val fqName = callableId.asSingleFqName()
                            val classFqName = callableId.classId?.asSingleFqName()
                            // either it is a member declaration
                            if (classFqName != null) {
                                if (classFqName != qualifiedNameAsFqName) {
                                    this += classFqName

  // +optional
  optional NonResourceAttributes nonResourceAttributes = 2;

  // User is the user you're testing for.
  // If you specify "User" but not "Groups", then is it interpreted as "What if User were not a member of any groups
  // +optional
  optional string user = 3;

  // Groups is the groups you're testing for.
  // +optional
  repeated string groups = 4;

{!../../../docs_src/path_params/tutorial005.py!}
```

#### Получение *значения перечисления*

Можно получить фактическое значение (в данном случае - `str`) с помощью `model_name.value` или в общем случае `your_enum_member.value`:

```Python hl_lines="20"
{!../../../docs_src/path_params/tutorial005.py!}
```

!!! tip "Подсказка"
    Значение `"lenet"` также можно получить с помощью `ModelName.lenet.value`.

			// expired.
			nonExistentUsers = append(nonExistentUsers, dn)
		}
	}
	return nonExistentUsers, nil
}

// LookupGroupMemberships - for each DN finds the set of LDAP groups they are a
// member of.
func (l *Config) LookupGroupMemberships(userDistNames []string, userDNToUsernameMap map[string]string) (map[string]set.StringSet, error) {
	conn, err := l.LDAP.Connect()
	if err != nil {
		return nil, err
	}

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

      .assertLogEqual("SeNsItIvE: ██")
      .assertLogEqual("Not-Sensitive: Value")
      .assertLogEqual("<-- END HTTP")
      .assertNoMoreLogs()
  }

  @Suppress("INVISIBLE_MEMBER", "INVISIBLE_REFERENCE")
  @Test
  fun sensitiveQueryParamsAreRedacted() {
    url = server.url("/api/login?user=test_user&authentication=basic&password=confidential_password")
    val networkInterceptor =

  // completedIndexes holds the completed indexes when .spec.completionMode =
  // "Indexed" in a text format. The indexes are represented as decimal integers
  // separated by commas. The numbers are listed in increasing order. Three or
  // more consecutive numbers are compressed and represented by the first and
  // last element of the series, separated by a hyphen.
  // For example, if the completed indexes are 1, 3, 4, 5 and 7, they are

  // +optional
  optional NonResourceAttributes nonResourceAttributes = 2;

  // User is the user you're testing for.
  // If you specify "User" but not "Group", then is it interpreted as "What if User were not a member of any groups
  // +optional
  optional string user = 3;

  // Groups is the groups you're testing for.
  // +optional
  repeated string group = 4;

```Python hl_lines="17"
{!../../../docs_src/path_params/tutorial005.py!}
```

#### *Enumeration Değerini* Edinelim

`model_name.value` veya genel olarak `your_enum_member.value` tanımlarını kullanarak (bu durumda bir `str` olan) gerçek değere ulaşabilirsiniz:

```Python hl_lines="20"
{!../../../docs_src/path_params/tutorial005.py!}
```

!!! tip "İpucu"

        /**
         * This method is used from [org.jetbrains.kotlin.backend.jvm.lower.ReflectiveAccessLowering.visitCall]
         * (via generateReflectiveAccessForGetter) and it is called for the private access member lowered to the getter/setter call.
         * If a private property has no getter/setter (the typical situation for simple private properties without explicitly defined