docstr: Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
  resized to keep the input aspect ratio.
  Args:
      image (`np.ndarray`):
          Image to resize.
      size (`Dict[str, int]`):
          Size of the output image.
      resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
          Resampling filter to use when resiizing the image.
      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.
docstr_tok: ["Resize","an","image",".","The","shortest","edge","of","the","image","is","resized","to","size","[","``","shortest_edge","''","]",",","with","the","longest","edge","resized","to","keep","the","input","aspect","ratio",".","Args",":","image","(","`","np.ndarray","`",")",":","Image","to","resize",".","size","(","`","Dict","[","str",",","int","]","`",")",":","Size","of","the","output","image",".","resample","(","`","PILImageResampling","`",",","*","optional","*",",","defaults","to","`","PILImageResampling.BICUBIC","`",")",":","Resampling","filter","to","use","when","resiizing","the","image",".","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","."]
code: def resize(
      self,
      image: np.ndarray,
      size: Dict[str, int],
      resample: PILImageResampling = PILImageResampling.BICUBIC,
      data_format: Optional[Union[str, ChannelDimension]] = None,
      **kwargs,
  ) -> np.ndarray:
      """
      Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
      resized to keep the input aspect ratio.
      Args:
          image (`np.ndarray`):
              Image to resize.
          size (`Dict[str, int]`):
              Size of the output image.
          resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
              Resampling filter to use when resiizing the image.
          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.
      """
      size = get_size_dict(
          size, default_to_square=True, height_width_order=True
      )
      # Hack(haoxuan): Bypass the shortest_edge detection. We hope to get a {"height": size[0], "width": size[1]}, where w=h.
      # if "shortest_edge" not in size:
      #     raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
      # output_size = get_resize_output_image_size(image, size=size["shortest_edge"], default_to_square=True)
      output_size = get_resize_output_image_size(
          image,
          size=(size["height"], size["width"]),
          default_to_square=True,
      )
      return resize(
          image,
          size=output_size,
          resample=resample,
          data_format=data_format,
          **kwargs,
      )
  
imports: import torch
  import torch.nn
  from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
  import math
  import numpy
  from typing import Dict
  from transformers.image_utils import PILImageResampling, ChannelDimension
  from transformers.image_processing_utils import get_size_dict
  from transformers.image_transforms import get_resize_output_image_size, resize
  from typing import List, Optional, Tuple, Union
label_desc: Use image node_id 1 for calling the CLIPImageProcessor_GIT obj's underlying member method code with example usage: obj.resize(image, size, resample, data_format) and returns: resize