code: def init_spark_standalone(
      num_executors,
      executor_cores,
      executor_memory="2g",
      driver_cores=4,
      driver_memory="2g",
      master=None,
      extra_executor_memory_for_ray=None,
      extra_python_lib=None,
      spark_log_level="WARN",
      redirect_spark_log=True,
      conf=None,
      jars=None,
      python_location=None,
      enable_numa_binding=False,
  ):
      """
      Create a SparkContext with BigDL configurations on Spark standalone cluster.
  
      You need to specify master if you already have a Spark standalone cluster. For a
      standalone cluster with multiple nodes, make sure that BigDL is installed via
      pip in the Python environment on every node.
      If master is not specified, a new Spark standalone cluster on the current single node
      would be started first and the SparkContext would use its master address. You need to
      call `stop_spark_standalone` after your program finishes to shutdown the cluster.
  
      :param num_executors: The number of Spark executors.
      :param executor_cores: The number of cores for each executor.
      :param executor_memory: The memory for each executor. Default to be '2g'.
      :param driver_cores: The number of cores for the Spark driver. Default to be 4.
      :param driver_memory: The memory for the Spark driver. Default to be '1g'.
      :param master: The master URL of an existing Spark standalone cluster: 'spark://master:port'.
      You only need to specify this if you have already started a standalone cluster.
      Default to be None and a new standalone cluster would be started in this case.
      :param extra_executor_memory_for_ray: The extra memory for Ray services. Default to be None.
      :param extra_python_lib: Extra python files or packages needed for distribution.
             Default to be None.
      :param spark_log_level: The log level for Spark. Default to be 'WARN'.
      :param redirect_spark_log: Whether to redirect the Spark log to local file. Default to be True.
      :param jars: Comma-separated list of jars to be included on driver and executor's classpath.
             Default to be None.
      :param conf: You can append extra conf for Spark in key-value format.
             i.e conf={"spark.executor.extraJavaOptions": "-XX:+PrintGCDetails"}.
             Default to be None.
      :param python_location: The path to your running Python executable. If not specified, the
             default Python interpreter in effect would be used.
      :param enable_numa_binding: Whether to use numactl to start spark worker in order to bind
             different worker processes to different cpus and memory areas. This is may lead to
             better performance on a multi-sockets machine. Defaults to False.
  
      :return: An instance of SparkContext.
      """
      from bigdl.dllib.utils.spark import SparkRunner
  
      runner = SparkRunner(
          spark_log_level=spark_log_level,
          redirect_spark_log=redirect_spark_log,
      )
      set_python_home()
      sc = runner.init_spark_standalone(
          num_executors=num_executors,
          executor_cores=executor_cores,
          executor_memory=executor_memory,
          driver_cores=driver_cores,
          driver_memory=driver_memory,
          master=master,
          extra_executor_memory_for_ray=extra_executor_memory_for_ray,
          extra_python_lib=extra_python_lib,
          conf=conf,
          jars=jars,
          python_location=python_location,
          enable_numa_binding=enable_numa_binding,
      )
      return sc
  
imports: from bigdl.dllib.utils.common import *
  from bigdl.dllib.utils.file_utils import callZooFunc
  from bigdl.dllib.utils.utils import set_python_home
  from bigdl.dllib.utils.log4Error import *
  import warnings
  import multiprocessing
  import os
  import threading
  import sys
label_desc: Use image node_id 4 for calling a global function with example usage: init_spark_standalone(num_executors, executor_cores, executor_memory, driver_cores, driver_memory, master, extra_executor_memory_for_ray, extra_python_lib, spark_log_level, redirect_spark_log, conf, jars, python_location, enable_numa_binding) and returns: sc