docstr: Called at the beginning of ``fit_batch()``.
  
  Arguments
  ---------
  batch : list of torch.Tensors
      Batch of data to use for training. Default implementation assumes
      this batch has two elements: inputs and targets.
  should_step : boolean
      Whether optimizer.step() was called or not.
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code: def on_fit_batch_start(self, batch, should_step):
      """Called at the beginning of ``fit_batch()``.
  
      Arguments
      ---------
      batch : list of torch.Tensors
          Batch of data to use for training. Default implementation assumes
          this batch has two elements: inputs and targets.
      should_step : boolean
          Whether optimizer.step() was called or not.
      """
      if self.optimizer_step == self.hparams.wav2vec2_freeze_steps:
          logger.warning(
              "speechbrain.lobes.models.huggingface_wav2vec - wav2vec 2.0 is unfrozen."
          )
  
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 5 for calling the S2UT obj's underlying member method code with example usage: obj.on_fit_batch_start(batch, should_step) without return types