code: def test_attach(sagemaker_session):
      aml = AutoML.attach(
          auto_ml_job_name=JOB_NAME_3,
          sagemaker_session=sagemaker_session,
      )
      assert aml.current_job_name == JOB_NAME_3
      assert aml.role == "mock_role_arn"
      assert aml.target_attribute_name == "y"
      assert aml.problem_type == "Auto"
      assert aml.output_path == "s3://output_prefix"
      assert aml.tags == LIST_TAGS_RESULT["Tags"]
      assert aml.content_type == "x-application/vnd.amazon+parquet"
      assert aml.s3_data_type == "ManifestFile"
      assert (
          aml.feature_specification_s3_uri
          == "s3://{}/features.json".format(BUCKET_NAME)
      )
      assert aml.validation_fraction == 0.2
      assert aml.mode == "ENSEMBLING"
      assert aml.auto_generate_endpoint_name is False
      assert aml.endpoint_name == "EndpointName"
  
imports: from __future__ import absolute_import
  import copy
  import pytest
  from mock import Mock, patch
  from sagemaker import AutoML, AutoMLJob, AutoMLInput, CandidateEstimator, PipelineModel
  from sagemaker.predictor import Predictor
  from sagemaker.session_settings import SessionSettings
  from sagemaker.workflow.functions import Join
  from tests.unit import SAGEMAKER_CONFIG_AUTO_ML, SAGEMAKER_CONFIG_TRAINING_JOB, _test_default_bucket_and_prefix_combinations, DEFAULT_S3_BUCKET_NAME, DEFAULT_S3_OBJECT_KEY_PREFIX_NAME
label_desc: Use image node_id 32 for calling a global function with example usage: test_attach(sagemaker_session) without return types