code: def test_shap_config_features_to_explain():
      baseline = [1, 2, 3]
      num_samples = 100
      agg_method = "mean_sq"
      use_logit = True
      save_local_shap_values = True
      seed = 123
      features_to_explain = [0, 1]
      shap_config = SHAPConfig(
          baseline=baseline,
          num_samples=num_samples,
          agg_method=agg_method,
          use_logit=use_logit,
          save_local_shap_values=save_local_shap_values,
          seed=seed,
          features_to_explain=features_to_explain,
      )
      expected_config = {
          "shap": {
              "baseline": baseline,
              "num_samples": num_samples,
              "agg_method": agg_method,
              "use_logit": use_logit,
              "save_local_shap_values": save_local_shap_values,
              "seed": seed,
              "features_to_explain": features_to_explain,
          }
      }
      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 18 for calling a global function with example usage: test_shap_config_features_to_explain() without return types