docstr: API for face detection.
  
  Args:
      images (list(numpy.ndarray)): images data, shape of each is [H, W, C]
      paths (list[str]): The paths of images.
      use_gpu (bool): Whether to use gpu.
      output_dir (str): The path to store output images.
      visualization (bool): Whether to save image or not.
      shrink (float): parameter to control the resize scale in preprocess.
      confs_threshold (float): confidence threshold.
  
  Returns:
      res (list[dict]): The result of face detection and save path of images.
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code: def face_detection(
      self,
      images=None,
      paths=None,
      data=None,
      use_gpu=False,
      output_dir="detection_result",
      visualization=False,
      shrink=0.5,
      confs_threshold=0.6,
  ):
      """
      API for face detection.
  
      Args:
          images (list(numpy.ndarray)): images data, shape of each is [H, W, C]
          paths (list[str]): The paths of images.
          use_gpu (bool): Whether to use gpu.
          output_dir (str): The path to store output images.
          visualization (bool): Whether to save image or not.
          shrink (float): parameter to control the resize scale in preprocess.
          confs_threshold (float): confidence threshold.
  
      Returns:
          res (list[dict]): The result of face detection and save path of images.
      """
      if use_gpu:
          try:
              _places = os.environ["CUDA_VISIBLE_DEVICES"]
              int(_places[0])
          except:
              raise RuntimeError(
                  "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES as cuda_device_id."
              )
  
      # compatibility with older versions
      if data:
          if "image" in data:
              if paths is None:
                  paths = list()
              paths += data["image"]
          elif "data" in data:
              if images is None:
                  images = list()
              images += data["data"]
  
      res = list()
      # process one by one
      for element in reader(images, paths, shrink):
          image = np.expand_dims(element["image"], axis=0).astype(
              "float32"
          )
  
          predictor = (
              self.gpu_predictor if use_gpu else self.cpu_predictor
          )
          input_names = predictor.get_input_names()
          input_handle = predictor.get_input_handle(input_names[0])
          input_handle.copy_from_cpu(image)
  
          predictor.run()
          output_names = predictor.get_output_names()
          output_handle = predictor.get_output_handle(output_names[0])
          output_data = output_handle.copy_to_cpu()
  
          out = postprocess(
              data_out=output_data,
              org_im=element["org_im"],
              org_im_path=element["org_im_path"],
              image_width=element["image_width"],
              image_height=element["image_height"],
              output_dir=output_dir,
              visualization=visualization,
              shrink=shrink,
              confs_threshold=confs_threshold,
          )
          res.append(out)
      return res
  
imports: from __future__ import absolute_import
  from __future__ import division
  import argparse
  import ast
  import os
  import numpy
  import paddle
  from paddle.inference import Config
  from paddle.inference import create_predictor
  from .data_feed import reader
  from .processor import base64_to_cv2
  from .processor import postprocess
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import runnable
  from paddlehub.module.module import serving
label_desc: Use image node_id 3 for calling the PyramidBoxLiteMobile obj's underlying member method code with example usage: obj.face_detection(images, paths, data, use_gpu, output_dir, visualization, shrink, confs_threshold) and returns: res