docstr: Called after ``fit_batch()``, meant for calculating and logging metrics.
  
  Arguments
  ---------
  batch : list of torch.Tensors
      Batch of data to use for training. Default implementation assumes
      this batch has two elements: inputs and targets.
  outputs : list or dictionary of torch.Tensors
      Returned value of compute_forward().
  loss : torch.Tensor
      Returned value of compute_objectives().
  should_step : boolean
      Whether optimizer.step() was called or not.
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code: def on_fit_batch_end(self, batch, outputs, loss, should_step):
      """Called after ``fit_batch()``, meant for calculating and logging metrics.
  
      Arguments
      ---------
      batch : list of torch.Tensors
          Batch of data to use for training. Default implementation assumes
          this batch has two elements: inputs and targets.
      outputs : list or dictionary of torch.Tensors
          Returned value of compute_forward().
      loss : torch.Tensor
          Returned value of compute_objectives().
      should_step : boolean
          Whether optimizer.step() was called or not.
      """
      if should_step:
          # anneal model lr every update
          self.hparams.noam_annealing(self.model_optimizer)
  
imports: import sys
  import torch
  import logging
  import pathlib
  from hyperpyyaml import load_hyperpyyaml
  import speechbrain
  from speechbrain.inference.vocoders import UnitHIFIGAN
  from speechbrain.inference.ASR import EncoderDecoderASR
  import tqdm
  import torchaudio
  import numpy
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 6 for calling the S2UT obj's underlying member method code with example usage: obj.on_fit_batch_end(batch, outputs, loss, should_step) without return types