code: def list_pipelines(
      sagemaker_session: Optional[Session] = None,
  ) -> List[Dict[str, Any]]:
      """Lists all SageMaker Pipelines created by Feature Processor SDK.
  
      Args:
          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:
          List[Dict[str, Any]]: Return list of SageMaker Pipeline metadata created for
              feature_processor.
      """
  
      _sagemaker_session = sagemaker_session or Session()
      next_token = None
      list_response = []
      pipeline_names_so_far = set([])
      while True:
          list_contexts_request = dict(
              ContextType=PIPELINE_CONTEXT_TYPE
          )
          if next_token:
              list_contexts_request["NextToken"] = next_token
          list_contexts_response = (
              _sagemaker_session.sagemaker_client.list_contexts(
                  **list_contexts_request
              )
          )
          for _context in list_contexts_response["ContextSummaries"]:
              pipeline_name = get_resource_name_from_arn(
                  _context["Source"]["SourceUri"]
              )
              if pipeline_name not in pipeline_names_so_far:
                  list_response.append(
                      dict(pipeline_name=pipeline_name)
                  )
                  pipeline_names_so_far.add(pipeline_name)
          next_token = list_contexts_response.get("NextToken")
          if not next_token:
              break
  
      return list_response
  
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 10 for calling a global function with example usage: list_pipelines(sagemaker_session) and returns: list_response