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: Rescale the image by the given factor. image = image * rescale_factor.
  
  Args:
      image (`np.ndarray`):
          Image to rescale.
      rescale_factor (`float`):
          The value to use for rescaling.
      data_format (`str` or `ChannelDimension`, *optional*):
          The channel dimension format for the output image. If unset, the channel dimension format of the input
          image is used. Can be one of:
          - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
          - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
      input_data_format (`str` or `ChannelDimension`, *optional*):
          The channel dimension format for the input image. If unset, is inferred from the input image. Can be
          one of:
          - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
          - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
code: def rescale(
      self,
      image: np.ndarray,
      rescale_factor: float,
      data_format: Optional[Union[str, ChannelDimension]] = None,
      input_data_format: Optional[Union[str, ChannelDimension]] = None,
  ) -> np.ndarray:
      """
      Rescale the image by the given factor. image = image * rescale_factor.
  
      Args:
          image (`np.ndarray`):
              Image to rescale.
          rescale_factor (`float`):
              The value to use for rescaling.
          data_format (`str` or `ChannelDimension`, *optional*):
              The channel dimension format for the output image. If unset, the channel dimension format of the input
              image is used. Can be one of:
              - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
              - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
          input_data_format (`str` or `ChannelDimension`, *optional*):
              The channel dimension format for the input image. If unset, is inferred from the input image. Can be
              one of:
              - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
              - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
      """
      return rescale(
          image,
          rescale_factor,
          data_format=data_format,
          input_data_format=input_data_format,
      )
  
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 9 for calling the DetaImageProcessor obj's underlying member method code with example usage: obj.rescale(image, rescale_factor, data_format, input_data_format) and returns: rescale