code: def __call__(self, samples: List):
      features, feature_lengths = _extract_features(
          self.valid_data_pipeline, samples
      )
      targets, target_lengths, biasingwords = _extract_labels(
          self.sp_model, samples
      )
      if self.blist:
          tries, biasingwords = _extract_tries(
              self.sp_model,
              biasingwords,
              self.blist,
              self.droprate,
              self.maxsize,
          )
      else:
          tries = []
      return Batch(
          features, feature_lengths, targets, target_lengths, tries
      )
  
imports: import json
  import math
  import random
  from functools import partial
  from typing import List
  import sentencepiece
  import torch
  import torchaudio
  from data_module import LibriSpeechDataModule
  from lightning import Batch
label_desc: Use image node_id 2 for calling the ValTransform obj's underlying member method code with example usage: obj.__call__(samples) and returns: Batch