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

        Args:
            state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class.
            model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
            sigma_hat (`float`): TODO
            sigma_prev (`float`): TODO
            sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO
            sample_prev (`torch.FloatTensor` or `np.ndarray`): TODO
            derivative (`torch.FloatTensor` or `np.ndarray`): TODO
            return_dict (`bool`): option for returning tuple rather than FlaxKarrasVeOutput 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, state)

        return FlaxKarrasVeOutput(prev_sample=sample_prev, derivative=derivative, state=state)

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

        Args:
            state (`KarrasVeSchedulerState`): the `FlaxKarrasVeScheduler` state data class.
            model_output (`torch.FloatTensor` or `np.ndarray`): direct output from learned diffusion model.
            sigma_hat (`float`): TODO
            sigma_prev (`float`): TODO
            sample_hat (`torch.FloatTensor` or `np.ndarray`): TODO
            sample_prev (`torch.FloatTensor` or `np.ndarray`): TODO
            derivative (`torch.FloatTensor` or `np.ndarray`): TODO
            return_dict (`bool`): option for returning tuple rather than FlaxKarrasVeOutput class

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

        
promptist/diffusers/src/diffusers/schedulers/scheduling_karras_ve_flax.py