</association>
        </field>
      </fields>
    </class>
    <class>
      <name>Profile</name>
      <version>1.0.0</version>
      <description><![CDATA[
        Modifications to the build process which is keyed on some
        sort of environmental parameter.
      ]]></description>
      <fields>
        <field>
          <name>id</name>
          <required>true</required>

* Любое упорядоченное количество аргументов, которые принимает функция (`email`).
* Любое количество именованных аргументов, которые принимает функция (`message="some notification"`).

## Встраивание зависимостей

# `VERSION` parameter.
# Note: 'Templatizing' this and the other conditional branches would require
# defining several variables (version, os, path) making it difficult to maintain
# and extend for future modifications.
mkdir -p glibc-src
mkdir -p glibc-build
cd glibc-src
wget --retry-connrefused --waitretry=1 --read-timeout=20 --timeout=15 --tries=5 "https://vault.centos.org/centos/7/os/Source/SPackages/glibc-2.17-317.el7.src.rpm"

* Eine beliebige Folge von Argumenten, die der Reihe nach an die Taskfunktion übergeben werden sollen (`email`).
* Alle Schlüsselwort-Argumente, die an die Taskfunktion übergeben werden sollen (`message="some notification"`).

## Dependency Injection

        // Should not throw exception when notification is disabled
        try {
            crawler.sendMail(infoMap);
        } catch (Exception e) {
            fail("Should not throw exception: " + e.getMessage());
        }
    }

    public void test_sendMail_withNotificationEnabled() {
        // Override fessConfig to enable notification

limitations under the License.
==============================================================================*/

#include "tensorflow/c/c_api_experimental.h"

#include "absl/strings/substitute.h"
#include "absl/synchronization/notification.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_internal.h"
#include "tensorflow/c/checkpoint_reader.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_internal.h"

						</ul>
					</div>
				</div>
			</nav>
		</header>

		<main id="content" class="container">
			<h2>
				<la:message key="labels.advance_search_title" />
			</h2>
			<div class="notification">${notification}</div>
			<div>
				<la:info id="msg" message="true">
					<div class="alert alert-info">${msg}</div>
				</la:info>

process images, audio, videos, and text. There are several modules specialized
in taking those formats, modifying them, and/or converting them to intermediate
formats that can be processed by TensorFlow.

These modifications and conversions are handled by a variety of libraries that
have different security properties and provide different levels of confidence
when dealing with untrusted data. Based on the security history of these

	for _, arn := range globalLambdaTargetList.List(globalSite.Region()) {
		arnMsg += color.Bold(fmt.Sprintf("%s ", arn))
	}
	logger.Startup(arnMsg + "\n")
}

// Prints bucket notification configurations.
func printEventNotifiers() {
	if globalNotificationSys == nil {
		return
	}

	arns := globalEventNotifier.GetARNList()
	if len(arns) == 0 {
		return
	}

}

func initAllSubsystems(ctx context.Context) {
	// Initialize notification peer targets
	globalNotificationSys = NewNotificationSys(globalEndpoints)

	// Create new notification system
	if globalEventNotifier == nil {
		globalEventNotifier = NewEventNotifier(GlobalContext)
	}

	// Create new bucket metadata system.