code: def test_validate_and_update_inference_response():
      cic = copy.copy(CLASSIFICATION_INFERENCE_CONTAINERS)
  
      AutoML.validate_and_update_inference_response(
          inference_containers=cic,
          inference_response_keys=[
              "predicted_label",
              "labels",
              "probabilities",
              "probability",
          ],
      )
  
      assert (
          cic[2]["Environment"]["SAGEMAKER_INFERENCE_OUTPUT"]
          == "predicted_label,labels,probabilities,probability"
      )
      assert (
          cic[2]["Environment"]["SAGEMAKER_INFERENCE_INPUT"]
          == "predicted_label,probabilities,probability"
      )
      assert (
          cic[1]["Environment"]["SAGEMAKER_INFERENCE_OUTPUT"]
          == "predicted_label,probabilities,probability"
      )
  
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 29 for calling a global function with example usage: test_validate_and_update_inference_response() without return types