def step_correct(
        self,
        model_output: torch.Tensor,
        sigma_hat: float,
        sigma_prev: float,
        sample_hat: torch.Tensor,
        sample_prev: torch.Tensor,
        derivative: torch.Tensor,
        return_dict: bool = True,
    ) -> Union[KarrasVeOutput, Tuple]:
        """
        Corrects the predicted sample based on the `model_output` of the network.

        Args:
            model_output (`torch.Tensor`):
                The direct output from learned diffusion model.
            sigma_hat (`float`): TODO
            sigma_prev (`float`): TODO
            sample_hat (`torch.Tensor`): TODO
            sample_prev (`torch.Tensor`): TODO
            derivative (`torch.Tensor`): TODO
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`.

        Returns:
            prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO

        """
        pred_original_sample = sample_prev + sigma_prev * model_output
        derivative_corr = (sample_prev - pred_original_sample) / sigma_prev
        sample_prev = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

        if not return_dict:
            return (sample_prev, derivative)

        return KarrasVeOutput(
            prev_sample=sample_prev, derivative=derivative, pred_original_sample=pred_original_sample
        )

        Corrects the predicted sample based on the `model_output` of the network.

        Args:
            model_output (`torch.Tensor`):
                The direct output from learned diffusion model.
            sigma_hat (`float`): TODO
            sigma_prev (`float`): TODO
            sample_hat (`torch.Tensor`): TODO
            sample_prev (`torch.Tensor`): TODO
            derivative (`torch.Tensor`): TODO
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~schedulers.scheduling_ddpm.DDPMSchedulerOutput`] or `tuple`.

        Returns:
            prev_sample (TODO): updated sample in the diffusion chain. derivative (TODO): TODO

        
src/diffusers/schedulers/deprecated/scheduling_karras_ve.py