code: def create_inputs(imgs, im_info):
      """generate input for different model type
      Args:
          imgs (list(numpy)): list of images (np.ndarray)
          im_info (list(dict)): list of image info
      Returns:
          inputs (dict): input of model
      """
      inputs = {}
  
      im_shape = []
      scale_factor = []
      if len(imgs) == 1:
          inputs["image"] = np.array((imgs[0],)).astype("float32")
          inputs["im_shape"] = np.array(
              (im_info[0]["im_shape"],)
          ).astype("float32")
          inputs["scale_factor"] = np.array(
              (im_info[0]["scale_factor"],)
          ).astype("float32")
          return inputs
  
      for e in im_info:
          im_shape.append(np.array((e["im_shape"],)).astype("float32"))
          scale_factor.append(
              np.array((e["scale_factor"],)).astype("float32")
          )
  
      inputs["im_shape"] = np.concatenate(im_shape, axis=0)
      inputs["scale_factor"] = np.concatenate(scale_factor, axis=0)
  
      imgs_shape = [[e.shape[1], e.shape[2]] for e in imgs]
      max_shape_h = max([e[0] for e in imgs_shape])
      max_shape_w = max([e[1] for e in imgs_shape])
      padding_imgs = []
      for img in imgs:
          im_c, im_h, im_w = img.shape[:]
          padding_im = np.zeros(
              (im_c, max_shape_h, max_shape_w), dtype=np.float32
          )
          padding_im[:, :im_h, :im_w] = img
          padding_imgs.append(padding_im)
      inputs["image"] = np.stack(padding_imgs, axis=0)
      return inputs
  
imports: import os
  import yaml
  import glob
  from functools import reduce
  import cv2
  import numpy
  import math
  import paddle
  from paddle.inference import Config
  from paddle.inference import create_predictor
  import sys
  from benchmark_utils import PaddleInferBenchmark
  from picodet_postprocess import PicoDetPostProcess
  from preprocess import preprocess, Resize, NormalizeImage, Permute, PadStride, LetterBoxResize, Pad, decode_image
  from mot.visualize import visualize_box_mask
  from mot_utils import argsparser, Timer, get_current_memory_mb
label_desc: Use image node_id 2 for calling a global function with example usage: create_inputs(imgs, im_info) and returns: inputs, inputs