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

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

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

            // 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")

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

   *     }
   * };
   * Multimap<String, String> transformed =
   *     Multimaps.transformValues(multimap, square);
   *   System.out.println(transformed);
   * }
   *
   * ... prints {@code {a=[4, 16], b=[9, 9], c=[36]}}.
   *
   * <p>Changes in the underlying multimap are reflected in this view. Conversely, this view
   * supports removal operations, and these are reflected in the underlying multimap.

   *     }
   * };
   * Multimap<String, String> transformed =
   *     Multimaps.transformValues(multimap, square);
   *   System.out.println(transformed);
   * }
   *
   * ... prints {@code {a=[4, 16], b=[9, 9], c=[36]}}.
   *
   * <p>Changes in the underlying multimap are reflected in this view. Conversely, this view
   * supports removal operations, and these are reflected in the underlying multimap.

  \"-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,
}
```