def sample(
        self,
        model,
        noise,
        cond,
        neg_cond,
        steps: int = 50,
        rescale_t: float = 1.0,
        cfg_strength: float = 3.0,
        cfg_interval: Tuple[float, float] = (0.0, 1.0),
        verbose: bool = True,
        **kwargs
    ):
        """
        Generate samples from the model using Euler method.
        
        Args:
            model: The model to sample from.
            noise: The initial noise tensor.
            cond: conditional information.
            neg_cond: negative conditional information.
            steps: The number of steps to sample.
            rescale_t: The rescale factor for t.
            cfg_strength: The strength of classifier-free guidance.
            cfg_interval: The interval for classifier-free guidance.
            verbose: If True, show a progress bar.
            **kwargs: Additional arguments for model_inference.

        Returns:
            a dict containing the following
            - 'samples': the model samples.
            - 'pred_x_t': a list of prediction of x_t.
            - 'pred_x_0': a list of prediction of x_0.
        """
        return super().sample(model, noise, cond, steps, rescale_t, verbose, neg_cond=neg_cond, cfg_strength=cfg_strength, cfg_interval=cfg_interval, **kwargs)

        Generate samples from the model using Euler method.
        
        Args:
            model: The model to sample from.
            noise: The initial noise tensor.
            cond: conditional information.
            neg_cond: negative conditional information.
            steps: The number of steps to sample.
            rescale_t: The rescale factor for t.
            cfg_strength: The strength of classifier-free guidance.
            cfg_interval: The interval for classifier-free guidance.
            verbose: If True, show a progress bar.
            **kwargs: Additional arguments for model_inference.

        Returns:
            a dict containing the following
            - 'samples': the model samples.
            - 'pred_x_t': a list of prediction of x_t.
            - 'pred_x_0': a list of prediction of x_0.
        
trellis/pipelines/samplers/flow_euler.py