Sampling from VAE's latent space

[seakforzq]

Is your feature request related to a problem? Please describe.
How to sample from the vae's latent space?

        target_key = sin.coords_key
        out_cls, targets, sout, means, log_vars, zs = net(sin, target_key)
        num_layers, BCE = len(out_cls), 0
        losses = []
        for out_cl, target in zip(out_cls, targets):
            curr_loss = crit(out_cl.F.squeeze(), target.type(out_cl.F.dtype).to(device))
            losses.append(curr_loss.item())
            BCE += curr_loss / num_layers

        KLD = -0.5 * torch.mean(
            torch.sum(1 + log_vars.F - means.F.pow(2) - log_vars.F.exp(), 1)
        )
        loss = KLD + BCE

        print(loss)

        batch_coords, batch_feats = sout.decomposed_coordinates_and_features
        for b, (coords, feats) in enumerate(zip(batch_coords, batch_feats)):
            pcd = PointCloud(coords)
            pcd.estimate_normals()
            pcd.translate([0.6 * config.resolution, 0, 0])
            pcd.rotate(M)
            opcd = PointCloud(data_dict["xyzs"][b])
            opcd.translate([-0.6 * config.resolution, 0, 0])
            opcd.estimate_normals()
            opcd.rotate(M)
            o3d.visualization.draw_geometries([pcd, opcd])

            n_vis += 1
            if n_vis > config.max_visualization:
                return

Describe the solution you'd like
The code above only reaches the reconstruction purpose, and target_key from sin is needed, can you help me with sampling from the latent space?

Describe alternatives you've considered
NO

Additional context
NO

[seakforzq]

And why we need gt_target to prune the upsampled sparsetensor?

        # If training, force target shape generation, use net.eval() to disable
        if self.training:
            keep1 += target

I think the network will not learn how to pruning the output in this way right?