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)
      else:
          config = OmegaConf.create(
              {"models": OmegaConf.load(models_conf)}
          )
          OmegaConf.update(config, "model_name", model_name, merge=True)
          OmegaConf.update(config, "data.task", task)
  
      return config
  
imports: import unittest
  import torch
  import omegaconf
  from omegaconf import OmegaConf, DictConfig
  from torch.nn import Sequential, Linear, ReLU, LeakyReLU, BatchNorm1d, Dropout
  import os
  import sys
  from torch_geometric.data import Data
  from torch_points3d.models.base_model import BaseModel
  from test.mockdatasets import MockDatasetGeometric
  from torch_points3d.models.model_factory import instantiate_model
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