code: def _get_container_entry_point_and_arguments(
      remote_decorator_config: _JobSettings,
      s3_base_uri: str,
      client_python_version: str,
      spark_dependency_paths: Dict[str, Optional[str]],
  ) -> Dict[str, List[str]]:
      """Extracts the container entry point and container arguments from remote decorator configs
  
      Args:
          remote_decorator_config (_JobSettings): Configurations used for setting up
              SageMaker Pipeline Step.
          s3_base_uri (str): S3 URI used as destination for dependencies upload.
          client_python_version (str): Python version used on client side.
          spark_dependency_paths (Dict[str, Optional[str]]): A dictionary contains S3 paths spark
              dependency files get uploaded to if present.
      Returns:
          Dict[str, List[str]]: Request dictionary containing container entry point and
              arguments setup.
      """
  
      spark_config = remote_decorator_config.spark_config
      jobs_container_entrypoint = JOBS_CONTAINER_ENTRYPOINT.copy()
  
      if spark_dependency_paths[SPARK_JAR_FILES_PATH]:
          jobs_container_entrypoint.extend(
              ["--jars", spark_dependency_paths[SPARK_JAR_FILES_PATH]]
          )
  
      if spark_dependency_paths[SPARK_PY_FILES_PATH]:
          jobs_container_entrypoint.extend(
              [
                  "--py-files",
                  spark_dependency_paths[SPARK_PY_FILES_PATH],
              ]
          )
  
      if spark_dependency_paths[SPARK_FILES_PATH]:
          jobs_container_entrypoint.extend(
              ["--files", spark_dependency_paths[SPARK_FILES_PATH]]
          )
  
      if spark_config and spark_config.spark_event_logs_uri:
          jobs_container_entrypoint.extend(
              [
                  "--spark-event-logs-s3-uri",
                  spark_config.spark_event_logs_uri,
              ]
          )
  
      if spark_config:
          jobs_container_entrypoint.extend([SPARK_APP_SCRIPT_PATH])
  
      container_args = ["--s3_base_uri", s3_base_uri]
      container_args.extend(
          [
              "--region",
              remote_decorator_config.sagemaker_session.boto_region_name,
          ]
      )
      container_args.extend(
          ["--client_python_version", client_python_version]
      )
  
      if remote_decorator_config.s3_kms_key:
          container_args.extend(
              ["--s3_kms_key", remote_decorator_config.s3_kms_key]
          )
  
      return dict(
          container_entry_point=jobs_container_entrypoint,
          container_arguments=container_args,
      )
  
imports: from __future__ import absolute_import
  import logging
  import json
  import re
  from datetime import datetime
  from typing import Callable, List, Optional, Dict, Sequence, Union, Any, Tuple
  import pytz
  from botocore.exceptions import ClientError
  from sagemaker.feature_store.feature_processor._config_uploader import ConfigUploader
  from sagemaker.feature_store.feature_processor._enums import FeatureProcessorMode
  from sagemaker.feature_store.feature_processor._event_bridge_rule_helper import EventBridgeRuleHelper
  from sagemaker.feature_store.feature_processor._feature_processor_pipeline_events import FeatureProcessorPipelineEvents
  from sagemaker.feature_store.feature_processor.lineage._feature_processor_lineage_name_helper import _get_feature_group_lineage_context_name, _get_feature_group_pipeline_lineage_context_name, _get_feature_group_pipeline_version_lineage_context_name, _get_feature_processor_pipeline_lineage_context_name, _get_feature_processor_pipeline_version_lineage_context_name
  from sagemaker.lineage import context
  from sagemaker.lineage._utils import get_resource_name_from_arn
  from sagemaker.remote_function.runtime_environment.runtime_environment_manager import RuntimeEnvironmentManager
  from sagemaker.remote_function.spark_config import SparkConfig
  from sagemaker.vpc_utils import SUBNETS_KEY, SECURITY_GROUP_IDS_KEY
  from sagemaker.feature_store.feature_processor._constants import EXECUTION_TIME_PIPELINE_PARAMETER, EXECUTION_TIME_PIPELINE_PARAMETER_FORMAT, RESOURCE_NOT_FOUND_EXCEPTION, SPARK_JAR_FILES_PATH, SPARK_PY_FILES_PATH, SPARK_FILES_PATH, FEATURE_PROCESSOR_TAG_KEY, FEATURE_PROCESSOR_TAG_VALUE, PIPELINE_CONTEXT_TYPE, DEFAULT_SCHEDULE_STATE, SCHEDULED_TIME_PIPELINE_PARAMETER, PIPELINE_CONTEXT_NAME_TAG_KEY, PIPELINE_VERSION_CONTEXT_NAME_TAG_KEY, PIPELINE_NAME_MAXIMUM_LENGTH, RESOURCE_NOT_FOUND, FEATURE_GROUP_ARN_REGEX_PATTERN, TO_PIPELINE_RESERVED_TAG_KEYS, DEFAULT_TRIGGER_STATE, EVENTBRIDGE_RULE_ARN_REGEX_PATTERN
  from sagemaker.feature_store.feature_processor._feature_processor_config import FeatureProcessorConfig
  from sagemaker.s3 import s3_path_join
  from sagemaker import Session, get_execution_role
  from sagemaker.feature_store.feature_processor._event_bridge_scheduler_helper import EventBridgeSchedulerHelper
  from sagemaker.workflow.pipeline import Pipeline
  from sagemaker.workflow.retry import StepRetryPolicy, StepExceptionTypeEnum, SageMakerJobStepRetryPolicy, SageMakerJobExceptionTypeEnum
  from sagemaker.workflow.steps import TrainingStep
  from sagemaker.estimator import Estimator
  from sagemaker.feature_store.feature_processor.lineage._feature_processor_lineage import FeatureProcessorLineageHandler, TransformationCode
  from sagemaker.remote_function.job import _JobSettings, JOBS_CONTAINER_ENTRYPOINT, SPARK_APP_SCRIPT_PATH
  from sagemaker.feature_store.feature_processor._data_source import CSVDataSource, FeatureGroupDataSource, ParquetDataSource
label_desc: Use image node_id 17 for calling a global function with example usage: _get_container_entry_point_and_arguments(remote_decorator_config, s3_base_uri, client_python_version, spark_dependency_paths) and returns: dict