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 _remove_padding(self, tensor: Tensor) -> Tensor:
      tensor_h, tensor_w = tensor.shape[:2]
      cropped = tensor[
          self._pads.top : tensor_h - self._pads.bottom,
          self._pads.left : tensor_w - self._pads.right,
          :,
      ]
      return cropped
  
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 5 for calling the PadToDivisor obj's underlying member method code with example usage: obj._remove_padding(tensor) and returns: cropped