docstr: Colorize images
  
  Args:
      images(list[str|np.ndarray]) : Images path or BGR image to be colorized.
      visualization(bool): Whether to save colorized images.
      batch_size(int): Batch size for prediciton.
      save_path(str) : Path to save colorized images.
  
  Returns:
      res(list[dict]) : The prediction result of each input image
code: def predict(
      self,
      images: list,
      visualization: bool = True,
      batch_size: int = 1,
      save_path: str = "colorization",
  ):
      """
      Colorize images
  
      Args:
          images(list[str|np.ndarray]) : Images path or BGR image to be colorized.
          visualization(bool): Whether to save colorized images.
          batch_size(int): Batch size for prediciton.
          save_path(str) : Path to save colorized images.
  
      Returns:
          res(list[dict]) : The prediction result of each input image
      """
      self.eval()
      with paddle.no_grad():
          lab2rgb = T.LAB2RGB()
          res = []
          total_num = len(images)
          loop_num = int(np.ceil(total_num / batch_size))
          for iter_id in range(loop_num):
              batch_data = []
              handle_id = iter_id * batch_size
              for image_id in range(batch_size):
                  try:
                      image = self.transforms(
                          images[handle_id + image_id]
                      )
                      batch_data.append(image)
                  except:
                      pass
              batch_data = np.array(batch_data)
              im = self.preprocess(batch_data)
              out_class, out_reg = self(
                  im["A"], im["hint_B"], im["mask_B"]
              )
  
              visual_ret = OrderedDict()
              for i in range(im["A"].shape[0]):
                  gray = lab2rgb(
                      np.concatenate(
                          (im["A"].numpy(), np.zeros(im["B"].shape)),
                          axis=1,
                      )
                  )[i]
                  gray = (
                      np.clip(np.transpose(gray, (1, 2, 0)), 0, 1) * 255
                  )
                  visual_ret["gray"] = gray.astype(np.uint8)
                  hint = lab2rgb(
                      np.concatenate(
                          (im["A"].numpy(), im["hint_B"].numpy()),
                          axis=1,
                      )
                  )[i]
                  hint = (
                      np.clip(np.transpose(hint, (1, 2, 0)), 0, 1) * 255
                  )
                  visual_ret["hint"] = hint.astype(np.uint8)
                  real = lab2rgb(
                      np.concatenate(
                          (im["A"].numpy(), im["B"].numpy()), axis=1
                      )
                  )[i]
                  real = (
                      np.clip(np.transpose(real, (1, 2, 0)), 0, 1) * 255
                  )
                  visual_ret["real"] = real.astype(np.uint8)
                  fake = lab2rgb(
                      np.concatenate(
                          (im["A"].numpy(), out_reg.numpy()), axis=1
                      )
                  )[i]
                  fake = (
                      np.clip(np.transpose(fake, (1, 2, 0)), 0, 1) * 255
                  )
                  visual_ret["fake_reg"] = fake.astype(np.uint8)
  
                  if visualization:
                      if isinstance(images[handle_id + i], str):
                          org_img = cv2.imread(
                              images[handle_id + i]
                          ).astype("float32")
                      else:
                          org_img = images[handle_id + i]
                      h, w, c = org_img.shape
                      fake_name = "fake_" + str(time.time()) + ".png"
                      if not os.path.exists(save_path):
                          os.mkdir(save_path)
                      fake_path = os.path.join(save_path, fake_name)
                      visual_gray = Image.fromarray(
                          visual_ret["fake_reg"]
                      )
                      visual_gray = visual_gray.resize(
                          (w, h), Image.BILINEAR
                      )
                      visual_gray.save(fake_path)
  
                  res.append(visual_ret)
          return res
  
imports: import time
  import os
  import base64
  import argparse
  from typing import List, Union, Tuple
  from collections import OrderedDict
  import cv2
  import paddle
  import numpy
  import paddle.nn
  import paddle.nn.functional
  from PIL import Image
  import paddlehub.vision.transforms
  import paddlehub.vision.functional
  from paddlehub.vision import utils
  from paddlehub.module.module import serving, RunModule, runnable
  from paddlehub.utils.utils import base64_to_cv2, cv2_to_base64, Version
label_desc: Use image node_id 3 for calling the ImageColorizeModule obj's underlying member method code with example usage: obj.predict(images, visualization, batch_size, save_path) and returns: res