code: def forward(self, *args, **kwargs):
      self.output = self.apply_nn(
          self.input, self.pre_computed, self.upsample
      )
      if self.match is None:
          return self.output
  
      self.output_target = self.apply_nn(
          self.input_target,
          self.pre_computed_target,
          self.upsample_target,
      )
      self.compute_loss()
  
      return self.output
  
imports: from typing import Any
  import torch
  import torch.nn.functional
  from torch.nn import Linear
  from torch.nn import Sequential
  from torch_geometric.data import Data
  from torch_geometric.nn import global_mean_pool
  import logging
  from torch_points3d.core.losses import *
  from torch_points3d.core.common_modules import MLP
  from torch_points3d.core.common_modules import FastBatchNorm1d
  from torch_points3d.core.base_conv.partial_dense import *
  from torch_points3d.models.registration.base import FragmentBaseModel
  from torch_points3d.models.base_architectures import BackboneBasedModel
  from torch_points3d.models.registration.base import create_batch_siamese
  from torch_points3d.models.base_model import BaseModel
  from torch_points3d.models.base_architectures.unet import UnwrappedUnetBasedModel
  from torch_points3d.modules.KPConv import *
  from torch_points3d.datasets.registration.pair import PairMultiScaleBatch
label_desc: Use image node_id 4 for calling the FragmentKPConv obj's underlying member method code with example usage: obj.forward() and returns: self, self