def step_correct(
        self,
        state: ScoreSdeVeSchedulerState,
        model_output: jnp.ndarray,
        sample: jnp.ndarray,
        key: random.KeyArray,
        return_dict: bool = True,
    ) -> Union[FlaxSdeVeOutput, Tuple]:
        """
        Correct the predicted sample based on the output model_output of the network. This is often run repeatedly
        after making the prediction for the previous timestep.

        Args:
            state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
            model_output (`jnp.ndarray`): direct output from learned diffusion model.
            sample (`jnp.ndarray`):
                current instance of sample being created by diffusion process.
            generator: random number generator.
            return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class

        Returns:
            [`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When
            returning a tuple, the first element is the sample tensor.

        """
        if state.timesteps is None:
            raise ValueError(
                "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
            )

        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
        # sample noise for correction
        key = random.split(key, num=1)
        noise = random.normal(key=key, shape=sample.shape)

        # compute step size from the model_output, the noise, and the snr
        grad_norm = jnp.linalg.norm(model_output)
        noise_norm = jnp.linalg.norm(noise)
        step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        step_size = step_size * jnp.ones(sample.shape[0])

        # compute corrected sample: model_output term and noise term
        step_size = step_size.flatten()
        step_size = broadcast_to_shape_from_left(step_size, sample.shape)
        prev_sample_mean = sample + step_size * model_output
        prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
            return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=prev_sample, state=state)

        Correct the predicted sample based on the output model_output of the network. This is often run repeatedly
        after making the prediction for the previous timestep.

        Args:
            state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
            model_output (`jnp.ndarray`): direct output from learned diffusion model.
            sample (`jnp.ndarray`):
                current instance of sample being created by diffusion process.
            generator: random number generator.
            return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class

        Returns:
            [`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When
            returning a tuple, the first element is the sample tensor.

        
promptist/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py