code: def create_inputs(imgs, im_info):
      """generate input for different model type
      Args:
          imgs (list(numpy)): list of image (np.ndarray)
          im_info (list(dict)): list of image info
      Returns:
          inputs (dict): input of model
      """
      inputs = {}
      inputs["image"] = np.stack(imgs, axis=0).astype("float32")
      im_shape = []
      for e in im_info:
          im_shape.append(np.array((e["im_shape"])).astype("float32"))
      inputs["im_shape"] = np.stack(im_shape, axis=0)
      return inputs
  
imports: import math
  import os
  import cv2
  import numpy
  import yaml
  from .infer import Detector
  from .keypoint_postprocess import HRNetPostProcess
  from .keypoint_preprocess import expand_crop
  from .visualize import visualize_pose
label_desc: Use image node_id 1 for calling a global function with example usage: create_inputs(imgs, im_info) and returns: inputs