def step(
        self,
        model_output: torch.Tensor,
        timestep: torch.long,
        sample: torch.LongTensor,
        generator: Optional[torch.Generator] = None,
        return_dict: bool = True,
    ) -> Union[VQDiffusionSchedulerOutput, Tuple]:
        """
        Predict the sample from the previous timestep by the reverse transition distribution. See
        [`~VQDiffusionScheduler.q_posterior`] for more details about how the distribution is computer.

        Args:
            log_p_x_0: (`torch.Tensor` of shape `(batch size, num classes - 1, num latent pixels)`):
                The log probabilities for the predicted classes of the initial latent pixels. Does not include a
                prediction for the masked class as the initial unnoised image cannot be masked.
            t (`torch.long`):
                The timestep that determines which transition matrices are used.
            x_t (`torch.LongTensor` of shape `(batch size, num latent pixels)`):
                The classes of each latent pixel at time `t`.
            generator (`torch.Generator`, or `None`):
                A random number generator for the noise applied to `p(x_{t-1} | x_t)` before it is sampled from.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput`] or
                `tuple`.

        Returns:
            [`~schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput`] or `tuple`:
                If return_dict is `True`, [`~schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput`] is
                returned, otherwise a tuple is returned where the first element is the sample tensor.
        """
        if timestep == 0:
            log_p_x_t_min_1 = model_output
        else:
            log_p_x_t_min_1 = self.q_posterior(model_output, sample, timestep)

        log_p_x_t_min_1 = gumbel_noised(log_p_x_t_min_1, generator)

        x_t_min_1 = log_p_x_t_min_1.argmax(dim=1)

        if not return_dict:
            return (x_t_min_1,)

        return VQDiffusionSchedulerOutput(prev_sample=x_t_min_1)

        Predict the sample from the previous timestep by the reverse transition distribution. See
        [`~VQDiffusionScheduler.q_posterior`] for more details about how the distribution is computer.

        Args:
            log_p_x_0: (`torch.Tensor` of shape `(batch size, num classes - 1, num latent pixels)`):
                The log probabilities for the predicted classes of the initial latent pixels. Does not include a
                prediction for the masked class as the initial unnoised image cannot be masked.
            t (`torch.long`):
                The timestep that determines which transition matrices are used.
            x_t (`torch.LongTensor` of shape `(batch size, num latent pixels)`):
                The classes of each latent pixel at time `t`.
            generator (`torch.Generator`, or `None`):
                A random number generator for the noise applied to `p(x_{t-1} | x_t)` before it is sampled from.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput`] or
                `tuple`.

        Returns:
            [`~schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput`] or `tuple`:
                If return_dict is `True`, [`~schedulers.scheduling_vq_diffusion.VQDiffusionSchedulerOutput`] is
                returned, otherwise a tuple is returned where the first element is the sample tensor.
        
src/diffusers/schedulers/scheduling_vq_diffusion.py