src/transformers/models/swin2sr/image_processing_swin2sr.py/Swin2SRImageProcessor/preprocess
class Swin2SRImageProcessor:    def preprocess(
        self,
        images: ImageInput,
        do_rescale: Optional[bool] = None,
        rescale_factor: Optional[float] = None,
        do_pad: Optional[bool] = None,
        pad_size: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST,
        **kwargs,
    ):
        """
        Preprocess an image or batch of images.

        Args:
            images (`ImageInput`):
                Image to preprocess.
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image values between [0 - 1].
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
            do_pad (`bool`, *optional*, defaults to `True`):
                Whether to pad the image to make the height and width divisible by `window_size`.
            pad_size (`int`, *optional*, defaults to `32`):
                The size of the sliding window for the local attention.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                - Unset: Return a list of `np.ndarray`.
                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output 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.
                - Unset: Use the channel dimension format of the input image.
        """
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        do_pad = do_pad if do_pad is not None else self.do_pad
        pad_size = pad_size if pad_size is not None else self.pad_size

        images = make_list_of_images(images)

        if not valid_images(images):
            raise ValueError(
                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "torch.Tensor, tf.Tensor or jax.ndarray."
            )

        if do_rescale and rescale_factor is None:
            raise ValueError("Rescale factor must be specified if do_rescale is True.")

        # All transformations expect numpy arrays.
        images = [to_numpy_array(image) for image in images]

        if do_rescale:
            images = [self.rescale(image=image, scale=rescale_factor) for image in images]

        if do_pad:
            images = [self.pad(image, size=pad_size) for image in images]

        images = [to_channel_dimension_format(image, data_format) for image in images]

        data = {"pixel_values": images}
        return BatchFeature(data=data, tensor_type=return_tensors)