code: def test_adding_libraries_to_model_run_id_passed(sklearn_knn_model):
      model_name = f"wheels-test-{random_int()}"
      artifact_path = "model"
      model_uri = f"models:/{model_name}/1"
      wheeled_model_uri = f"models:/{model_name}/2"
  
      # Log a model
      with mlflow.start_run():
          original_run_id = (
              mlflow.tracking.fluent._get_or_start_run().info.run_id
          )
          mlflow.sklearn.log_model(
              sk_model=sklearn_knn_model.model,
              artifact_path=artifact_path,
              registered_model_name=model_name,
          )
  
      with mlflow.start_run():
          wheeled_run_id = (
              mlflow.tracking.fluent._get_or_start_run().info.run_id
          )
          pass
  
      wheeled_model_info = add_libraries_to_model(
          model_uri, run_id=wheeled_run_id
      )
      assert original_run_id != wheeled_run_id
      assert wheeled_model_info.run_id == wheeled_run_id
  
      # Verify new model version created
      wheeled_model_version = get_model_version_from_model_uri(
          wheeled_model_uri
      )
      assert wheeled_model_version.run_id == wheeled_run_id
      assert wheeled_model_version.name == model_name
  
imports: import random
  from collections import namedtuple
  from unittest import mock
  import numpy
  import pytest
  import sklearn.neighbors
  from sklearn import datasets
  import mlflow
  from mlflow import MlflowClient
  from mlflow.entities.model_registry import ModelVersion
  from mlflow.exceptions import MlflowException
  from mlflow.models import add_libraries_to_model
  from mlflow.models.utils import _enforce_array, _enforce_datatype, _enforce_object, _enforce_property, get_model_version_from_model_uri
  from mlflow.types import DataType
  from mlflow.types.schema import Array, Object, Property
label_desc: Use image node_id 5 for calling a global function with example usage: test_adding_libraries_to_model_run_id_passed(sklearn_knn_model) without return types