class_docstr: Helper class for running an MLflow project as a Databricks Job.
  :param databricks_profile: Optional Databricks CLI profile to use to fetch hostname &
         authentication information when making Databricks API requests.
class_docstr_tok: ["Helper","class","for","running","an","MLflow","project","as","a","Databricks","Job",".",":","param","databricks_profile",":","Optional","Databricks","CLI","profile","to","use","to","fetch","hostname","&","authentication","information","when","making","Databricks","API","requests","."]
code: def run_databricks(
      self,
      uri,
      entry_point,
      work_dir,
      parameters,
      experiment_id,
      cluster_spec,
      run_id,
      env_manager,
  ):
      tracking_uri = _get_tracking_uri_for_run()
      dbfs_fuse_uri = self._upload_project_to_dbfs(
          work_dir, experiment_id
      )
      env_vars = {
          MLFLOW_TRACKING_URI.name: tracking_uri,
          MLFLOW_EXPERIMENT_ID.name: experiment_id,
      }
      _logger.info(
          "=== Running entry point %s of project %s on Databricks ===",
          entry_point,
          uri,
      )
      # Launch run on Databricks
      command = _get_databricks_run_cmd(
          dbfs_fuse_uri, run_id, entry_point, parameters, env_manager
      )
      return self._run_shell_command_job(
          uri, command, env_vars, cluster_spec
      )
  
imports: import hashlib
  import json
  import logging
  import os
  import posixpath
  import re
  import tempfile
  import textwrap
  import time
  from shlex import quote
  from mlflow import tracking
  from mlflow.entities import RunStatus
  from mlflow.environment_variables import MLFLOW_EXPERIMENT_ID, MLFLOW_TRACKING_URI
  from mlflow.exceptions import ExecutionException, MlflowException
  from mlflow.projects.submitted_run import SubmittedRun
  from mlflow.projects.utils import MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
  from mlflow.utils import databricks_utils, file_utils, rest_utils
  from mlflow.utils.mlflow_tags import MLFLOW_DATABRICKS_RUN_URL, MLFLOW_DATABRICKS_SHELL_JOB_ID, MLFLOW_DATABRICKS_SHELL_JOB_RUN_ID, MLFLOW_DATABRICKS_WEBAPP_URL
  from mlflow.utils.uri import is_databricks_uri, is_http_uri
  from mlflow.version import VERSION, is_release_version
label_desc: Use image node_id 8 for calling the DatabricksJobRunner obj's underlying member method code with example usage: obj.run_databricks(uri, entry_point, work_dir, parameters, experiment_id, cluster_spec, run_id, env_manager) and returns: self