class_docstr: Padding input into targeted size according to specific padding value.
  
  Args:
      target_size(Union[List[int], Tuple[int], int]): Targeted image size.
      im_padding_value(list): Border value for 3 channels, default is [127.5, 127.5, 127.5].
class_docstr_tok: ["Padding","input","into","targeted","size","according","to","specific","padding","value",".","Args",":","target_size","(","Union","[","List","[","int","]",",","Tuple","[","int","]",",","int","]",")",":","Targeted","image","size",".","im_padding_value","(","list",")",":","Border","value","for","3","channels",",","default","is","[","127.5",",","127.5",",","127.5","]","."]
code: def __init__(
      self,
      target_size: Union[List[int], Tuple[int], int],
      im_padding_value: list = [127.5, 127.5, 127.5],
  ):
      if isinstance(target_size, list) or isinstance(
          target_size, tuple
      ):
          if len(target_size) != 2:
              raise ValueError(
                  "when target is list or tuple, it should include 2 elements, but it is {}".format(
                      target_size
                  )
              )
      elif not isinstance(target_size, int):
          raise TypeError(
              "Type of target_size is invalid. Must be Integer or List or tuple, now is {}".format(
                  type(target_size)
              )
          )
      self.target_size = target_size
      self.im_padding_value = im_padding_value
  
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 1 to create a new Padding object  with example: obj = Padding(target_size, im_padding_value)