class_docstr: Resize the input images.
  
  Args:
      target_size(int): Targeted input size.
      interp(str): Interpolation method.
  
  Returns:
      img(np.ndarray): Preprocessed image.
      data(dict): Image info and label info, default is None.
class_docstr_tok: ["Resize","the","input","images",".","Args",":","target_size","(","int",")",":","Targeted","input","size",".","interp","(","str",")",":","Interpolation","method",".","Returns",":","img","(","np.ndarray",")",":","Preprocessed","image",".","data","(","dict",")",":","Image","info","and","label","info",",","default","is","None","."]
code: def __call__(self, img, data=None):
      if self.interp == "RANDOM":
          interp = random.choice(list(self.interp_dict.keys()))
      else:
          interp = self.interp
      img = F.resize(img, self.target_size, self.interp_dict[interp])
      if data is not None:
          return img, data
      else:
          return img
  
imports: import os
  import random
  from typing import Callable
  import cv2
  import PIL
  import numpy
  import paddlehub.vision.transforms.functional
  from paddlehub.vision.utils import box_crop, box_iou_xywh
label_desc: Use image node_id 2 for calling the Resize obj's underlying member method code with example usage: obj.__call__(img, data) and returns: img,  data, img