class_docstr: Resize input image to target size.
  
  Args:
      target_size(List[int]|int]): Target image size.
      interpolation(str): Interpolation mode, default is 'LINEAR'. It support 6 modes: 'NEAREST', 'LINEAR', 'CUBIC', 'AREA', 'LANCZOS4' and 'RANDOM'.
class_docstr_tok: ["Resize","input","image","to","target","size",".","Args",":","target_size","(","List","[","int","]","|int","]",")",":","Target","image","size",".","interpolation","(","str",")",":","Interpolation","mode",",","default","is","'LINEAR","'",".","It","support","6","modes",":","'NEAREST","'",",","'LINEAR","'",",","'CUBIC","'",",","'AREA","'",",","'LANCZOS4","'","and","'RANDOM","'","."]
code: def __call__(self, im: np.ndarray):
      if self.interpolation == "RANDOM":
          interpolation = random.choice(
              list(self.interpolation_dict.keys())
          )
      else:
          interpolation = self.interpolation
      im = F.resize(
          im, self.target_size, self.interpolation_dict[interpolation]
      )
      return im
  
imports: import random
  from typing import Callable, Union, List, Tuple
  import cv2
  import PIL
  import numpy
  import paddlehub.vision.functional
label_desc: Use image node_id 2 for calling the Resize obj's underlying member method code with example usage: obj.__call__(im) and returns: im