code: def load_model_config(task, model_type, model_name):
      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", model_name)
          config.update("data.task", task)
          config.update("data.grid_size", 1)
      else:
          config = OmegaConf.create(
              {"models": OmegaConf.load(models_conf)}
          )
          OmegaConf.update(config, "model_name", model_name, merge=True)
          OmegaConf.update(config, "data.task", task, merge=True)
          OmegaConf.update(config, "data.grid_size", 1, merge=True)
  
      return config
  
imports: import unittest
  import omegaconf
  from omegaconf import OmegaConf
  import os
  import sys
  from glob import glob
  import torch
  from test.mockdatasets import MockDatasetGeometric, MockDataset
  from test.mockdatasets import PairMockDatasetGeometric, PairMockDataset
  from test.utils import test_hasgrad
  from torch_points3d.models.model_factory import instantiate_model
  from torch_points3d.core.data_transform import XYZFeature, GridSampling3D
  from torch_points3d.utils.model_building_utils.model_definition_resolver import resolve_model
  from torch_points3d.datasets.registration.pair import Pair, PairBatch, PairMultiScaleBatch, DensePairBatch
  from torch_geometric.transforms import Compose
label_desc: Use image node_id 1 for calling a global function with example usage: load_model_config(task, model_type, model_name) and returns: config