code: def forward(self, data, **kwargs):
      batch_obj = Batch()
      data, data_skip = data
      x, pos, batch = data.x, data.pos, data.batch
      x_skip, pos_skip, batch_skip = (
          data_skip.x,
          data_skip.pos,
          data_skip.batch,
      )
  
      if self.neighbour_finder is not None:
          row, col = self.neighbour_finder(
              pos, pos_skip, batch, batch_skip
          )
          edge_index = torch.stack([col, row], dim=0)
      else:
          edge_index = None
  
      x = self.conv(x, pos, pos_skip, batch, batch_skip, edge_index)
  
      if x_skip is not None and self._skip:
          x = torch.cat([x, x_skip], dim=1)
  
      if hasattr(self, "nn"):
          batch_obj.x = self.nn(x)
      else:
          batch_obj.x = x
      copy_from_to(data_skip, batch_obj)
      return batch_obj
  
imports: from abc import abstractmethod
  from typing import *
  import torch
  from torch.nn import Linear, ReLU, LeakyReLU, BatchNorm1d, Dropout
  from torch_geometric.nn import knn_interpolate, fps, radius, global_max_pool, global_mean_pool, knn
  from torch_geometric.data import Batch
  from torch_points3d.core.base_conv.base_conv import *
  from torch_points3d.core.common_modules import *
  from torch_points3d.core.spatial_ops import *
label_desc: Use image node_id 3 for calling the BaseConvolutionUp obj's underlying member method code with example usage: obj.forward(data) and returns: batch_obj