code: def create_headers(image_name):
      """
      This function generates C header files for the input and output arrays required to run inferences
      """
      img_path = os.path.join("./", f"{image_name}")
  
      # Resize image to 32x320
      img = cv2.imread(img_path)
      img = resize_norm_img(img, [3, 32, 320])
      img_data = img.astype("float32")
  
      # # Add the batch dimension, as we are expecting 4-dimensional input: NCHW.
      img_data = np.expand_dims(img_data, axis=0)
  
      if os.path.exists("./include/inputs.h"):
          os.remove("./include/inputs.h")
      if os.path.exists("./include/outputs.h"):
          os.remove("./include/outputs.h")
      # Create input header file
      create_header_file("inputs", "input", img_data, "./include")
      # Create output header file
      output_data = np.zeros([8500], np.float32)
      create_header_file(
          "outputs",
          "output0",
          output_data,
          "./include",
      )
      output_data = np.zeros([170000], np.float32)
      create_header_file(
          "outputs",
          "output1",
          output_data,
          "./include",
      )
  
file_path: PaddleDetection/deploy/third_engine/demo_avh/convert_image.py
imports: import os
  import pathlib
  import re
  import sys
  import cv2
  import math
  from PIL import Image
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: create_headers(image_name) without return types