code: def vstack_autopad(
      images: Iterable[np.ndarray], pad_value: int = 0
  ) -> np.ndarray:
      """
      Stack images vertically with automatic padding
  
      Args:
          images: List of images to stack
  
      Returns:
          image
      """
      max_width = 0
      for img in images:
          max_width = max(max_width, img.shape[1])
  
      padded_images = []
      for img in images:
          width = img.shape[1]
          pad_top = 0
          pad_bottom = 0
          pad_left = 0
          pad_right = max_width - width
          img = cv2.copyMakeBorder(
              img,
              pad_top,
              pad_bottom,
              pad_left,
              pad_right,
              cv2.BORDER_CONSTANT,
              value=pad_value,
          )
          padded_images.append(img)
  
      return np.vstack(padded_images)
  
file_path: pytorch-toolbelt/pytorch_toolbelt/utils/visualization.py
imports: from __future__ import absolute_import
  import itertools
  import math
  import warnings
  from typing import List, Iterable, Tuple, Optional
  import cv2
  import numpy
  from .torch_utils import image_to_tensor
label_desc: Use image node_id 5 for calling a global function with example usage: vstack_autopad(images, pad_value) and returns: np