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

und wir rufen seine `.dict()`-Methode auf:

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

dann haben wir jetzt in der Variable `user_dict` ein `dict` mit den gleichen Daten (es ist ein `dict` statt eines Pydantic-Modellobjekts).

Wenn wir es ausgeben:

```Python
print(user_dict)
```

bekommen wir ein Python-`dict`:

```Python

	VPGATHERDQ (BP)(X15*2), K1, Y15 // ERROR "index and destination registers should be distinct"
	VPGATHERDQ (BP)(Y20*2), K1, Z20 // ERROR "index and destination registers should be distinct"
	// Instructions without EVEX variant can't use High-16 registers.
	VADDSUBPD X20, X1, X2           // ERROR "invalid instruction"
	VADDSUBPS X0, X20, X2           // ERROR "invalid instruction"
	// Use of K0 for write mask (Yknot0).

- Servers running distributed MinIO instances should be less than 15 minutes apart. You can enable [NTP](http://www.ntp.org/) service as a best practice to ensure same times across servers.
- `MINIO_DOMAIN` environment variable should be defined and exported for bucket DNS style support.
- Running Distributed MinIO on **Windows** operating system is considered **experimental**. Please proceed with caution.

if [ ! -x "$JAVACMD" ]; then
  echo "Error: JAVA_HOME is not defined correctly." >&2
  echo "  We cannot execute $JAVACMD" >&2
  exit 1
fi

if [ -z "$JAVA_HOME" ]; then
  echo "Warning: JAVA_HOME environment variable is not set." >&2
fi

# traverses directory structure from process work directory to filesystem root
# first directory with .mvn subdirectory is considered project base directory
find_maven_basedir() {

```sh
openssl genrsa -aes256 -passout pass:PASSWORD -out private.key 2048
```

**Note:** When using a password-protected private key, the password must be provided through the environment variable `MINIO_CERT_PASSWD` using the following command:

```sh
export MINIO_CERT_PASSWD=<PASSWORD>
```

                extractFile(ent.getKey(), embeddedFile, writer);
            }
        }
    }

    /**
     * Gets the embedded file from a file specification, trying different platform-specific variants.
     * @param fileSpec the file specification
     * @return the embedded file, or null if not found
     */
    protected PDEmbeddedFile getEmbeddedFile(final PDComplexFileSpecification fileSpec) {

Vous écrivez du python standard avec des annotations de types:

```Python
from datetime import date

from pydantic import BaseModel

# Déclare une variable comme étant une str
# et profitez de l'aide de votre IDE dans cette fonction
def main(user_id: str):
    return user_id


# Un modèle Pydantic
class User(BaseModel):
    id: int
    name: str

### Memoria por Proceso

Ahora, cuando el programa carga cosas en memoria, por ejemplo, un modelo de machine learning en una variable, o el contenido de un archivo grande en una variable, todo eso **consume un poco de la memoria (RAM)** del servidor.

    server.requireClientAuth()
    val call = client.newCall(Request.Builder().url(server.url("/")).build())
    assertFailsWith<IOException> {
      call.execute()
    }

    // Observed Events are variable
    // JDK 14
    // CallStart, ProxySelectStart, ProxySelectEnd, DnsStart, DnsEnd, ConnectStart, SecureConnectStart,
    // SecureConnectEnd, ConnectEnd, ConnectionAcquired, RequestHeadersStart, RequestHeadersEnd,

```Python
commons: Annotated[dict, Depends(common_parameters)]
```

Pero como estamos usando `Annotated`, podemos almacenar ese valor `Annotated` en una variable y usarlo en múltiples lugares:

{* ../../docs_src/dependencies/tutorial001_02_an_py310.py hl[12,16,21] *}

/// tip | Consejo