code: def scheduled_explainability_monitor(
      explainability_monitor,
      sagemaker_session,
      model_config,
      endpoint_name,
      shap_config,
  ):
      monitor_schedule_name = utils.unique_name_from_base(
          "explainability-monitor"
      )
      analysis_config = ExplainabilityAnalysisConfig(
          shap_config,
          model_config,
          headers=HEADERS_OF_FEATURES,
          label_headers=[HEADER_OF_PREDICTION],
      )
      s3_uri_monitoring_output = os.path.join(
          "s3://",
          sagemaker_session.default_bucket(),
          endpoint_name,
          monitor_schedule_name,
          "monitor_output",
      )
      explainability_monitor.create_monitoring_schedule(
          monitor_schedule_name=monitor_schedule_name,
          analysis_config=analysis_config,
          endpoint_input=endpoint_name,
          output_s3_uri=s3_uri_monitoring_output,
          schedule_cron_expression=CRON,
      )
      return explainability_monitor
  
imports: from __future__ import print_function, absolute_import
  from datetime import datetime, timedelta
  import json
  import numpy
  import os
  import pandas
  import pytest
  import tempfile
  import uuid
  import time
  import tests.integ
  import tests.integ.timeout
  from sagemaker import image_uris, utils
  from sagemaker.model_monitor import BiasAnalysisConfig, CronExpressionGenerator, DataCaptureConfig, EndpointInput, ExplainabilityAnalysisConfig, ModelBiasMonitor, ModelExplainabilityMonitor, BatchTransformInput, MonitoringDatasetFormat
  from sagemaker.s3 import S3Uploader
  from sagemaker.utils import unique_name_from_base
  from sagemaker.clarify import BiasConfig, DataConfig, ModelConfig, ModelPredictedLabelConfig, SHAPConfig
  from sagemaker.model import Model
  from tests.integ import DATA_DIR
  from tests.integ.retry import retries
label_desc: Use image node_id 16 for calling a global function with example usage: scheduled_explainability_monitor(explainability_monitor, sagemaker_session, model_config, endpoint_name, shap_config) and returns: explainability_monitor