chore: import upstream snapshot with attribution

examples/shap/explainer_logging.py

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+import shap
+import sklearn
+from sklearn.datasets import load_diabetes
+
+import mlflow
+
+# prepare training data
+X, y = load_diabetes(return_X_y=True, as_frame=True)
+
+# train a model
+model = sklearn.ensemble.RandomForestRegressor(n_estimators=100)
+model.fit(X, y)
+
+# create an explainer
+explainer_original = shap.Explainer(model.predict, X, algorithm="permutation")
+
+# log an explainer
+with mlflow.start_run() as run:
+    mlflow.shap.log_explainer(explainer_original, artifact_path="shap_explainer")
+
+    # load back the explainer
+    explainer_new = mlflow.shap.load_explainer(f"runs:/{run.info.run_id}/shap_explainer")
+
+    # run explainer on data
+    shap_values = explainer_new(X[:5])
+
+    print(shap_values)