code: def postprocess(self, feats, curr_sample_rate):
      if feats.dim() == 2:
          feats = feats.mean(-1)
  
      if curr_sample_rate != self.sample_rate:
          raise Exception(
              f"sample rate: {curr_sample_rate}, need {self.sample_rate}"
          )
  
      assert feats.dim() == 1, feats.dim()
  
      if self.normalize:
          with torch.no_grad():
              feats = F.layer_norm(feats, feats.shape)
      return feats
  
imports: import logging
  import os
  import sys
  import time
  import io
  import numpy
  import torch
  import torch.nn.functional
  from ..None import FairseqDataset
  from ..data_utils import compute_block_mask_1d, get_buckets, get_bucketed_sizes
  from fairseq.data.audio.audio_utils import parse_path, read_from_stored_zip, is_sf_audio_data
  from fairseq.data.text_compressor import TextCompressor, TextCompressionLevel
label_desc: Use image node_id 4 for calling the RawAudioDataset obj's underlying member method code with example usage: obj.postprocess(feats, curr_sample_rate) and returns: feats