docstr: Transfer a image to ocean style.
  
  images (list[dict]): data of images, each element is a dict:
    - content (numpy.ndarray): input image，shape is \[H, W, C\]，BGR format；<br/>
    - style (numpy.ndarray) : style image，shape is \[H, W, C\]，BGR format；<br/>
  paths (list[dict]): paths to images, eacg element is a dict:
    - content (str): path to input image；<br/>
    - style (str) : path to style image；<br/>
  
   output_dir (str): the dir to save the results
   use_gpu (bool): if True, use gpu to perform the computation, otherwise cpu.
   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,
      visualization: bool = True,
  ):
      """
      Transfer a image to ocean style.
  
      images (list[dict]): data of images, each element is a dict:
        - content (numpy.ndarray): input image，shape is \[H, W, C\]，BGR format；<br/>
        - style (numpy.ndarray) : style image，shape is \[H, W, C\]，BGR format；<br/>
      paths (list[dict]): paths to images, eacg element is a dict:
        - content (str): path to input image；<br/>
        - style (str) : path to style image；<br/>
  
       output_dir (str): the dir to save the results
       use_gpu (bool): if True, use gpu to perform the computation, otherwise cpu.
       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_dict in images:
              content_img = image_dict["content"]
              style_img = image_dict["style"]
              results.append(self.network.run(content_img, style_img))
  
      if paths != None:
          for path_dict in paths:
              content_img = cv2.imread(path_dict["content"])
              style_img = cv2.imread(path_dict["style"])
              results.append(self.network.run(content_img, style_img))
  
      if visualization == True:
          if not os.path.exists(output_dir):
              os.makedirs(output_dir, exist_ok=True)
          for i, out in enumerate(results):
              cv2.imwrite(
                  os.path.join(output_dir, "output_{}.png".format(i)),
                  out[:, :, ::-1],
              )
  
      return results
  
imports: import argparse
  import copy
  import os
  import cv2
  import numpy
  import paddle
  from skimage.io import imread
  from skimage.transform import rescale
  from skimage.transform import resize
  import paddlehub
  from .model import LapStylePredictor
  from .util import base64_to_cv2
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import runnable
  from paddlehub.module.module import serving
label_desc: Use image node_id 2 for calling the Lapstyle_ocean obj's underlying member method code with example usage: obj.style_transfer(images, paths, output_dir, use_gpu, visualization) and returns: results