class_docstr: Constructs a Deformable DETR image processor.
  
  Args:
      format (`str`, *optional*, defaults to `"coco_detection"`):
          Data format of the annotations. One of "coco_detection" or "coco_panoptic".
      do_resize (`bool`, *optional*, defaults to `True`):
          Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be
          overridden by the `do_resize` parameter in the `preprocess` method.
      size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 800, "longest_edge": 1333}`):
          Size of the image's (height, width) dimensions after resizing. Can be overridden by the `size` parameter in
          the `preprocess` method.
      resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
          Resampling filter to use if resizing the image.
      do_rescale (`bool`, *optional*, defaults to `True`):
          Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the
          `do_rescale` parameter 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 the `rescale_factor` parameter in the
          `preprocess` method.
      do_normalize:
          Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the
          `preprocess` method.
      image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
          Mean values to use when normalizing the image. Can be a single value or a list of values, one for each
          channel. Can be overridden by the `image_mean` parameter in the `preprocess` method.
      image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
          Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one
          for each channel. Can be overridden by the `image_std` parameter in the `preprocess` method.
      do_pad (`bool`, *optional*, defaults to `True`):
          Controls whether to pad the image to the largest image in a batch and create a pixel mask. Can be
          overridden by the `do_pad` parameter in the `preprocess` method.
docstr: Pad an image with zeros to the given size.
code: def _pad_image(
      self,
      image: np.ndarray,
      output_size: Tuple[int, int],
      constant_values: Union[float, Iterable[float]] = 0,
      data_format: Optional[ChannelDimension] = None,
      input_data_format: Optional[Union[str, ChannelDimension]] = None,
  ) -> np.ndarray:
      """
      Pad an image with zeros to the given size.
      """
      input_height, input_width = get_image_size(
          image, channel_dim=input_data_format
      )
      output_height, output_width = output_size
  
      pad_bottom = output_height - input_height
      pad_right = output_width - input_width
      padding = ((0, pad_bottom), (0, pad_right))
      padded_image = pad(
          image,
          padding,
          mode=PaddingMode.CONSTANT,
          constant_values=constant_values,
          data_format=data_format,
          input_data_format=input_data_format,
      )
      return padded_image
  
imports: import pathlib
  from typing import Any, Callable, Dict, Iterable, List, Optional, 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, pad, rescale, resize, rgb_to_id, to_channel_dimension_format
  from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, AnnotationFormat, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_batched, is_scaled_image, to_numpy_array, valid_images, validate_annotations
  from ...utils import is_flax_available, is_jax_tensor, is_tf_available, is_tf_tensor, is_torch_available, is_torch_tensor, is_torchvision_available, is_vision_available, logging
  from ...utils.generic import TensorType
label_desc: Use image node_id 11 for calling the DetaImageProcessor obj's underlying member method code with example usage: obj._pad_image(image, output_size, constant_values, data_format, input_data_format) and returns: padded_image