class_docstr: Constructs a Mask2Former image processor. The image processor can be used to prepare image(s) and optional targets
  for the model.
  
  This image processor inherits from [`BaseImageProcessor`] 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)`.
      size_divisor (`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.
      resample (`int`, *optional*, defaults to `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`.
      do_rescale (`bool`, *optional*, defaults to `True`):
          Whether to rescale the input to a certain `scale`.
      rescale_factor (`float`, *optional*, defaults to `1/ 255`):
          Rescale the input by the given factor. Only has an effect if `do_rescale` is set to `True`.
      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 _preprocess(
      self,
      image: ImageInput,
      do_resize: bool = None,
      size: Dict[str, int] = None,
      size_divisor: int = None,
      resample: PILImageResampling = None,
      do_rescale: bool = None,
      rescale_factor: float = None,
      do_normalize: bool = None,
      image_mean: Optional[Union[float, List[float]]] = None,
      image_std: Optional[Union[float, List[float]]] = None,
      input_data_format: Optional[Union[str, ChannelDimension]] = None,
  ):
      if do_resize:
          image = self.resize(
              image,
              size=size,
              size_divisor=size_divisor,
              resample=resample,
              input_data_format=input_data_format,
          )
      if do_rescale:
          image = self.rescale(
              image,
              rescale_factor=rescale_factor,
              input_data_format=input_data_format,
          )
      if do_normalize:
          image = self.normalize(
              image,
              mean=image_mean,
              std=image_std,
              input_data_format=input_data_format,
          )
      return image
  
imports: import math
  import warnings
  from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Union
  import numpy
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import PaddingMode, get_resize_output_image_size, pad, rescale, resize, to_channel_dimension_format
  from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_batched, is_scaled_image, to_numpy_array, valid_images
  from ...utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, TensorType, is_torch_available, is_torch_tensor, logging
label_desc: Use image node_id 7 for calling the Mask2FormerImageProcessor obj's underlying member method code with example usage: obj._preprocess(image, do_resize, size, size_divisor, resample, do_rescale, rescale_factor, do_normalize, image_mean, image_std, input_data_format) and returns: image