code: def transform_wav_audio(wav_audio, hparams, pipeline=None):
      """Transform the contents of a wav file based on hyperparameters.
  
      Args:
        wav_audio: The contents of a wav file; this will be written to a temporary
            file and transformed via SoX.
        hparams: The tf.contrib.training.HParams object to use to construct the
            audio transform pipeline.
        pipeline: A list of pipeline stages, each specified as a tuple of stage
            name (SoX method) and a dictionary of parameters. If None, uses
            `AUDIO_TRANSFORM_PIPELINE`.
  
      Returns:
        The contents of the wav file that results from applying the audio transform
        pipeline to the input audio.
      """
      if not hparams.transform_audio:
          return wav_audio
  
      pipeline = construct_pipeline(
          hparams,
          pipeline
          if pipeline is not None
          else AUDIO_TRANSFORM_PIPELINE,
      )
  
      with tempfile.NamedTemporaryFile(
          suffix=".wav"
      ) as temp_input_with_noise:
          with tempfile.NamedTemporaryFile(suffix=".wav") as temp_input:
              temp_input.write(wav_audio)
              temp_input.flush()
  
              # Add noise before all other pipeline steps.
              noise_vol = random.uniform(
                  hparams.audio_transform_min_noise_vol,
                  hparams.audio_transform_max_noise_vol,
              )
              add_noise(
                  temp_input.name,
                  temp_input_with_noise.name,
                  noise_vol,
                  hparams.audio_transform_noise_type,
              )
  
          with tempfile.NamedTemporaryFile(
              suffix=".wav"
          ) as temp_output:
              run_pipeline(
                  pipeline, temp_input_with_noise.name, temp_output.name
              )
              return temp_output.read()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import random
  import subprocess
  import tempfile
  from magenta.contrib import training
  import sox
  import tensorflow.compat.v1
label_desc: Use image node_id 4 for calling a global function with example usage: transform_wav_audio(wav_audio, hparams, pipeline) and returns: wav_audio, temp_output