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 __init__(
      self,
      divisor: int,
      border_mode: int = 0 if cv2 is None else cv2.BORDER_CONSTANT,
      fill_value: int = 0,
  ):
      super().__init__()
      self.border_mode = border_mode
      self.fill_value = fill_value
      self.divisor = divisor
      self._pads = None
  
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 1 to create a new PadToDivisor object from inherited base classes: EvalTransform with example: obj = PadToDivisor(divisor, border_mode, fill_value)