code: def __init__(self):
      self.pretrained_model = os.path.join(
          self.directory, "wav2lip_hq.pdparams"
      )
  
      self.network = Wav2LipPredictor(
          checkpoint_path=self.pretrained_model,
          static=False,
          fps=25,
          pads=[0, 10, 0, 0],
          face_det_batch_size=16,
          wav2lip_batch_size=128,
          resize_factor=1,
          crop=[0, -1, 0, -1],
          box=[-1, -1, -1, -1],
          rotate=False,
          nosmooth=False,
          face_detector="sfd",
          face_enhancement=True,
      )
  
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 1 to create a new wav2lip object  with example: obj = wav2lip()