code: def postprocess(self, wav, cur_sample_rate):
      if wav.dim() == 2:
          wav = wav.mean(-1)
      assert wav.dim() == 1, wav.dim()
  
      if cur_sample_rate != self.sample_rate:
          raise Exception(f"sr {cur_sample_rate} != {self.sample_rate}")
  
      if self.normalize:
          with torch.no_grad():
              wav = F.layer_norm(wav, wav.shape)
      return wav
  
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 16 for calling the HubertDataset obj's underlying member method code with example usage: obj.postprocess(wav, cur_sample_rate) and returns: wav