code: def test_valid_shap_config(baseline):
      num_samples = 100
      agg_method = "mean_sq"
      use_logit = True
      seed = 123
      granularity = "sentence"
      language = "german"
      model_type = "IMAGE_CLASSIFICATION"
      num_segments = 2
      feature_extraction_method = "segmentation"
      segment_compactness = 10
      max_objects = 4
      iou_threshold = 0.5
      context = 1.0
      text_config = TextConfig(
          granularity=granularity,
          language=language,
      )
      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=baseline,
          num_samples=num_samples,
          agg_method=agg_method,
          use_logit=use_logit,
          seed=seed,
          text_config=text_config,
          image_config=image_config,
      )
      expected_config = {
          "shap": {
              "baseline": baseline,
              "num_samples": num_samples,
              "agg_method": agg_method,
              "use_logit": use_logit,
              "save_local_shap_values": True,
              "seed": seed,
              "text_config": {
                  "granularity": granularity,
                  "language": language,
              },
              "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,
              },
          }
      }
      assert expected_config == shap_config.get_explainability_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 17 for calling a global function with example usage: test_valid_shap_config(baseline) without return types