code: def _build_unet(self):
      if self._config:
          model_config = self._config
      else:
          path_to_model = os.path.join(
              PATH_TO_CONFIG,
              "unet_{}_{}.yaml".format(
                  self.num_layers,
                  "ms" if self.kwargs["multiscale"] else "ss",
              ),
          )
          model_config = OmegaConf.load(path_to_model)
      ModelFactory.resolve_model(
          model_config, self.num_features, self._kwargs
      )
      modules_lib = sys.modules[__name__]
      return PointNet2Unet(
          model_config, None, None, modules_lib, **self.kwargs
      )
  
imports: import os
  import sys
  from omegaconf import DictConfig, OmegaConf
  import logging
  from torch_points3d.applications.modelfactory import ModelFactory
  from torch_points3d.modules.pointnet2 import *
  from torch_points3d.core.base_conv.dense import DenseFPModule
  from torch_points3d.models.base_architectures.unet import UnwrappedUnetBasedModel
  from torch_points3d.datasets.multiscale_data import MultiScaleBatch
  from torch_points3d.core.common_modules.dense_modules import Conv1D
  from torch_points3d.core.common_modules.base_modules import Seq
  from .utils import extract_output_nc
label_desc: Use image node_id 1 for calling the PointNet2Factory obj's underlying member method code with example usage: obj._build_unet() and returns: PointNet2Unet