class_docstr: Pad side of the image so that its side is divisible by divisor.
  
  Args:
      divisor (int): desirable image size divisor
      border_mode (OpenCV flag): OpenCV border mode.
      fill_value (int, float, list of int, lisft of float): padding value if border_mode is cv2.BORDER_CONSTANT.
class_docstr_tok: ["Pad","side","of","the","image","so","that","its","side","is","divisible","by","divisor",".","Args",":","divisor","(","int",")",":","desirable","image","size","divisor","border_mode","(","OpenCV","flag",")",":","OpenCV","border","mode",".","fill_value","(","int",",","float",",","list","of","int",",","lisft","of","float",")",":","padding","value","if","border_mode","is","cv2.BORDER_CONSTANT","."]
code: def _get_dim_padding(self, dim_size: int) -> Tuple[int, int]:
      pad = (self.divisor - dim_size % self.divisor) % self.divisor
      pad_upper = pad // 2
      pad_lower = pad - pad_upper
      return pad_upper, pad_lower
  
imports: import torch
  from numpy import ndarray
  from torch import Tensor
  from typing import Tuple
  from collections import namedtuple
label_desc: Use image node_id 4 for calling the PadToDivisor obj's underlying member method code with example usage: obj._get_dim_padding(dim_size) and returns: pad_upper,  pad_lower