chore: import upstream snapshot with attribution

doc/source/serve/doc_code/translator_example.py

@@ -0,0 +1,47 @@
+import requests
+
+# __serve_example_begin__
+import starlette
+
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+from ray import serve
+
+
+@serve.deployment
+class Translator:
+    def __init__(self):
+        self.tokenizer = AutoTokenizer.from_pretrained("t5-small")
+        self.model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
+
+    def translate(self, text: str) -> str:
+        input_ids = self.tokenizer(
+            f"translate English to German: {text}", return_tensors="pt"
+        ).input_ids
+        output_ids = self.model.generate(
+            input_ids, num_beams=4, early_stopping=True, max_length=300
+        )
+        return self.tokenizer.decode(
+            output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+    async def __call__(self, req: starlette.requests.Request):
+        req = await req.json()
+        return self.translate(req["text"])
+
+
+app = Translator.bind()
+# __serve_example_end__
+
+
+serve.run(app, name="app2", route_prefix="/translate")
+
+# __request_begin__
+text = "Hello, the weather is quite fine today!"
+resp = requests.post("http://localhost:8000/translate", json={"text": text})
+
+print(resp.text)
+# 'Hallo, das Wetter ist heute ziemlich gut!'
+# __request_end__
+
+assert resp.text == "Hallo, das Wetter ist heute ziemlich gut!"