docstr: images (list[numpy.ndarray]): data of images, shape of each is [H, W, C], color space must be BGR(read by cv2).
  paths (list[str]): paths to images
  output_dir (str): the dir to save the results
  use_gpu (bool): if True, use gpu to perform the computation, otherwise cpu.
  need_animation (bool): if True, save every frame to show the process of painting.
  visualization (bool): if True, save results in output_dir.
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code: def style_transfer(
      self,
      images: list = None,
      paths: list = None,
      output_dir: str = "./transfer_result/",
      use_gpu: bool = False,
      need_animation: bool = False,
      visualization: bool = True,
  ):
      """
  
  
      images (list[numpy.ndarray]): data of images, shape of each is [H, W, C], color space must be BGR(read by cv2).
      paths (list[str]): paths to images
      output_dir (str): the dir to save the results
      use_gpu (bool): if True, use gpu to perform the computation, otherwise cpu.
      need_animation (bool): if True, save every frame to show the process of painting.
      visualization (bool): if True, save results in output_dir.
      """
      results = []
      paddle.disable_static()
      place = "gpu:0" if use_gpu else "cpu"
      place = paddle.set_device(place)
      if images == None and paths == None:
          print(
              "No image provided. Please input an image or a image path."
          )
          return
  
      if images != None:
          for image in images:
              image = image[:, :, ::-1]
              image = totensor(image)
              final_result_list = render_serial(
                  image, self.network, self.meta_brushes
              )
              results.append(final_result_list)
  
      if paths != None:
          for path in paths:
              image = cv2.imread(path)[:, :, ::-1]
              image = totensor(image)
              final_result_list = render_serial(
                  image, self.network, self.meta_brushes
              )
              results.append(final_result_list)
  
      if visualization == True:
          if not os.path.exists(output_dir):
              os.makedirs(output_dir, exist_ok=True)
          for i, out in enumerate(results):
              if out:
                  if need_animation:
                      curoutputdir = os.path.join(
                          output_dir, "output_{}".format(i)
                      )
                      if not os.path.exists(curoutputdir):
                          os.makedirs(curoutputdir, exist_ok=True)
                      for j, outimg in enumerate(out):
                          cv2.imwrite(
                              os.path.join(
                                  curoutputdir, "frame_{}.png".format(j)
                              ),
                              outimg,
                          )
                  else:
                      cv2.imwrite(
                          os.path.join(
                              output_dir, "output_{}.png".format(i)
                          ),
                          out[-1],
                      )
  
      return results
  
imports: import os
  import argparse
  import copy
  import paddle
  import paddlehub
  from paddlehub.module.module import moduleinfo, runnable, serving
  import numpy
  import cv2
  from skimage.io import imread
  from skimage.transform import rescale, resize
  from .model import Painter
  from .render_utils import totensor, read_img
  from .render_serial import render_serial
  from .util import base64_to_cv2
label_desc: Use image node_id 2 for calling the paint_transformer obj's underlying member method code with example usage: obj.style_transfer(images, paths, output_dir, use_gpu, need_animation, visualization) and returns: results