code: def get_input(self):
      if self.match is not None:
          input = self.input.clone()
          input_target = self.input_target.clone()
          input.ind = self.match[:, 0]
          input_target.ind = self.match[:, 1]
          input.size = self.size_match
          input_target.size = self.size_match
          return input, input_target
      else:
          return self.input, None
  
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 4 for calling the BaseMS_SparseConv3d obj's underlying member method code with example usage: obj.get_input() and returns: input,  input_target, self,  None