code: def compute_loss(self):
      self.loss = 0
  
      # Collect internal losses and set them with self and them to self for later tracking
      if self.lambda_internal_losses:
          self.loss_internal = self.collect_internal_losses(
              lambda_weight=self.lambda_internal_losses
          )
          self.loss += self.loss_internal
  
      if self.mode == "match":
          self.loss_reg = self.compute_loss_match()
      elif self.mode == "label":
          self.loss_reg = self.compute_loss_label()
  
      self.loss += self.loss_reg
  
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 5 for calling the FragmentKPConv obj's underlying member method code with example usage: obj.compute_loss() without return types