code: def load_config(task, model_type) -> DictConfig:
      models_conf = os.path.join(
          ROOT, "conf/models/{}/{}.yaml".format(task, model_type)
      )
      if omegaconf.__version__ == "1.4.1":
          config = OmegaConf.load(models_conf)
          config.update("model_name", "pointnet2")
          config.update("data.task", "segmentation")
      else:
          config = OmegaConf.create(
              {"models": OmegaConf.load(models_conf)}
          )
          OmegaConf.update(
              config, "model_name", "pointnet2", merge=True
          )
          OmegaConf.update(
              config, "data.task", "segmentation", merge=True
          )
  
      return config
  
imports: import unittest
  import omegaconf
  from omegaconf import OmegaConf, DictConfig
  import os
  import sys
  import hydra
  import shutil
  import torch
  from mockdatasets import MockDatasetGeometric
  from torch_points3d.models.model_factory import instantiate_model
  from torch_points3d.utils.model_building_utils.model_definition_resolver import resolve_model
  from torch_points3d.metrics.model_checkpoint import ModelCheckpoint
label_desc: Use image node_id 1 for calling a global function with example usage: load_config(task, model_type) and returns: config