chore: import upstream snapshot with attribution

doc/source/serve/doc_code/class_based_autoscaling.py

@@ -0,0 +1,46 @@
+# __serve_example_begin__
+import json
+import tempfile
+
+from ray import serve
+from ray.serve.config import AutoscalingConfig, AutoscalingPolicy
+
+# Create a JSON file with the initial target replica count.
+# In production this file would be written by an external system.
+scaling_file = tempfile.NamedTemporaryFile(
+    mode="w", suffix=".json", delete=False
+)
+json.dump({"replicas": 2}, scaling_file)
+scaling_file.close()
+
+
+@serve.deployment(
+    autoscaling_config=AutoscalingConfig(
+        min_replicas=1,
+        max_replicas=10,
+        upscale_delay_s=3,
+        downscale_delay_s=10,
+        policy=AutoscalingPolicy(
+            policy_function="class_based_autoscaling_policy:FileBasedAutoscalingPolicy",
+            policy_kwargs={
+                "file_path": scaling_file.name,
+                "poll_interval_s": 2.0,
+            },
+        ),
+    ),
+    max_ongoing_requests=100,
+)
+class MyDeployment:
+    async def __call__(self) -> str:
+        return "Hello, world!"
+
+
+app = MyDeployment.bind()
+# __serve_example_end__
+
+if __name__ == "__main__":
+    import requests  # noqa
+
+    serve.run(app)
+    resp = requests.get("http://localhost:8000/")
+    assert resp.text == "Hello, world!"