code: def mask_to_test_readable(mask: Image) -> Dict:
      npimg = np.array(mask)
      white_pixels = (npimg == 255).sum()
      shape = npimg.shape
      return {
          "hash": hashimage(mask),
          "white_pixels": white_pixels,
          "shape": shape,
      }
  
imports: import tempfile
  import unittest
  from typing import Dict
  import datasets
  import numpy
  import requests
  from datasets import load_dataset
  from huggingface_hub.utils import insecure_hashlib
  from transformers import MODEL_FOR_IMAGE_SEGMENTATION_MAPPING, MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING, MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING, AutoImageProcessor, AutoModelForImageSegmentation, AutoModelForInstanceSegmentation, DetrForSegmentation, ImageSegmentationPipeline, MaskFormerForInstanceSegmentation, is_vision_available, pipeline
  from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_timm, require_torch, require_vision, slow
  from .test_pipelines_common import ANY
label_desc: Use image node_id 2 for calling a global function with example usage: mask_to_test_readable(mask) and returns: dict