class_docstr: Constructs a MaskFormer feature extractor. The feature extractor can be used to prepare image(s) and optional
  targets for the model.
  
  This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
  should refer to this superclass for more information regarding those methods.
  
  Args:
      do_resize (`bool`, *optional*, defaults to `True`):
          Whether to resize the input to a certain `size`.
      size (`int`, *optional*, defaults to 800):
          Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
          sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
          the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
          height / width, size)`.
      max_size (`int`, *optional*, defaults to 1333):
          The largest size an image dimension can have (otherwise it's capped). Only has an effect if `do_resize` is
          set to `True`.
      resample (`int`, *optional*, defaults to `PIL.Image.Resampling.BILINEAR`):
          An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
          `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
          `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
          to `True`.
      size_divisibility (`int`, *optional*, defaults to 32):
          Some backbones need images divisible by a certain number. If not passed, it defaults to the value used in
          Swin Transformer.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether or not to normalize the input with mean and standard deviation.
      image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
          The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
      image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
          The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
          ImageNet std.
      ignore_index (`int`, *optional*):
          Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels
          denoted with 0 (background) will be replaced with `ignore_index`.
      reduce_labels (`bool`, *optional*, defaults to `False`):
          Whether or not to decrement all label values of segmentation maps by 1. Usually used for datasets where 0
          is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k).
          The background label will be replaced by `ignore_index`.
code: def convert_segmentation_map_to_binary_masks(
      self,
      segmentation_map: "np.ndarray",
      instance_id_to_semantic_id: Optional[Dict[int, int]] = None,
  ):
      # Get unique ids (class or instance ids based on input)
      all_labels = np.unique(segmentation_map)
  
      # Drop background label if applicable
      if self.reduce_labels:
          all_labels = all_labels[all_labels != 0]
  
      # Generate a binary mask for each object instance
      binary_masks = [
          np.ma.masked_where(segmentation_map == i, segmentation_map)
          for i in all_labels
      ]
      binary_masks = np.stack(
          binary_masks, axis=0
      )  # (num_labels, height, width)
  
      # Convert instance ids to class ids
      if instance_id_to_semantic_id is not None:
          labels = np.zeros(all_labels.shape[0])
  
          for label in all_labels:
              class_id = instance_id_to_semantic_id[label]
              labels[all_labels == label] = class_id
      else:
          labels = all_labels
  
      # Decrement labels by 1
      if self.reduce_labels:
          labels -= 1
  
      return binary_masks.astype(np.float32), labels.astype(np.int64)
  
imports: from typing import TYPE_CHECKING, Dict, List, Optional, Set, Tuple, Union
  import numpy
  from PIL import Image
  from transformers.image_utils import PILImageResampling
  from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
  from ...image_utils import ImageFeatureExtractionMixin, ImageInput, is_torch_tensor
  from ...utils import TensorType, is_torch_available, logging
label_desc: Use image node_id 5 for calling the MaskFormerFeatureExtractor obj's underlying member method code with example usage: obj.convert_segmentation_map_to_binary_masks(segmentation_map, instance_id_to_semantic_id) and returns: binary_masks,  labels