docstr: Wrapper to load or convert list of multiple images to a single image 
  tensor. Multiple images are concatenated together on the first dimension.
  
  Parameters
  ----------
  images : a list of path-to-file, PIL.Image, or np.ndarray
      Image to load or convert.
  detection_size : int, optional
      Target detection size. The default is None.
  
  Returns
  -------
  img : torch.tensor
      A single tensor image with 4 dimension [batch, channel, width, height].
  original_shape : tuple
      A list of tuples (height, width) of the original input image before resizing.
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code: def load_images(self, images, detection_size=None):
      """
      Wrapper to load or convert list of multiple images to a single image
      tensor. Multiple images are concatenated together on the first dimension.
  
      Parameters
      ----------
      images : a list of path-to-file, PIL.Image, or np.ndarray
          Image to load or convert.
      detection_size : int, optional
          Target detection size. The default is None.
  
      Returns
      -------
      img : torch.tensor
          A single tensor image with 4 dimension [batch, channel, width, height].
      original_shape : tuple
          A list of tuples (height, width) of the original input image before resizing.
      """
      images, original_shapes = zip(
          *[
              self.load_image(image, detection_size=detection_size)
              for image in images
          ]
      )
      return torch.cat(images, dim=0), original_shapes
  
imports: import os
  import math
  import yaml
  from shapely.geometry import Polygon
  import PIL.Image
  import numpy
  import cv2
  import pyclipper
  import torch
  from .model.constructor import Configurable
label_desc: Use image node_id 11 for calling the DBNet obj's underlying member method code with example usage: obj.load_images(images, detection_size) and returns: torch,  original_shapes