code: def test_auto_ml_input_object_list_fit(sagemaker_session):
      auto_ml = AutoML(
          role=ROLE,
          target_attribute_name=TARGET_ATTRIBUTE_NAME,
          sagemaker_session=sagemaker_session,
          max_candidates=1,
          mode=MODE,
      )
      job_name = unique_name_from_base(BASE_JOB_NAME, max_length=32)
      s3_input_training = sagemaker_session.upload_data(
          path=TRAINING_DATA, key_prefix=PREFIX + "/input"
      )
      s3_input_validation = sagemaker_session.upload_data(
          path=VALIDATION_DATA, key_prefix=PREFIX + "/input"
      )
      input_training = AutoMLInput(
          inputs=s3_input_training,
          target_attribute_name=TARGET_ATTRIBUTE_NAME,
          channel_type="training",
      )
      input_validation = AutoMLInput(
          inputs=s3_input_validation,
          target_attribute_name=TARGET_ATTRIBUTE_NAME,
          channel_type="validation",
      )
      inputs = [input_training, input_validation]
      with timeout(minutes=AUTO_ML_DEFAULT_TIMEMOUT_MINUTES):
          auto_ml.fit(inputs, job_name=job_name)
  
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 5 for calling a global function with example usage: test_auto_ml_input_object_list_fit(sagemaker_session) without return types