chore: import upstream snapshot with attribution

examples/pyfunc/train.py

@@ -0,0 +1,43 @@
+import os
+from typing import Any
+
+from custom_code import iris_classes
+from sklearn.datasets import load_iris
+from sklearn.linear_model import LogisticRegression
+
+import mlflow
+from mlflow.models import infer_signature
+
+
+class CustomPredict(mlflow.pyfunc.PythonModel):
+    """Custom pyfunc class used to create customized mlflow models"""
+
+    def load_context(self, context):
+        self.model = mlflow.sklearn.load_model(context.artifacts["custom_model"])
+
+    def predict(self, context, model_input, params: dict[str, Any] | None = None):
+        prediction = self.model.predict(model_input)
+        return iris_classes(prediction)
+
+
+X, y = load_iris(return_X_y=True, as_frame=True)
+params = {"C": 1.0, "random_state": 42}
+classifier = LogisticRegression(**params).fit(X, y)
+
+predictions = classifier.predict(X)
+signature = infer_signature(X, predictions)
+
+with mlflow.start_run(run_name="test_pyfunc") as run:
+    model_info = mlflow.sklearn.log_model(sk_model=classifier, name="model", signature=signature)
+
+    # start a child run to create custom imagine model
+    with mlflow.start_run(run_name="test_custom_model", nested=True):
+        print(f"Pyfunc run ID: {run.info.run_id}")
+        # log a custom model
+        mlflow.pyfunc.log_model(
+            name="artifacts",
+            code_paths=[os.getcwd()],
+            artifacts={"custom_model": model_info.model_uri},
+            python_model=CustomPredict(),
+            signature=signature,
+        )