code: def scheduled_model_quality_monitor(
      sagemaker_session,
      model_quality_monitor,
      endpoint_name,
      ground_truth_input,
  ):
      monitor_schedule_name = utils.unique_name_from_base(
          "model-quality-monitor"
      )
      s3_uri_monitoring_output = os.path.join(
          "s3://",
          sagemaker_session.default_bucket(),
          endpoint_name,
          monitor_schedule_name,
          "monitor_output",
      )
      # To include attributes
      endpoint_input = EndpointInput(
          endpoint_name=endpoint_name,
          destination=ENDPOINT_INPUT_LOCAL_PATH,
          start_time_offset=START_TIME_OFFSET,
          end_time_offset=END_TIME_OFFSET,
          inference_attribute=INFERENCE_ATTRIBUTE,
      )
      model_quality_monitor.create_monitoring_schedule(
          monitor_schedule_name=monitor_schedule_name,
          endpoint_input=endpoint_input,
          ground_truth_input=ground_truth_input,
          problem_type=PROBLEM_TYPE,
          output_s3_uri=s3_uri_monitoring_output,
          schedule_cron_expression=CRON,
      )
      return model_quality_monitor
  
imports: from __future__ import print_function, absolute_import
  import tempfile
  from datetime import datetime, timedelta
  import json
  import os
  import numpy
  import pytest
  import pandas
  import tests.integ
  import tests.integ.timeout
  from sagemaker import image_uris, utils
  from sagemaker.model_monitor import CronExpressionGenerator, DataCaptureConfig, EndpointInput, ModelQualityMonitor, DatasetFormat
  from sagemaker.s3 import S3Uploader
  from sagemaker.utils import unique_name_from_base
  from sagemaker.model import Model
  from tests.integ import DATA_DIR
  from tests.integ.retry import retries
label_desc: Use image node_id 6 for calling a global function with example usage: scheduled_model_quality_monitor(sagemaker_session, model_quality_monitor, endpoint_name, ground_truth_input) and returns: model_quality_monitor