def to_representation(self, x: sp.SparseTensor) -> List[Gaussian]:
        """
        Convert a batch of network outputs to 3D representations.

        Args:
            x: The [N x * x C] sparse tensor output by the network.

        Returns:
            list of representations
        """
        ret = []
        for i in range(x.shape[0]):
            representation = Gaussian(
                sh_degree=0,
                aabb=[-0.5, -0.5, -0.5, 1.0, 1.0, 1.0],
                mininum_kernel_size = self.rep_config['3d_filter_kernel_size'],
                scaling_bias = self.rep_config['scaling_bias'],
                opacity_bias = self.rep_config['opacity_bias'],
                scaling_activation = self.rep_config['scaling_activation']
            )
            xyz = (x.coords[x.layout[i]][:, 1:].float() + 0.5) / self.resolution
            for k, v in self.layout.items():
                if k == '_xyz':
                    offset = x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape'])
                    offset = offset * self.rep_config['lr'][k]
                    if self.rep_config['perturb_offset']:
                        offset = offset + self.offset_perturbation
                    offset = torch.tanh(offset) / self.resolution * 0.5 * self.rep_config['voxel_size']
                    _xyz = xyz.unsqueeze(1) + offset
                    setattr(representation, k, _xyz.flatten(0, 1))
                else:
                    feats = x.feats[x.layout[i]][:, v['range'][0]:v['range'][1]].reshape(-1, *v['shape']).flatten(0, 1)
                    feats = feats * self.rep_config['lr'][k]
                    setattr(representation, k, feats)
            ret.append(representation)
        return ret

        Convert a batch of network outputs to 3D representations.

        Args:
            x: The [N x * x C] sparse tensor output by the network.

        Returns:
            list of representations
        
trellis/models/structured_latent_vae/decoder_gs.py