code: def test_decode_jpeg(img_path, pil_mode, mode):
      with Image.open(img_path) as img:
          is_cmyk = img.mode == "CMYK"
          if pil_mode is not None:
              img = img.convert(pil_mode)
          img_pil = torch.from_numpy(np.array(img))
          if is_cmyk and mode == ImageReadMode.UNCHANGED:
              # flip the colors to match libjpeg
              img_pil = 255 - img_pil
  
      img_pil = normalize_dimensions(img_pil)
      data = read_file(img_path)
      img_ljpeg = decode_image(data, mode=mode)
  
      # Permit a small variation on pixel values to account for implementation
      # differences between Pillow and LibJPEG.
      abs_mean_diff = (
          (img_ljpeg.type(torch.float32) - img_pil).abs().mean().item()
      )
      assert abs_mean_diff < 2
  
imports: import glob
  import io
  import os
  import sys
  from pathlib import Path
  import numpy
  import pytest
  import torch
  import torchvision.transforms.functional
  from common_utils import assert_equal, needs_cuda
  from PIL import __version__, Image
  from torchvision.io.image import _read_png_16, decode_image, decode_jpeg, decode_png, encode_jpeg, encode_png, ImageReadMode, read_file, read_image, write_file, write_jpeg, write_png
label_desc: Use image node_id 5 for calling a global function with example usage: test_decode_jpeg(img_path, pil_mode, mode) without return types