code: def _get_repl_id():
      """
      Get a unique REPL ID for a PythonSubscriber instance. This is used to distinguish between
      REPLs in multitenant, REPL-aware environments where multiple Python processes may share the
      same Spark JVM (e.g. in Databricks). In such environments, we pull the REPL ID from Spark
      local properties, and expect that the PythonSubscriber for the current Python process only
      receives events for datasource reads triggered by the current process.
      """
      repl_id = get_databricks_repl_id()
      if repl_id:
          return repl_id
      main_file = sys.argv[0] if len(sys.argv) > 0 else "<console>"
      return f"PythonSubscriber[{main_file}][{uuid.uuid4().hex}]"
  
imports: import concurrent.futures
  import logging
  import sys
  import threading
  import uuid
  from py4j.java_gateway import CallbackServerParameters
  import mlflow
  from mlflow import MlflowClient
  from mlflow.exceptions import MlflowException
  from mlflow.spark import FLAVOR_NAME
  from mlflow.tracking.context.abstract_context import RunContextProvider
  from mlflow.utils import _truncate_and_ellipsize
  from mlflow.utils.autologging_utils import ExceptionSafeClass, autologging_is_disabled
  from mlflow.utils.databricks_utils import get_repl_id
  from mlflow.utils.validation import MAX_TAG_VAL_LENGTH
label_desc: Use image node_id 13 for calling a global function with example usage: _get_repl_id() and returns: str, main_file, str, uuid, str, repl_id