def sample_slat(
        self,
        cond: dict,
        coords: torch.Tensor,
        sampler_params: dict = {},
    ) -> sp.SparseTensor:
        """
        Sample structured latent with the given conditioning.
        
        Args:
            cond (dict): The conditioning information.
            coords (torch.Tensor): The coordinates of the sparse structure.
            sampler_params (dict): Additional parameters for the sampler.
        """
        # Sample structured latent
        flow_model = self.models['slat_flow_model']
        noise = sp.SparseTensor(
            feats=torch.randn(coords.shape[0], flow_model.in_channels).to(self.device),
            coords=coords,
        )
        sampler_params = {**self.slat_sampler_params, **sampler_params}
        slat = self.slat_sampler.sample(
            flow_model,
            noise,
            **cond,
            **sampler_params,
            verbose=True
        ).samples

        std = torch.tensor(self.slat_normalization['std'])[None].to(slat.device)
        mean = torch.tensor(self.slat_normalization['mean'])[None].to(slat.device)
        slat = slat * std + mean
        
        return slat

        Sample structured latent with the given conditioning.
        
        Args:
            cond (dict): The conditioning information.
            coords (torch.Tensor): The coordinates of the sparse structure.
            sampler_params (dict): Additional parameters for the sampler.
        
trellis/pipelines/trellis_image_to_3d.py