class_docstr: Constructs a Donut image processor.
  
  Args:
      do_resize (`bool`, *optional*, defaults to `True`):
          Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
          `do_resize` in the `preprocess` method.
      size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
          Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with
          the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess`
          method.
      resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
          Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
      do_thumbnail (`bool`, *optional*, defaults to `True`):
          Whether to resize the image using thumbnail method.
      do_align_long_axis (`bool`, *optional*, defaults to `False`):
          Whether to align the long axis of the image with the long axis of `size` by rotating by 90 degrees.
      do_pad (`bool`, *optional*, defaults to `True`):
          Whether to pad the image. If `random_padding` is set to `True` in `preprocess`, each image is padded with a
          random amont of padding on each size, up to the largest image size in the batch. Otherwise, all images are
          padded to the largest image size in the batch.
      do_rescale (`bool`, *optional*, defaults to `True`):
          Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
          the `preprocess` method.
      rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
          Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
          method.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method.
      image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
          Mean to use if normalizing the image. This is a float or list of floats the length of the number of
          channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
      image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
          Image standard deviation.
code: def pad(self, *args, **kwargs):
      logger.info(
          "pad is deprecated and will be removed in version 4.27. Please use pad_image instead."
      )
      return self.pad_image(*args, **kwargs)
  
file_path: transformers/src/transformers/models/donut/image_processing_donut.py
imports: from typing import Dict, List, Optional, Union
  import numpy
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import get_resize_output_image_size, pad, resize, to_channel_dimension_format
  from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images
  from ...utils import TensorType, logging
  from ...utils.import_utils import is_vision_available
label_desc: Use image node_id 4 for calling the DonutImageProcessor obj's underlying member method code with example usage: obj.pad() and returns: self