code: def get_mask_area(
      seg_img: np.ndarray, target_size: Tuple[int, int], n_classes: int
  ) -> np.ndarray:
      final_h, final_w = target_size
      np_seg_img = seg_img.astype(np.uint8)
      np_seg_img = np_seg_img.reshape(final_h, final_w, 3)
      m_id = rgb_to_id(np_seg_img)
      area = [(m_id == i).sum() for i in range(n_classes)]
      return area
  
imports: import io
  import pathlib
  from collections import defaultdict
  from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
  import numpy
  from ...feature_extraction_utils import BatchFeature
  from ...image_processing_utils import BaseImageProcessor, get_size_dict
  from ...image_transforms import PaddingMode, center_to_corners_format, corners_to_center_format, id_to_rgb, pad, rescale, resize, rgb_to_id, to_channel_dimension_format
  from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, AnnotationFormat, AnnotationType, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, validate_annotations
  from ...utils import TensorType, is_flax_available, is_jax_tensor, is_scipy_available, is_tf_available, is_tf_tensor, is_torch_available, is_torch_tensor, is_vision_available, logging
label_desc: Use image node_id 14 for calling a global function with example usage: get_mask_area(seg_img, target_size, n_classes) and returns: area