code: def check_segment_validity(
      mask_labels,
      mask_probs,
      k,
      mask_threshold=0.5,
      overlap_mask_area_threshold=0.8,
  ):
      # Get the mask associated with the k class
      mask_k = mask_labels == k
      mask_k_area = mask_k.sum()
  
      # Compute the area of all the stuff in query k
      original_area = (mask_probs[k] >= mask_threshold).sum()
      mask_exists = mask_k_area > 0 and original_area > 0
  
      # Eliminate disconnected tiny segments
      if mask_exists:
          area_ratio = mask_k_area / original_area
          if not area_ratio.item() > overlap_mask_area_threshold:
              mask_exists = False
  
      return mask_exists, mask_k
  
imports: import pathlib
  import warnings
  from typing import Dict, List, Optional, Set, Tuple, Union
  import numpy
  from PIL import Image
  from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
  from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
  from ...utils import TensorType, is_torch_available, logging
label_desc: Use image node_id 8 for calling a global function with example usage: check_segment_validity(mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold) and returns: mask_exists,  mask_k