code: def test_shap_with_image_config(
      name_from_base,
      clarify_processor,
      clarify_processor_with_job_name_prefix,
      data_config,
      model_config,
  ):
      model_type = "IMAGE_CLASSIFICATION"
      num_segments = 2
      feature_extraction_method = "segmentation"
      segment_compactness = 10
      max_objects = 4
      iou_threshold = 0.5
      context = 1.0
      image_config = ImageConfig(
          model_type=model_type,
          num_segments=num_segments,
          feature_extraction_method=feature_extraction_method,
          segment_compactness=segment_compactness,
          max_objects=max_objects,
          iou_threshold=iou_threshold,
          context=context,
      )
  
      shap_config = SHAPConfig(
          baseline=[
              [
                  0.26124998927116394,
                  0.2824999988079071,
                  0.06875000149011612,
              ]
          ],
          num_samples=100,
          agg_method="mean_sq",
          image_config=image_config,
      )
  
      expected_image_config = {
          "model_type": model_type,
          "num_segments": num_segments,
          "feature_extraction_method": feature_extraction_method,
          "segment_compactness": segment_compactness,
          "max_objects": max_objects,
          "iou_threshold": iou_threshold,
          "context": context,
      }
      expected_predictor_config = {
          "model_name": "xgboost-model",
          "instance_type": "ml.c5.xlarge",
          "initial_instance_count": 1,
      }
  
      _run_test_explain(
          name_from_base,
          clarify_processor,
          clarify_processor_with_job_name_prefix,
          data_config,
          model_config,
          shap_config,
          None,
          None,
          expected_predictor_config,
          expected_image_config=expected_image_config,
      )
  
imports: from __future__ import absolute_import, print_function
  import copy
  import pytest
  from mock import MagicMock, Mock, patch
  from sagemaker import Processor, image_uris
  from sagemaker.clarify import BiasConfig, DataConfig, ModelConfig, ModelPredictedLabelConfig, PDPConfig, SageMakerClarifyProcessor, SHAPConfig, TextConfig, ImageConfig, _AnalysisConfigGenerator, DatasetType, ProcessingOutputHandler, SegmentationConfig
label_desc: Use image node_id 47 for calling a global function with example usage: test_shap_with_image_config(name_from_base, clarify_processor, clarify_processor_with_job_name_prefix, data_config, model_config) without return types