code: def __init__(
      self,
      backbone,
      input_nc=1,
      grid_size=0.05,
      pointnet_nn=None,
      pre_mlp_nn=None,
      post_mlp_nn=[64, 64, 32],
      add_pos=False,
      add_pre_x=False,
      backend="minkowski",
      aggr=None,
  ):
      if input_nc is None:
          input_nc = 1
      nn.Module.__init__(self)
      self.unet = SparseConv3d(
          architecture="unet",
          input_nc=input_nc,
          config=backbone,
          backend=backend,
      )
      if pre_mlp_nn is not None:
          self.pre_mlp = MLP(pre_mlp_nn)
      else:
          self.pre_mlp = torch.nn.Identity()
      if pointnet_nn is not None:
          self.pointnet = MLP(pointnet_nn)
      else:
          self.pointnet = torch.nn.Identity()
      self.post_mlp = MLP(post_mlp_nn)
      self._grid_size = grid_size
      self.add_pos = add_pos
      self.add_pre_x = add_pre_x
      self.aggr = aggr
  
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 1 to create a new UnetMSparseConv3d object from inherited base classes: nn with example: obj = UnetMSparseConv3d(backbone, input_nc, grid_size, pointnet_nn, pre_mlp_nn, post_mlp_nn, add_pos, add_pre_x, backend, aggr)