code: def test_run_with_artifact_path(tmp_path):
      artifact_file = tmp_path.joinpath("model.pkl")
      artifact_file.write_text("Hello world")
      with mlflow.start_run() as run:
          mlflow.log_artifact(artifact_file)
          submitted_run = mlflow.projects.run(
              TEST_PROJECT_DIR,
              entry_point="test_artifact_path",
              parameters={
                  "model": f"runs:/{run.info.run_id}/model.pkl"
              },
              env_manager="local",
              experiment_id=FileStore.DEFAULT_EXPERIMENT_ID,
          )
          validate_exit_status(
              submitted_run.get_status(), RunStatus.FINISHED
          )
  
imports: import json
  import os
  import shutil
  import subprocess
  import uuid
  from unittest import mock
  import git
  import pytest
  import yaml
  from databricks_cli.configure.provider import DatabricksConfig
  import mlflow
  from mlflow import MlflowClient
  from mlflow.entities import RunStatus, SourceType, ViewType
  from mlflow.environment_variables import MLFLOW_CONDA_CREATE_ENV_CMD, MLFLOW_CONDA_HOME
  from mlflow.exceptions import ExecutionException, MlflowException
  from mlflow.projects import _parse_kubernetes_config, _resolve_experiment_id
  from mlflow.store.tracking.file_store import FileStore
  from mlflow.utils import PYTHON_VERSION
  from mlflow.utils.conda import CONDA_EXE, get_or_create_conda_env
  from mlflow.utils.mlflow_tags import LEGACY_MLFLOW_GIT_BRANCH_NAME, LEGACY_MLFLOW_GIT_REPO_URL, MLFLOW_GIT_BRANCH, MLFLOW_GIT_REPO_URL, MLFLOW_PARENT_RUN_ID, MLFLOW_PROJECT_BACKEND, MLFLOW_PROJECT_ENTRY_POINT, MLFLOW_PROJECT_ENV, MLFLOW_SOURCE_NAME, MLFLOW_SOURCE_TYPE, MLFLOW_USER
  from mlflow.utils.process import ShellCommandException
  from tests.projects.utils import TEST_PROJECT_DIR, TEST_PROJECT_NAME, validate_exit_status
label_desc: Use image node_id 12 for calling a global function with example usage: test_run_with_artifact_path(tmp_path) without return types