      ) = self.capacitron_vae_layer(
          reference_mel_info,
          text_info,
          speaker_embedding,  # pylint: disable=not-callable
      )
  
      VAE_outputs = VAE_outputs.to(inputs.device)
      encoder_output = self._concat_speaker_embedding(
          inputs, VAE_outputs
      )  # concatenate to the output of the basic tacotron encoder
      return (
          encoder_output,
          posterior_distribution,
          prior_distribution,
          capacitron_beta,
      )
  
imports: import copy
  from abc import abstractmethod
  from typing import Dict, Tuple
  import torch
  from coqpit import Coqpit
  from torch import nn
  from TTS.tts.layers.losses import TacotronLoss
  from TTS.tts.models.base_tts import BaseTTS
  from TTS.tts.utils.helpers import sequence_mask
  from TTS.tts.utils.speakers import SpeakerManager
  from TTS.tts.utils.synthesis import synthesis
  from TTS.tts.utils.text.tokenizer import TTSTokenizer
  from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
  from TTS.utils.generic_utils import format_aux_input
  from TTS.utils.io import load_fsspec
  from TTS.utils.training import gradual_training_scheduler
label_desc: Use image node_id 16 for calling the BaseTacotron obj's underlying member method code with example usage: obj.compute_capacitron_VAE_embedding(inputs, reference_mel_info, text_info, speaker_embedding) and returns: encoder_output,  posterior_distribution,  prior_distribution,  capacitron_beta