code: def _get_cluster_info(self, sc):
      def get_worker_address(iter):
          worker_ip = get_node_ip()
          worker_port = find_free_port()
          addresses = find_ip_and_free_port(iter)
          res = [
              (f"{worker_ip}:{worker_port}", address)
              for address in addresses
          ]
          return res
  
      worker_info = (
          self.workerRDD.barrier()
          .mapPartitions(get_worker_address)
          .collect()
      )
      address_info = [info[0] for info in worker_info]
      cluster_info = [info[1] for info in worker_info]
  
      return address_info, cluster_info
  
imports: import os
  import types
  import logging
  import tempfile
  import shutil
  from bigdl.dllib.utils.common import get_node_and_core_number
  from bigdl.dllib.utils.file_utils import is_local_path
  from bigdl.dllib.utils.utils import get_node_ip
  from bigdl.orca.data.file import is_file, exists, get_remote_file_to_local, get_remote_files_with_prefix_to_local, put_local_file_to_remote, put_local_files_with_prefix_to_remote
  from bigdl.orca.learn.tf2.spark_runner import SparkRunner
  from bigdl.orca.learn.utils import find_free_port, find_ip_and_free_port
  from bigdl.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, make_data_creator, load_model, save_model, process_xshards_of_pandas_dataframe, add_predict_to_pd_xshards, update_predict_xshards
  from bigdl.orca.learn.log_monitor import start_log_server, stop_log_server
  from bigdl.orca import OrcaContext
  from bigdl.orca.data.shard import SparkXShards
  from bigdl.dllib.utils.log4Error import invalidInputError
  from pyspark.sql.dataframe import DataFrame
  from typing import TYPE_CHECKING, Any, Dict, List, Callable, Union, Optional
label_desc: Use image node_id 2 for calling the SparkTFEstimator obj's underlying member method code with example usage: obj._get_cluster_info(sc) and returns: address_info,  cluster_info, res