code: def test_auto_ml_default_fit(strftime, sagemaker_session):
      auto_ml = AutoML(
          role=ROLE,
          target_attribute_name=TARGET_ATTRIBUTE_NAME,
          sagemaker_session=sagemaker_session,
      )
      inputs = DEFAULT_S3_INPUT_DATA
      auto_ml.fit(inputs)
      sagemaker_session.auto_ml.assert_called_once()
      _, args = sagemaker_session.auto_ml.call_args
      assert args == {
          "input_config": [
              {
                  "DataSource": {
                      "S3DataSource": {
                          "S3DataType": "S3Prefix",
                          "S3Uri": DEFAULT_S3_INPUT_DATA,
                      }
                  },
                  "TargetAttributeName": TARGET_ATTRIBUTE_NAME,
              }
          ],
          "output_config": {"S3OutputPath": DEFAULT_OUTPUT_PATH},
          "auto_ml_job_config": {
              "CompletionCriteria": {},
              "SecurityConfig": {
                  "EnableInterContainerTrafficEncryption": ENCRYPT_INTER_CONTAINER_TRAFFIC
              },
          },
          "role": ROLE,
          "job_name": DEFAULT_JOB_NAME,
          "problem_type": None,
          "job_objective": None,
          "generate_candidate_definitions_only": GENERATE_CANDIDATE_DEFINITIONS_ONLY,
          "tags": None,
      }
  
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 12 for calling a global function with example usage: test_auto_ml_default_fit(strftime, sagemaker_session) without return types