class_docstr: The Brain implementation for Tacotron2
docstr: Fits a single batch and applies annealing
  
  Arguments
  ---------
  batch: tuple
      a training batch
  
  Returns
  -------
  loss: torch.Tensor
      detached loss
docstr_tok: ["Fits","a","single","batch","and","applies","annealing","Arguments","--","--","--","--","-","batch",":","tuple","a","training","batch","Returns","--","--","--","-","loss",":","torch.Tensor","detached","loss"]
code: def fit_batch(self, batch):
      """Fits a single batch and applies annealing
  
      Arguments
      ---------
      batch: tuple
          a training batch
  
      Returns
      -------
      loss: torch.Tensor
          detached loss
      """
      result = super().fit_batch(batch)
      self.hparams.lr_annealing(self.optimizer)
      return result
  
imports: import torch
  import speechbrain
  import sys
  import logging
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.text_to_sequence import text_to_sequence
  from speechbrain.utils.data_utils import scalarize
  import os
  from speechbrain.inference.vocoders import HIFIGAN
  import torchaudio
label_desc: Use image node_id 3 for calling the Tacotron2Brain obj's underlying member method code with example usage: obj.fit_batch(batch) and returns: result