code: def test_auto_ml_fit_optional_args(sagemaker_session):
      output_path = "s3://{}/{}".format(
          sagemaker_session.default_bucket(), "specified_ouput_path"
      )
      problem_type = "MulticlassClassification"
      job_objective = {"MetricName": "Accuracy"}
      auto_ml = AutoML(
          role=ROLE,
          target_attribute_name=TARGET_ATTRIBUTE_NAME,
          sagemaker_session=sagemaker_session,
          max_candidates=1,
          output_path=output_path,
          problem_type=problem_type,
          job_objective=job_objective,
          generate_candidate_definitions_only=True,
      )
      inputs = TRAINING_DATA
      with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
          auto_ml.fit(
              inputs, job_name=unique_name_from_base(BASE_JOB_NAME)
          )
  
      auto_ml_desc = auto_ml.describe_auto_ml_job(
          job_name=auto_ml.latest_auto_ml_job.job_name
      )
      assert auto_ml_desc["AutoMLJobStatus"] == "Completed"
      assert (
          auto_ml_desc["AutoMLJobName"]
          == auto_ml.latest_auto_ml_job.job_name
      )
      assert auto_ml_desc["AutoMLJobObjective"] == job_objective
      assert auto_ml_desc["ProblemType"] == problem_type
      assert (
          auto_ml_desc["OutputDataConfig"]["S3OutputPath"]
          == output_path
      )
  
imports: from __future__ import absolute_import
  import os
  import pytest
  from botocore.exceptions import ClientError
  import tests.integ
  from sagemaker import AutoML, AutoMLInput, CandidateEstimator
  from sagemaker.utils import unique_name_from_base
  from tests.integ import AUTO_ML_DEFAULT_TIMEMOUT_MINUTES, DATA_DIR, auto_ml_utils
  from tests.integ.timeout import timeout
  from tests.conftest import CUSTOM_S3_OBJECT_KEY_PREFIX
label_desc: Use image node_id 6 for calling a global function with example usage: test_auto_ml_fit_optional_args(sagemaker_session) without return types