code: def describe(
      pipeline_name: str, sagemaker_session: Optional[Session] = None
  ) -> Dict[str, Union[int, str]]:
      """Describe feature processor and other related resources.
  
      This API will include details related to the feature processor including SageMaker Pipeline and
      EventBridge Schedule.
  
      Args:
          pipeline_name (str): Name of the pipeline.
          sagemaker_session (Optional[Session]): Session object which manages interactions
              with Amazon SageMaker APIs and any other AWS services needed. If not specified, the
              function creates one using the default AWS configuration chain.
      Returns:
          Dict[str, Union[int, str]]: Return information for resources related to feature processor.
      """
  
      _sagemaker_session = sagemaker_session or Session()
      describe_response_dict = {}
  
      try:
          describe_pipeline_response = (
              _sagemaker_session.sagemaker_client.describe_pipeline(
                  PipelineName=pipeline_name
              )
          )
          pipeline_definition = json.loads(
              describe_pipeline_response["PipelineDefinition"]
          )
          pipeline_step = pipeline_definition["Steps"][0]
          describe_response_dict = dict(
              pipeline_arn=describe_pipeline_response["PipelineArn"],
              pipeline_execution_role_arn=describe_pipeline_response[
                  "RoleArn"
              ],
          )
  
          if "RetryPolicies" in pipeline_step:
              describe_response_dict["max_retries"] = pipeline_step[
                  "RetryPolicies"
              ][0]["MaxAttempts"]
      except ClientError as e:
          if (
              RESOURCE_NOT_FOUND_EXCEPTION
              == e.response["Error"]["Code"]
          ):
              logger.info("Pipeline %s does not exist.", pipeline_name)
  
      event_bridge_scheduler_helper = EventBridgeSchedulerHelper(
          _sagemaker_session,
          _sagemaker_session.boto_session.client("scheduler"),
      )
  
      event_bridge_schedule = (
          event_bridge_scheduler_helper.describe_schedule(pipeline_name)
      )
      if event_bridge_schedule:
          describe_response_dict.update(
              dict(
                  schedule_arn=event_bridge_schedule["Arn"],
                  schedule_expression=event_bridge_schedule[
                      "ScheduleExpression"
                  ],
                  schedule_state=event_bridge_schedule["State"],
                  schedule_start_date=event_bridge_schedule[
                      "StartDate"
                  ].strftime(EXECUTION_TIME_PIPELINE_PARAMETER_FORMAT),
                  schedule_role=event_bridge_schedule["Target"][
                      "RoleArn"
                  ],
              )
          )
  
      event_bridge_rule_helper = EventBridgeRuleHelper(
          _sagemaker_session,
          _sagemaker_session.boto_session.client("events"),
      )
      event_based_trigger = event_bridge_rule_helper.describe_rule(
          pipeline_name
      )
      if event_based_trigger:
          describe_response_dict.update(
              dict(
                  trigger=event_based_trigger["Arn"],
                  event_pattern=event_based_trigger["EventPattern"],
                  trigger_state=event_based_trigger["State"],
              )
          )
  
      return describe_response_dict
  
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 9 for calling a global function with example usage: describe(pipeline_name, sagemaker_session) and returns: describe_response_dict