code: def forward(self, data, **kwargs):
      inp = data.clone()
      if self.add_pos:
          inp.x = self.pointnet(
              torch.cat([inp.x, data.pos - data.pos.mean(0)], 1)
          )
      else:
          inp.x = self.pointnet(inp.x)
      pre_x = inp.x
      d, cluster = self._prepare_data(inp)
      d.x = self.pre_mlp(d.x)
      d = self.unet.forward(d)
      inp_post_mlp = d.x[cluster]
      if self.add_pos:
          inp_post_mlp = torch.cat(
              [inp_post_mlp, data.pos - data.pos.mean(0)], 1
          )
      if self.add_pre_x:
          inp_post_mlp = torch.cat([inp_post_mlp, pre_x], 1)
      data.x = self.post_mlp(inp_post_mlp)
      return data
  
imports: import torch
  import torch.nn
  from torch.nn import LeakyReLU, Linear, Sequential
  from torch_geometric.nn.pool.consecutive import consecutive_cluster
  from torch_geometric.nn import voxel_grid
  from torch_geometric.data import Batch
  from torch_scatter import scatter_max, scatter_mean
  from torch_points3d.applications.sparseconv3d import SparseConv3d
  from torch_points3d.core.common_modules import FastBatchNorm1d, Seq
  from torch_points3d.core.common_modules import MLP
  from torch_points3d.models.registration.base import FragmentBaseModel
label_desc: Use image node_id 5 for calling the UnetMSparseConv3d obj's underlying member method code with example usage: obj.forward(data) and returns: data