"title": "Secret Name",
                                }
                            )
                            | IsDict(
                                # TODO: remove when deprecating Pydantic v1
                                {
                                    "type": "string",
                                    "title": "Secret Name",
                                }

  lua: warning
  main: warning
  misc: warning
  mongo: warning
  quic: warning
  pool: warning
  rbac: warning
  redis: warning
  router: warning
  runtime: warning
  stats: warning
  secret: warning
  tap: warning
  testing: warning
  thrift: warning
  tracing: warning
  upstream: warning
  udp: warning

	exit 1
fi

s3-check-md5 -versions -access-key minioadmin -secret-key minioadmin -endpoint http://127.0.0.1:9001/ -bucket bucket2
s3-check-md5 -versions -access-key minioadmin -secret-key minioadmin -endpoint http://127.0.0.1:9001/ -bucket versioned

///

## Handle JWT tokens

Import the modules installed.

Create a random secret key that will be used to sign the JWT tokens.

To generate a secure random secret key use the command:

<div class="termy">

```console
$ openssl rand -hex 32

09d25e094faa6ca2556c818166b7a9563b93f7099f6f0f4caa6cf63b88e8d3e7
```

  tensorflow::JobDef* job_def = server_def->mutable_cluster()->mutable_job(0);
  int port = tensorflow::testing::PickUnusedPortOrDie();
  job_def->mutable_tasks()->at(task_index) =
      tensorflow::strings::StrCat("localhost:", port);
}

void CheckTFE_TensorHandleHasFloats(TFE_TensorHandle* handle,
                                    const std::vector<float>& expected_values) {

{{- if not .Values.existingSecret }}
apiVersion: v1
kind: Secret
metadata:
  name: {{ template "minio.secretName" . }}
  labels:
    app: {{ template "minio.name" . }}
    chart: {{ template "minio.chart" . }}
    release: {{ .Release.Name }}
    heritage: {{ .Release.Service }}
type: Opaque
data:
  rootUser: {{ include "minio.root.username" . | b64enc | quote }}
  rootPassword: {{ include "minio.root.password" . | b64enc | quote }}

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

& ⤴️ 👥 🤙:

```Python
user_dict = user_in.dict()
```

👥 🔜 ✔️ `dict` ⏮️ 💽 🔢 `user_dict` (⚫️ `dict` ↩️ Pydantic 🏷 🎚).

& 🚥 👥 🤙:

```Python
print(user_dict)
```

👥 🔜 🤚 🐍 `dict` ⏮️:

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',

    return [pet1, pet2]


client = TestClient(app)


def test_filter_top_level_model():
    response = client.post(
        "/users", json={"email": "******@****.***", "password": "secret"}
    )
    assert response.json() == {"email": "******@****.***"}


def test_filter_second_level_model():
    response = client.get("/pets/1")
    assert response.json() == {
        "name": "Nibbler",

👆 🔜 👀 👩‍💻 🔢 💖:

<img src="/img/tutorial/security/image07.png">

✔ 🈸 🎏 🌌 ⏭.

⚙️ 🎓:

🆔: `johndoe`
🔐: `secret`

/// check

👀 👈 🕳 📟 🔢 🔐 "`secret`", 👥 🕴 ✔️ #️⃣ ⏬.

///

<img src="/img/tutorial/security/image08.png">

🤙 🔗 `/users/me/`, 👆 🔜 🤚 📨:

```JSON
{
  "username": "johndoe",

У Pydantic-моделей есть метод `.dict()`, который возвращает `dict` с данными модели.

Поэтому, если мы создадим Pydantic-объект `user_in` таким способом:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

и затем вызовем:

```Python
user_dict = user_in.dict()
```