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`.
docstr: Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
  in the batch and optionally returns their corresponding pixel mask.
  
  Args:
      image (`np.ndarray`):
          Image to pad.
      constant_values (`float` or `Iterable[float]`, *optional*):
          The value to use for the padding if `mode` is `"constant"`.
      return_pixel_mask (`bool`, *optional*, defaults to `True`):
          Whether to return a pixel mask.
      return_tensors (`str` or `TensorType`, *optional*):
          The type of tensors to return. Can be one of:
              - Unset: Return a list of `np.ndarray`.
              - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
              - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
              - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
              - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
      data_format (`str` or `ChannelDimension`, *optional*):
          The channel dimension format of the image. If not provided, it will be the same as the input image.
      input_data_format (`ChannelDimension` or `str`, *optional*):
          The channel dimension format of the input image. If not provided, it will be inferred.
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code: def pad(
      self,
      images: List[np.ndarray],
      constant_values: Union[float, Iterable[float]] = 0,
      return_pixel_mask: bool = True,
      return_tensors: Optional[Union[str, TensorType]] = None,
      data_format: Optional[ChannelDimension] = None,
      input_data_format: Optional[Union[str, ChannelDimension]] = None,
  ) -> BatchFeature:
      """
      Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width
      in the batch and optionally returns their corresponding pixel mask.
  
      Args:
          image (`np.ndarray`):
              Image to pad.
          constant_values (`float` or `Iterable[float]`, *optional*):
              The value to use for the padding if `mode` is `"constant"`.
          return_pixel_mask (`bool`, *optional*, defaults to `True`):
              Whether to return a pixel mask.
          return_tensors (`str` or `TensorType`, *optional*):
              The type of tensors to return. Can be one of:
                  - Unset: Return a list of `np.ndarray`.
                  - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                  - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                  - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                  - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
          data_format (`str` or `ChannelDimension`, *optional*):
              The channel dimension format of the image. If not provided, it will be the same as the input image.
          input_data_format (`ChannelDimension` or `str`, *optional*):
              The channel dimension format of the input image. If not provided, it will be inferred.
      """
      pad_size = get_max_height_width(
          images, input_data_format=input_data_format
      )
  
      padded_images = [
          self._pad_image(
              image,
              pad_size,
              constant_values=constant_values,
              data_format=data_format,
              input_data_format=input_data_format,
          )
          for image in images
      ]
      data = {"pixel_values": padded_images}
  
      if return_pixel_mask:
          masks = [
              make_pixel_mask(
                  image=image,
                  output_size=pad_size,
                  input_data_format=input_data_format,
              )
              for image in images
          ]
          data["pixel_mask"] = masks
  
      return BatchFeature(data=data, tensor_type=return_tensors)
  
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 12 for calling the Mask2FormerImageProcessor obj's underlying member method code with example usage: obj.pad(images, constant_values, return_pixel_mask, return_tensors, data_format, input_data_format) and returns: BatchFeature