def step_correct(
        self,
        model_output: torch.FloatTensor,
        sigma_hat: float,
        sigma_prev: float,
        sample_hat: torch.FloatTensor,
        sample_prev: torch.FloatTensor,
        derivative: torch.FloatTensor,
        return_dict: bool = True,
    ) -> Union[KarrasVeOutput, Tuple]:
        """
        Correct the predicted sample based on the output model_output of the network. TODO complete description

        Args:
            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
            sigma_hat (`float`): TODO
            sigma_prev (`float`): TODO
            sample_hat (`torch.FloatTensor`): TODO
            sample_prev (`torch.FloatTensor`): TODO
            derivative (`torch.FloatTensor`): TODO
            return_dict (`bool`): option for returning tuple rather than KarrasVeOutput class

        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
        )

        Correct the predicted sample based on the output model_output of the network. TODO complete description

        Args:
            model_output (`torch.FloatTensor`): direct output from learned diffusion model.
            sigma_hat (`float`): TODO
            sigma_prev (`float`): TODO
            sample_hat (`torch.FloatTensor`): TODO
            sample_prev (`torch.FloatTensor`): TODO
            derivative (`torch.FloatTensor`): TODO
            return_dict (`bool`): option for returning tuple rather than KarrasVeOutput class

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

        
promptist/diffusers/src/diffusers/schedulers/scheduling_karras_ve.py