code: def create_header_file(name, tensor_name, tensor_data, output_path):
      """
      This function generates a header file containing the data from the numpy array provided.
      """
      file_path = pathlib.Path(f"{output_path}/" + name).resolve()
      # Create header file with npy_data as a C array
      raw_path = file_path.with_suffix(".h").resolve()
      with open(raw_path, "a") as header_file:
          header_file.write(
              "\n"
              + f"const size_t {tensor_name}_len = {tensor_data.size};\n"
              + f'__attribute__((section(".data.tvm"), aligned(16))) float {tensor_name}[] = '
          )
  
          header_file.write("{")
          for i in np.ndindex(tensor_data.shape):
              header_file.write(f"{tensor_data[i]}, ")
          header_file.write("};\n\n")
  
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 2 for calling a global function with example usage: create_header_file(name, tensor_name, tensor_data, output_path) without return types