code: def __init__(
      self,
      global_stats_path: str,
      sp_model_path: str,
      blist: List[str],
      droprate: float,
      maxsize: int,
  ):
      self.sp_model = spm.SentencePieceProcessor(
          model_file=sp_model_path
      )
      self.valid_data_pipeline = torch.nn.Sequential(
          FunctionalModule(_piecewise_linear_log),
          GlobalStatsNormalization(global_stats_path),
      )
      self.blist = blist
      self.droprate = droprate
      self.maxsize = maxsize
  
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 1 to create a new ValTransform object  with example: obj = ValTransform(global_stats_path, sp_model_path, blist, droprate, maxsize)