class_docstr: Image to Image pipeline using any `AutoModelForImageToImage`. This pipeline generates an image based on a previous
  image input.
  
  Example:
  
  ```python
  >>> from PIL import Image
  >>> import requests
  
  >>> from transformers import pipeline
  
  >>> upscaler = pipeline("image-to-image", model="caidas/swin2SR-classical-sr-x2-64")
  >>> img = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
  >>> img = img.resize((64, 64))
  >>> upscaled_img = upscaler(img)
  >>> img.size
  (64, 64)
  
  >>> upscaled_img.size
  (144, 144)
  ```
  
  This image to image pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"image-to-image"`.
  
  See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=image-to-image).
docstr: Transform the image(s) passed as inputs.
  
  Args:
      images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
          The pipeline handles three types of images:
  
          - A string containing a http link pointing to an image
          - A string containing a local path to an image
          - An image loaded in PIL directly
  
          The pipeline accepts either a single image or a batch of images, which must then be passed as a string.
          Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL
          images.
      timeout (`float`, *optional*, defaults to None):
          The maximum time in seconds to wait for fetching images from the web. If None, no timeout is used and
          the call may block forever.
  
  Return:
      An image (Image.Image) or a list of images (List["Image.Image"]) containing result(s). If the input is a
      single image, the return will be also a single image, if the input is a list of several images, it will
      return a list of transformed images.
code: def __call__(
      self,
      images: Union[str, List[str], "Image.Image", List["Image.Image"]],
      **kwargs,
  ) -> Union["Image.Image", List["Image.Image"]]:
      """
      Transform the image(s) passed as inputs.
  
      Args:
          images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
              The pipeline handles three types of images:
  
              - A string containing a http link pointing to an image
              - A string containing a local path to an image
              - An image loaded in PIL directly
  
              The pipeline accepts either a single image or a batch of images, which must then be passed as a string.
              Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL
              images.
          timeout (`float`, *optional*, defaults to None):
              The maximum time in seconds to wait for fetching images from the web. If None, no timeout is used and
              the call may block forever.
  
      Return:
          An image (Image.Image) or a list of images (List["Image.Image"]) containing result(s). If the input is a
          single image, the return will be also a single image, if the input is a list of several images, it will
          return a list of transformed images.
      """
      return super().__call__(images, **kwargs)
  
file_path: transformers/src/transformers/pipelines/image_to_image.py
imports: from typing import List, Union
  import numpy
  from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
  from .base import PIPELINE_INIT_ARGS, Pipeline
label_desc: Use image node_id 3 for calling the ImageToImagePipeline obj's underlying member method code with example usage: obj.__call__(images) and returns: super