code: def crop_to_max_size(self, wav, target_size):
      size = len(wav)
      diff = size - target_size
      if diff <= 0:
          return wav, 0
  
      start, end = 0, target_size
      if self.random_crop:
          start = np.random.randint(0, diff + 1)
          end = size - diff + start
      return wav[start:end], start
  
imports: import itertools
  import logging
  import os
  import sys
  from typing import Any, List, Optional, Union
  import numpy
  import torch
  import torch.nn.functional
  from fairseq.data import data_utils
  from fairseq.data.fairseq_dataset import FairseqDataset
  from fairseq.data.audio.audio_utils import parse_path, read_from_stored_zip
  import io
label_desc: Use image node_id 7 for calling the HubertDataset obj's underlying member method code with example usage: obj.crop_to_max_size(wav, target_size) and returns: wav,  start, wav,  int