class_docstr: Get padding images.
  
  Args:
      stride(int): Stride for calculate pad value for edges.
      padValue(int): Initialization for new area.
code: def __call__(self, img: np.ndarray):
      h, w = img.shape[0:2]
      pad = 4 * [0]
      pad[2] = (
          0
          if (h % self.stride == 0)
          else self.stride - (h % self.stride)
      )  # down
      pad[3] = (
          0
          if (w % self.stride == 0)
          else self.stride - (w % self.stride)
      )  # right
  
      img_padded = img
      pad_up = np.tile(
          img_padded[0:1, :, :] * 0 + self.padValue, (pad[0], 1, 1)
      )
      img_padded = np.concatenate((pad_up, img_padded), axis=0)
      pad_left = np.tile(
          img_padded[:, 0:1, :] * 0 + self.padValue, (1, pad[1], 1)
      )
      img_padded = np.concatenate((pad_left, img_padded), axis=1)
      pad_down = np.tile(
          img_padded[-2:-1, :, :] * 0 + self.padValue, (pad[2], 1, 1)
      )
      img_padded = np.concatenate((img_padded, pad_down), axis=0)
      pad_right = np.tile(
          img_padded[:, -2:-1, :] * 0 + self.padValue, (1, pad[3], 1)
      )
      img_padded = np.concatenate((img_padded, pad_right), axis=1)
  
      return img_padded, pad
  
imports: import base64
  import math
  from typing import Callable
  import cv2
  import numpy
  from scipy.ndimage import gaussian_filter
label_desc: Use image node_id 2 for calling the PadDownRight obj's underlying member method code with example usage: obj.__call__(img) and returns: img_padded,  pad