Porque tenemos un proxy con un prefijo de path de `/api/v1` para nuestra aplicación, el frontend necesita obtener el esquema de OpenAPI en `/api/v1/openapi.json`.

```mermaid
graph LR

browser("Navegador")
proxy["Proxy en http://0.0.0.0:9999/api/v1/app"]
server["Servidor en http://127.0.0.1:8000/app"]

browser --> proxy
proxy --> server
```

/// tip | Consejo

*   [Python license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/nn.py#L1)
*   [Java license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/Graph.java#L1)
*   [Go license example](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/go/operation.go#L1)

Da wir für unsere Anwendung einen Proxy mit dem Pfadpräfix `/api/v1` haben, muss das Frontend das OpenAPI-Schema unter `/api/v1/openapi.json` abrufen.

```mermaid
graph LR

browser("Browser")
proxy["Proxy auf http://0.0.0.0:9999/api/v1/app"]
server["Server auf http://127.0.0.1:8000/app"]

browser --> proxy
proxy --> server
```

/// tip | Tipp

- kubeadm will now print required flags when running `kubeadm upgrade plan`.([#65802](https://github.com/kubernetes/kubernetes/pull/65802), [@xlgao-zju](https://github.com/xlgao-zju))
- Unix support for ZFS as a valid graph driver has been added for Docker, enabling users to use Kubeadm with ZFS. ([#65635](https://github.com/kubernetes/kubernetes/pull/65635), [@neolit123](https://github.com/neolit123))

### SIG GCP

import com.google.common.annotations.J2ktIncompatible;
import com.google.common.base.Optional;
import com.google.common.base.Predicate;
import com.google.common.collect.ImmutableList;
import com.google.common.graph.Traverser;
import com.google.j2objc.annotations.J2ObjCIncompatible;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.nio.channels.Channels;

IX+5AWPFeAG7Q8/Nb84wT3spE4L9JMpnhgx+Q==","X-Minio-Internal-Server-Side-Encryption-Seal-Algorithm":"DAREv2-HMAC-SHA256","content-type":"application/octet-stream"}},{"Bucket":"buck1","Name":"go_113/src/cmd/vendor/github.com/google/pprof/internal/graph/graph.go","UserDef":{"X-Minio-Internal-Server-Side-Encryption-Iv":"k88Uat8VyiUdn0jTQSen=","X-Minio-Internal-Server-Side-Encryption-S3-Kms-Key-Id":"my-minio-key","X-Minio-Internal-Server-Side-Encryption-S3-Kms-Sealed-Key":"IAAfAJQCzy9qvFJr5WHi/lAXtl6V...

    
    From: [@mikedanese](https://github.com/mikedanese):
    Instead of aliasing. Aliases are annoying in a number of ways. This is
    specifically bugging me now because they make the action graph harder to
    analyze programmatically. By using aliases here, we would need to handle
    potentially aliased go_binary targets and dereference to the effective
    target.
  

    
    From: [@mikedanese](https://github.com/mikedanese):
    Instead of aliasing. Aliases are annoying in a number of ways. This is
    specifically bugging me now because they make the action graph harder to
    analyze programmatically. By using aliases here, we would need to handle
    potentially aliased go_binary targets and dereference to the effective
    target.
  

    ],
    "access_right": "open",
    "notes": "Specific TensorFlow versions can be found in the \"Versions\" list on the right side of this page.<br>See the full list of authors <a href=\"https://github.com/tensorflow/tensorflow/graphs/contributors\">on GitHub</a>."

// representation between various supported formats e.g. FunctionDef and Mlir
// function.
class AbstractFunction : public core::RefCounted {
 protected:
  enum AbstractFunctionKind { kGraph, kMlir };
  explicit AbstractFunction(AbstractFunctionKind kind) : kind_(kind) {}

 public:
  // Returns which subclass is this instance of.
  AbstractFunctionKind getKind() const { return kind_; }