class_docstr: Compress the audio using an MP3 encoder to lower the audio quality.
  This may help machine learning models deal with compressed, low-quality audio.
  
  This transform depends on either lameenc or pydub/ffmpeg.
  
  Note that bitrates below 32 kbps are only supported for low sample rates (up to 24000 Hz).
  
  Note: When using the lameenc backend, the output may be slightly longer than the input due
  to the fact that the LAME encoder inserts some silence at the beginning of the audio.
  
  Warning: This transform writes to disk, so it may be slow. Ideally, the work should be done
  in memory. Contributions are welcome.
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docstr: If the audio was pre-gained down earlier, post-gain it up to compensate here.
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code: def maybe_post_gain(self, samples):
      """If the audio was pre-gained down earlier, post-gain it up to compensate here."""
      if self.post_gain_factor is not None:
          samples = samples * self.post_gain_factor
      return samples
  
file_path: audiomentations/audiomentations/augmentations/mp3_compression.py
imports: import os
  import random
  import tempfile
  import uuid
  import librosa
  import numpy
  import sys
  from numpy.typing import NDArray
  from audiomentations.core.transforms_interface import BaseWaveformTransform
  from audiomentations.core.utils import convert_float_samples_to_int16, get_max_abs_amplitude
label_desc: Use image node_id 5 for calling the Mp3Compression obj's underlying member method code with example usage: obj.maybe_post_gain(samples) and returns: samples