code: def binary_mask_to_rle(mask):
      """
      Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format.
  
      Args:
          mask (`torch.Tensor` or `numpy.array`):
              A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
              segment_id or class_id.
      Returns:
          `List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE
          format.
      """
      if is_torch_tensor(mask):
          mask = mask.numpy()
  
      pixels = mask.flatten()
      pixels = np.concatenate([[0], pixels, [0]])
      runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
      runs[1::2] -= runs[::2]
      return list(runs)
  
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 18 for calling a global function with example usage: binary_mask_to_rle(mask) and returns: list