code: def run_databricks_project(cluster_spec, **kwargs):
      return mlflow.projects.run(
          uri=TEST_PROJECT_DIR,
          backend="databricks",
          backend_config=cluster_spec,
          parameters={"alpha": "0.4"},
          **kwargs,
      )
  
imports: import filecmp
  import json
  import os
  import shutil
  from unittest import mock
  import databricks_cli
  import pytest
  from databricks_cli.configure.provider import DatabricksConfig
  import mlflow
  from mlflow import MlflowClient, cli
  from mlflow.entities import RunStatus
  from mlflow.environment_variables import MLFLOW_TRACKING_URI
  from mlflow.exceptions import MlflowException
  from mlflow.projects import ExecutionException, databricks
  from mlflow.projects.databricks import DatabricksJobRunner, _get_cluster_mlflow_run_cmd
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE, ErrorCode
  from mlflow.store.tracking.file_store import FileStore
  from mlflow.tracking.request_header.default_request_header_provider import DefaultRequestHeaderProvider
  from mlflow.utils import file_utils
  from mlflow.utils.mlflow_tags import MLFLOW_DATABRICKS_RUN_URL, MLFLOW_DATABRICKS_SHELL_JOB_RUN_ID, MLFLOW_DATABRICKS_WEBAPP_URL
  from mlflow.utils.uri import construct_db_uri_from_profile
  from tests import helper_functions
  from tests.integration.utils import invoke_cli_runner
  from tests.projects.utils import TEST_PROJECT_DIR, validate_exit_status
label_desc: Use image node_id 14 for calling a global function with example usage: run_databricks_project(cluster_spec) and returns: mlflow