code: def test_auto_ml_additional_optional_params(sagemaker_session):
      auto_ml = AutoML(
          role=ROLE,
          target_attribute_name=TARGET_ATTRIBUTE_NAME,
          sagemaker_session=sagemaker_session,
          volume_kms_key=VOLUME_KMS_KEY,
          vpc_config=VPC_CONFIG,
          encrypt_inter_container_traffic=ENCRYPT_INTER_CONTAINER_TRAFFIC,
          compression_type=COMPRESSION_TYPE,
          output_kms_key=OUTPUT_KMS_KEY,
          output_path=OUTPUT_PATH,
          problem_type=PROBLEM_TYPE,
          max_candidates=MAX_CANDIDATES,
          max_runtime_per_training_job_in_seconds=MAX_RUNTIME_PER_TRAINING_JOB,
          total_job_runtime_in_seconds=TOTAL_JOB_RUNTIME,
          job_objective=JOB_OBJECTIVE,
          generate_candidate_definitions_only=GENERATE_CANDIDATE_DEFINITIONS_ONLY,
          tags=TAGS,
          content_type=CONTENT_TYPE,
          s3_data_type=S3_DATA_TYPE,
          feature_specification_s3_uri=FEATURE_SPECIFICATION_S3_URI,
          validation_fraction=VALIDATION_FRACTION,
          mode=MODE,
          auto_generate_endpoint_name=AUTO_GENERATE_ENDPOINT_NAME,
          endpoint_name=ENDPOINT_NAME,
      )
      inputs = DEFAULT_S3_INPUT_DATA
      auto_ml.fit(inputs, job_name=JOB_NAME)
      sagemaker_session.auto_ml.assert_called_once()
      _, args = sagemaker_session.auto_ml.call_args
  
      assert args == {
          "input_config": [
              {
                  "ContentType": CONTENT_TYPE,
                  "CompressionType": COMPRESSION_TYPE,
                  "DataSource": {
                      "S3DataSource": {
                          "S3DataType": S3_DATA_TYPE,
                          "S3Uri": DEFAULT_S3_INPUT_DATA,
                      }
                  },
                  "TargetAttributeName": TARGET_ATTRIBUTE_NAME,
              }
          ],
          "output_config": {
              "S3OutputPath": OUTPUT_PATH,
              "KmsKeyId": OUTPUT_KMS_KEY,
          },
          "auto_ml_job_config": {
              "CandidateGenerationConfig": {
                  "FeatureSpecificationS3Uri": FEATURE_SPECIFICATION_S3_URI
              },
              "DataSplitConfig": {
                  "ValidationFraction": VALIDATION_FRACTION
              },
              "Mode": MODE,
              "CompletionCriteria": {
                  "MaxAutoMLJobRuntimeInSeconds": TOTAL_JOB_RUNTIME,
                  "MaxCandidates": MAX_CANDIDATES,
                  "MaxRuntimePerTrainingJobInSeconds": MAX_RUNTIME_PER_TRAINING_JOB,
              },
              "SecurityConfig": {
                  "VolumeKmsKeyId": VOLUME_KMS_KEY,
                  "VpcConfig": VPC_CONFIG,
                  "EnableInterContainerTrafficEncryption": ENCRYPT_INTER_CONTAINER_TRAFFIC,
              },
          },
          "job_name": JOB_NAME,
          "role": ROLE,
          "job_objective": JOB_OBJECTIVE,
          "problem_type": PROBLEM_TYPE,
          "generate_candidate_definitions_only": GENERATE_CANDIDATE_DEFINITIONS_ONLY,
          "tags": TAGS,
          "model_deploy_config": {
              "AutoGenerateEndpointName": AUTO_GENERATE_ENDPOINT_NAME,
              "EndpointName": ENDPOINT_NAME,
          },
      }
  
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 11 for calling a global function with example usage: test_auto_ml_additional_optional_params(sagemaker_session) without return types