docstr: face (str): path to video/image that contains faces to use.
  audio (str): path to input audio.
  output_dir: the dir to save the results
  use_gpu: if True, use gpu to perform the computation, otherwise cpu.
  visualization: if True, save results in output_dir.
docstr_tok: ["face","(","str",")",":","path","to","video\/image","that","contains","faces","to","use",".","audio","(","str",")",":","path","to","input","audio",".","output_dir",":","the","dir","to","save","the","results","use_gpu",":","if","True",",","use","gpu","to","perform","the","computation",",","otherwise","cpu",".","visualization",":","if","True",",","save","results","in","output_dir","."]
code: def wav2lip_transfer(
      self,
      face,
      audio,
      output_dir="./output_result/",
      use_gpu=False,
      visualization=True,
  ):
      """
      face (str): path to video/image that contains faces to use.
      audio (str): path to input audio.
      output_dir: the dir to save the results
      use_gpu: if True, use gpu to perform the computation, otherwise cpu.
      visualization: if True, save results in output_dir.
      """
      paddle.disable_static()
      place = "gpu:0" if use_gpu else "cpu"
      place = paddle.set_device(place)
      self.network.run(face, audio, output_dir, visualization)
  
file_path: PaddleHub/modules/image/Image_gan/gan/wav2lip/module.py
imports: import os
  import argparse
  import copy
  import paddle
  import paddlehub
  from paddlehub.module.module import moduleinfo, runnable, serving
  import numpy
  import cv2
  from .model import Wav2LipPredictor
label_desc: Use image node_id 2 for calling the wav2lip obj's underlying member method code with example usage: obj.wav2lip_transfer(face, audio, output_dir, use_gpu, visualization) without return types