* all elements in priority order and place them in another collection.
 *
 * <p>Operations on this class make no guarantees about the ordering of elements with equal
 * priority. If you need to enforce an ordering, you can define custom classes or comparators that
 * use a secondary key to break ties in primary priority values. For example, here is a class that
 * applies first-in-first-out tie-breaking to comparable elements. To use it, you would insert a

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_C_EAGER_C_API_H_
#define TENSORFLOW_C_EAGER_C_API_H_

// C API extensions to experiment with eager execution of kernels.
// WARNING: Unlike tensorflow/c/c_api.h, the API here is not guaranteed to be
// stable and can change without notice.

	prometheus.MustRegister(minioVersionInfo)
}

// newMinioCollector describes the collector
// and returns reference of minioCollector
// It creates the Prometheus Description which is used
// to define metric and  help string
func newMinioCollector() *minioCollector {
	return &minioCollector{
		desc: prometheus.NewDesc("minio_stats", "Statistics exposed by MinIO server", nil, nil),
	}
}

2)  Decrypt encrypted object key with the KEK derived from the data key.
3a) Receive new plain data key from the KMS using the master key ID of the server config.
3b) Receive encrypted form of the data key from the KMS.
4)  Derive a new KEK from the new data key and re-encrypt the OEK with it.
5)  Store the encrypted OEK encrypted data key and master key ID in object metadata.
 ```

#### „Schema“

Ein „Schema“ ist eine Definition oder Beschreibung von etwas. Nicht der eigentliche Code, der es implementiert, sondern lediglich eine abstrakte Beschreibung.

#### API-„Schema“

In diesem Fall ist  <a href="https://github.com/OAI/OpenAPI-Specification" class="external-link" target="_blank">OpenAPI</a> eine Spezifikation, die vorschreibt, wie ein Schema für Ihre API zu definieren ist.

    * Como as estruturas de dados do Pydantic são apenas instâncias de classes que você define, a auto completação, _linting_, _mypy_ e a sua intuição devem funcionar corretamente com seus dados validados.
* Valida **estruturas complexas**:
    * Use modelos hierárquicos do Pydantic, `List` e `Dict` do `typing` do Python, etc.

Create a variable `ALGORITHM` with the algorithm used to sign the JWT token and set it to `"HS256"`.

Create a variable for the expiration of the token.

Define a Pydantic Model that will be used in the token endpoint for the response.

Create a utility function to generate a new access token.

//// tab | Python 3.10+

```Python hl_lines="4 7  13-15  29-31  79-87"

---

If you just don't know, use normal `def`.

---

**Note**: You can mix `def` and `async def` in your *path operation functions* as much as you need and define each one using the best option for you. FastAPI will do the right thing with them.

Anyway, in any of the cases above, FastAPI will still work asynchronously and be extremely fast.

//// tab | Python 3.8+

```Python hl_lines="31  37"
{!> ../../docs_src/response_model/tutorial006.py!}
```

////

## Recap

Use the *path operation decorator's* parameter `response_model` to define response models and especially to ensure private data is filtered out.

  if (op_reg_data->shape_inference_fn == nullptr) {
    status->status =
        InvalidArgument("No shape inference function exists for op '",
                        node_def.op(), "', did you forget to define it?");
    return;
  }

  status->status = c.Run(op_reg_data->shape_inference_fn);
  if (!status->status.ok()) return;

  // Set output_shapes.
  TF_ShapeAndTypeList* output_shapes_result =