chore: import upstream snapshot with attribution

python/samples/learn_resources/plugin.py

@@ -0,0 +1,60 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+from typing import Annotated
+
+from semantic_kernel import Kernel
+from semantic_kernel.functions import kernel_function
+
+
+# Let's define a light plugin
+class LightPlugin:
+    is_on: bool = False
+
+    @kernel_function(
+        name="get_state",
+        description="Gets the state of the light.",
+    )
+    def get_state(
+        self,
+    ) -> Annotated[str, "the output is a string"]:
+        """Returns the state result of the light."""
+        return "On" if self.is_on else "Off"
+
+    @kernel_function(
+        name="change_state",
+        description="Changes the state of the light.",
+    )
+    def change_state(
+        self,
+        new_state: Annotated[bool, "the new state of the light"],
+    ) -> Annotated[str, "the output is a string"]:
+        """Changes the state of the light."""
+        self.is_on = new_state
+        state = self.get_state()
+        print(f"The light is now: {state}")
+        return state
+
+
+async def main():
+    # Initialize the kernel
+    kernel = Kernel()
+
+    light_plugin = kernel.add_plugin(
+        LightPlugin(),
+        plugin_name="LightPlugin",
+    )
+
+    result = await kernel.invoke(light_plugin["get_state"])
+    print(f"The light is: {result}")
+
+    print("Changing the light's state...")
+
+    # Kernel Arguments are passed in as kwargs.
+    result = await kernel.invoke(light_plugin["change_state"], new_state=True)
+    print(f"The light is: {result}")
+
+
+# Run the main function
+if __name__ == "__main__":
+    asyncio.run(main())