}

    /**
     * Evaluates search results for relevance using the configured LLM client.
     *
     * @param userMessage the original user message
     * @param query the search query used
     * @param searchResults the search results to evaluate
     * @return evaluation result with relevant document IDs
     * @throws LlmException if LLM is not available
     */

            min_length=3,
            max_length=50,
            pattern="^fixedquery$",
            deprecated=True,
        ),
    ] = None,
):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

   * Runnable}, and arrange that {@code get} will return the given result on successful completion.
   *
   * @param runnable the runnable task
   * @param result the result to return on successful completion. If you don't need a particular
   *     result, consider using constructions of the form: {@code ListenableFuture<?> f =
   *     ListenableFutureTask.create(runnable, null)}

def do_blocking_work(name: str) -> str:
    # Some blocking I/O operation
    return f"Hello {name}"


@app.get("/items/")
async def read_items():
    result = await asyncify(do_blocking_work)(name="World")
    return {"message": result}
```

And run async code inside of blocking sync code with `syncify()`:

```python
from asyncer import syncify
from fastapi import FastAPI

app = FastAPI()

      tensorflow::NameAttrList* name_and_attrs) const = 0;

  virtual bool GetInt(absl::string_view, int64_t* result) const = 0;
  virtual bool GetFloat(absl::string_view attr_name, float* result) const = 0;
  virtual bool GetBool(absl::string_view attr_name, bool* result) const = 0;
  virtual bool GetType(absl::string_view attr_name, DataType* result) const = 0;
  virtual absl::Status GetTypeList(
      absl::string_view attr_name,

    for (CollectStrategy scheme : CollectStrategy.values()) {
      A finalAccum = scheme.result(collector, inputs);
      if (collector.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
        @SuppressWarnings("unchecked") // `R` and `A` match for an `IDENTITY_FINISH`
        R result = (R) finalAccum;
        assertEquivalent(expectedResult, result);
      }

    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):
    result = ml_models["answer_to_everything"](x)

                return false;
            }

            final Map<String, Object> result = parseJson(response.getContentAsString());
            final Boolean found = (Boolean) result.get("found");
            if (found == null || !found) {
                return false;
            }

            final Map<String, Object> source = getMapValue(result, "_source");
            if (source == null) {
                return false;

                // Verify all searches completed and returned results
                assertEquals(numSearchThreads, allResults.size());
                for (List<Map<String, Object>> results : allResults) {
                    assertEquals(10, results.size());
                }

            } finally {
                executor.shutdown();

        byte[] emptyToken = new byte[0];

        // WHEN parsing the auth data
        List<KerberosAuthData> result = KerberosAuthData.parse(unknownAuthType, emptyToken, mockKeys);

        // THEN the result should be an empty list
        assertNotNull(result, "The result should not be null.");
        assertTrue(result.isEmpty(), "The result should be empty for unknown auth types.");
    }

    /**