dependencies: Optional[Sequence[Depends]] = None,
        summary: Optional[str] = None,
        description: Optional[str] = None,
        response_description: str = "Successful Response",
        responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
        deprecated: Optional[bool] = None,
        methods: Optional[List[str]] = None,
        operation_id: Optional[str] = None,

Entonces declara tus *path operation functions* con `async def` de la siguiente manera:

```Python hl_lines="2"
@app.get('/')
async def read_results():
    results = await some_library()
    return results
```

/// note | Nota

Solo puedes usar `await` dentro de funciones creadas con `async def`.

///

---

            "axisLabel": "",
            "axisPlacement": "auto",
            "barAlignment": 0,
            "drawStyle": "line",
            "fillOpacity": 10,
            "gradientMode": "none",
            "hideFrom": {
              "legend": false,
              "tooltip": false,
              "viz": false
            },
            "insertNulls": false,
            "lineInterpolation": "linear",

                                           TF_Status* status) {
  Node* node = &output.oper->node;

  mutex_lock l(graph->mu);
  tensorflow::shape_inference::InferenceContext* ic =
      graph->refiner.GetContext(node);
  if (ic == nullptr) {
    status->status =
        InvalidArgument("Node ", node->name(), " was not found in the graph");
    return;
  }

  auto shape_and_type_vec =

[Moshi](https://github.com/square/moshi) is a handy API for converting between JSON and Java objects. Here we're using it to decode a JSON response from a GitHub API.

Note that `ResponseBody.charStream()` uses the `Content-Type` response header to select which charset to use when decoding the response body. It defaults to `UTF-8` if no charset is specified.

=== ":material-language-kotlin: Kotlin"

							continue
						}
						// Bucket not found proceed to delete such a job.
					}
				} else {
					if err := job.Replicate.Start(job.ctx, j.objLayer, *job); err != nil {
						if !isErrBucketNotFound(err) {
							batchLogIf(j.ctx, err)
							j.canceler(job.ID, false)
							continue
						}
						// Bucket not found proceed to delete such a job.
					}
				}

  * More expressive node affinity syntax, and support for “soft” node affinity.
Node selectors (to constrain pods to schedule on a subset of nodes) now support
the operators {<code>In, NotIn, Exists, DoesNotExist, Gt, Lt</code>}  instead of just conjunction of exact match on node label values. In
addition, we’ve introduced a new “soft” kind of node selector that is just a

results = await some_library()
```

⤴️, 📣 👆 *➡ 🛠️ 🔢* ⏮️ `async def` 💖:

```Python hl_lines="2"
@app.get('/')
async def read_results():
    results = await some_library()
    return results
```

/// note

👆 💪 🕴 ⚙️ `await` 🔘 🔢 ✍ ⏮️ `async def`.

///

---

🚥 👆 ⚙️ 🥉 🥳 🗃 👈 🔗 ⏮️ 🕳 (💽, 🛠️, 📁 ⚙️, ♒️.) & 🚫 ✔️ 🐕‍🦺 ⚙️ `await`, (👉 ⏳ 💼 🌅 💽 🗃), ⤴️ 📣 👆 *➡ 🛠️ 🔢* 🛎, ⏮️ `def`, 💖:

     */
    boolean isHidden () throws CIFSException;


    /**
     * Tests to see if the file this <code>SmbResource</code> represents is not a directory.
     *
     * @return <code>true</code> if this <code>SmbResource</code> is not a directory
     * @throws CIFSException
     */
    boolean isFile () throws CIFSException;


    /**

  tensorflow::unwrap(ctx)->SetThreadLocalDevicePlacementPolicy(
      static_cast<tensorflow::ContextDevicePlacementPolicy>(policy));
}

// Note: this function looks up a thread local policy. So it should be called in
// the appropriate client thread. In particular, in async mode, it may not be
// safe to call this function from the async EagerExecutor threads.
extern TFE_ContextDevicePlacementPolicy TFE_ContextGetDevicePlacementPolicy(