code: def prepare_input():
      transforms = [
          T.Resize(size=(target_height, target_width)),
          T.Normalize(
              mean=(0, 0, 0),
              std=(1, 1, 1),
              data_format="HWC",
              to_rgb=True,
          ),
          T.Transpose(),
      ]
  
      img_file = root_path / "street.jpeg"
      img = cv2.imread(str(img_file))
      normalized_img = T.Compose(transforms)(img)
      normalized_img = (
          normalized_img.astype(np.float32, copy=False) / 255.0
      )
  
      # add an new axis in front
      img_input = normalized_img[np.newaxis, :]
      # scale_factor is calculated as: im_shape / original_im_shape
      h_scale = target_height / img.shape[0]
      w_scale = target_width / img.shape[1]
      input = {
          "image": img_input,
          "im_shape": [target_height, target_width],
          "scale_factor": [h_scale, w_scale],
      }
      return input, img
  
file_path: PaddleDetection/docs/advanced_tutorials/openvino_inference/fairmot_onnx_openvino.py
imports: from collections import defaultdict
  from pathlib import Path
  import cv2
  import numpy
  import paddle.vision.transforms
  from openvino.inference_engine import IECore
  from ppdet.modeling.mot.tracker import JDETracker
  from ppdet.modeling.mot.visualization import plot_tracking_dict
label_desc: Use image node_id 3 for calling a global function with example usage: prepare_input() and returns: input,  img