code: def get_config():
      """Get the default hyperparameter configuration."""
      config = ml_collections.ConfigDict()
  
      # Optimizer.
      config.optimizer = "adam"
      config.learning_rate = 1e-3
  
      # Training hyperparameters.
      config.batch_size = 256
      config.num_train_steps = 100_000
      config.log_every_steps = 100
      config.eval_every_steps = 1_000
      config.checkpoint_every_steps = 10_000
      config.add_virtual_node = False
      config.add_undirected_edges = True
      config.add_self_loops = True
  
      # GNN hyperparameters.
      config.model = "GraphConvNet"
      config.message_passing_steps = 5
      config.latent_size = 256
      config.dropout_rate = 0.1
      config.num_mlp_layers = 2
      config.num_classes = 128
      config.skip_connections = True
      config.layer_norm = True
      return config
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_config() and returns: config