// Test PRINT share type
            setPrivateField(response, "shareType", Smb2TreeConnectResponse.SMB2_SHARE_TYPE_PRINT);
            assertEquals(Smb2TreeConnectResponse.SMB2_SHARE_TYPE_PRINT, response.getShareType(), "Should handle PRINT share type");
        }

        @Test
        @DisplayName("Should handle share flags")

Por exemplo, você poderia ter um arquivo `main.py` com:

```Python hl_lines="3"
import os

name = os.getenv("MY_NAME", "World")
print(f"Hello {name} from Python")
```

/// tip | Dica

{* ../../docs_src/handling_errors/tutorial006.py hl[2:5,15,21] *}

          echo "MAVEN_HOME=$PWD/maven-local" >> $GITHUB_ENV
          echo "$PWD/maven-local/bin" >> $GITHUB_PATH

      - name: Build Maven and ITs and run them
        shell: bash
        run: mvn verify -e -B -V -Prun-its,mimir

      - name: Upload test artifacts
        uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4
        if: failure() || cancelled()
        with:

  \"-Dmaven.multiModuleProjectDirectory=$MAVEN_PROJECTBASEDIR\" \
  $LAUNCHER_CLASS \
  $MAVEN_ARGS"
# Add remaining arguments with proper quoting
for arg in "$@"; do
    cmd="$cmd \"$arg\""
done

# Debug: print the command that will be executed
#echo "About to execute:"
#echo "$cmd"

e depois chamarmos:

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

agora temos um `dict` com os dados na variável `user_dict` (é um `dict` em vez de um objeto de modelo Pydantic).

E se chamarmos:

```Python
print(user_dict)
```

teríamos um `dict` Python com:

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

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

то теперь у нас есть `dict` с данными модели в переменной `user_dict` (это `dict` вместо объекта Pydantic-модели).

И если мы вызовем:

```Python
print(user_dict)
```

мы можем получить `dict` с такими данными:

```Python
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

Sie könnten zum Beispiel eine Datei `main.py` haben mit:

```Python hl_lines="3"
import os

name = os.getenv("MY_NAME", "World")
print(f"Hello {name} from Python")
```

/// tip | Tipp

      if (delegate == null) {
        return;
      }

      /*
       * If we're about to complete the TimeoutFuture, we want to release our reference to it.
       * Otherwise, we'll pin it (and its result) in memory until the timeout task is GCed. (The
       * need to clear our reference to the TimeoutFuture is the reason we use a *static* nested
       * class with a manual reference back to the "containing" class.)

```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
{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```