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).
code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
      requires_backends(self, "vision")
      self.check_model_type(MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES)
  
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 1 to create a new ImageToImagePipeline object from inherited base classes: Pipeline with example: obj = ImageToImagePipeline()