def __call__(
        self,
        measurement: torch.Tensor,
        operator: torch.nn.Module,
        loss_fn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
        batch_size: int = 1,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        num_inference_steps: int = 1000,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        zeta: float = 0.3,
    ) -> Union[ImagePipelineOutput, Tuple]:
        r"""
        The call function to the pipeline for generation.

        Args:
            measurement (`torch.Tensor`, *required*):
                A 'torch.Tensor', the corrupted image
            operator (`torch.nn.Module`, *required*):
                A 'torch.nn.Module', the operator generating the corrupted image
            loss_fn (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *required*):
                A 'Callable[[torch.Tensor, torch.Tensor], torch.Tensor]', the loss function used
                between the measurements, for most of the cases using RMSE is fine.
            batch_size (`int`, *optional*, defaults to 1):
                The number of images to generate.
            generator (`torch.Generator`, *optional*):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            num_inference_steps (`int`, *optional*, defaults to 1000):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.

        Example:

        ```py
        >>> from diffusers import DDPMPipeline

        >>> # load model and scheduler
        >>> pipe = DDPMPipeline.from_pretrained("google/ddpm-cat-256")

        >>> # run pipeline in inference (sample random noise and denoise)
        >>> image = pipe().images[0]

        >>> # save image
        >>> image.save("ddpm_generated_image.png")
        ```

        Returns:
            [`~pipelines.ImagePipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
                returned where the first element is a list with the generated images
        """
        # Sample gaussian noise to begin loop
        if isinstance(self.unet.config.sample_size, int):
            image_shape = (
                batch_size,
                self.unet.config.in_channels,
                self.unet.config.sample_size,
                self.unet.config.sample_size,
            )
        else:
            image_shape = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

        if self.device.type == "mps":
            # randn does not work reproducibly on mps
            image = randn_tensor(image_shape, generator=generator)
            image = image.to(self.device)
        else:
            image = randn_tensor(image_shape, generator=generator, device=self.device)

        # set step values
        self.scheduler.set_timesteps(num_inference_steps)

        for t in self.progress_bar(self.scheduler.timesteps):
            with torch.enable_grad():
                # 1. predict noise model_output
                image = image.requires_grad_()
                model_output = self.unet(image, t).sample

                # 2. compute previous image x'_{t-1} and original prediction x0_{t}
                scheduler_out = self.scheduler.step(model_output, t, image, generator=generator)
                image_pred, origi_pred = scheduler_out.prev_sample, scheduler_out.pred_original_sample

                # 3. compute y'_t = f(x0_{t})
                measurement_pred = operator(origi_pred)

                # 4. compute loss = d(y, y'_t-1)
                loss = loss_fn(measurement, measurement_pred)
                loss.backward()

                print("distance: {0:.4f}".format(loss.item()))

                with torch.no_grad():
                    image_pred = image_pred - zeta * image.grad
                    image = image_pred.detach()

        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).numpy()
        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=image)

        The call function to the pipeline for generation.

        Args:
            measurement (`torch.Tensor`, *required*):
                A 'torch.Tensor', the corrupted image
            operator (`torch.nn.Module`, *required*):
                A 'torch.nn.Module', the operator generating the corrupted image
            loss_fn (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, *required*):
                A 'Callable[[torch.Tensor, torch.Tensor], torch.Tensor]', the loss function used
                between the measurements, for most of the cases using RMSE is fine.
            batch_size (`int`, *optional*, defaults to 1):
                The number of images to generate.
            generator (`torch.Generator`, *optional*):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            num_inference_steps (`int`, *optional*, defaults to 1000):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            output_type (`str`, *optional*, defaults to `"pil"`):
                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.

        Example:

        ```py
        >>> from diffusers import DDPMPipeline

        >>> # load model and scheduler
        >>> pipe = DDPMPipeline.from_pretrained("google/ddpm-cat-256")

        >>> # run pipeline in inference (sample random noise and denoise)
        >>> image = pipe().images[0]

        >>> # save image
        >>> image.save("ddpm_generated_image.png")
        ```

        Returns:
            [`~pipelines.ImagePipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
                returned where the first element is a list with the generated images
        
examples/community/dps_pipeline.py