code: def hash_image(image):
      if isinstance(image, np.ndarray):
          image = Image.fromarray(image.squeeze())
      elif isinstance(image, torch.Tensor):
          image = Image.fromarray(object_to_numpy(image).squeeze())
  
      image = image.resize((10, 10))
      image = image.convert("L")
  
      pixel_data = list(image.getdata())
      avg_pixel = sum(pixel_data) / len(pixel_data)
  
      bits = "".join(
          ["1" if (px >= avg_pixel) else "0" for px in pixel_data]
      )
      hex_representation = str(hex(int(bits, 2)))[2:][::-1].upper()
      md_5hash = md5()
      md_5hash.update(hex_representation.encode())
      return md_5hash.hexdigest()
  
imports: import random
  from copy import copy
  from hashlib import md5
  from typing import Dict, Iterator, List, Sized
  import numpy
  import torch
  from PIL import Image
  from torch.utils.data import BatchSampler, DataLoader, Sampler
  from deepchecks.vision import VisionData
  from deepchecks.vision.vision_data.utils import object_to_numpy
label_desc: Use image node_id 7 for calling a global function with example usage: hash_image(image) and returns: md_5hash