code: def datalist(self):
      torch.manual_seed(0)
      datalist_source = [
          Data(
              pos=torch.randn((self.num_points, 3)),
              x=self._feature,
              pair_ind=self._pair_ind,
              size_pair_ind=torch.tensor([len(self._pair_ind)]),
          )
          for i in range(self.batch_size)
      ]
      datalist_target = [
          Data(
              pos=torch.randn((self.num_points, 3)),
              x=self._feature,
              pair_ind=self._pair_ind,
              size_pair_ind=torch.tensor([len(self._pair_ind)]),
          )
          for i in range(self.batch_size)
      ]
      if self._transform:
          datalist_source = [
              self._transform(d.clone()) for d in datalist_source
          ]
          datalist_target = [
              self._transform(d.clone()) for d in datalist_target
          ]
      if self._ms_transform:
          datalist_source = [
              self._ms_transform(d.clone()) for d in datalist_source
          ]
          datalist_target = [
              self._ms_transform(d.clone()) for d in datalist_target
          ]
      datalist = [
          Pair.make_pair(datalist_source[i], datalist_target[i])
          for i in range(self.batch_size)
      ]
      return datalist
  
imports: import numpy
  import torch
  from torch_geometric.data import Data, Batch
  from torch_points3d.datasets.batch import SimpleBatch
  from torch_points3d.core.data_transform import MultiScaleTransform
  from torch_points3d.datasets.multiscale_data import MultiScaleBatch
  from torch_points3d.datasets.registration.pair import Pair, PairBatch, PairMultiScaleBatch, DensePairBatch
label_desc: Use image node_id 2 for calling the PairMockDataset obj's underlying member method code with example usage: obj.datalist() and returns: datalist