painless
}

dependencies {
  api(project(":server")) {
    // JMH ships with the conflicting version 4.6. This prevents us from using jopt-simple in benchmarks (which should be ok) but allows
    // us to invoke the JMH uberjar as usual.
    exclude group: 'net.sf.jopt-simple', module: 'jopt-simple'
  }
  expression(project(path: ':modules:lang-expression', configuration: 'zip'))

    assert response.headers["WWW-Authenticate"] == 'Basic realm="simple"'


def test_security_http_basic_invalid_credentials():
    response = client.get(
        "/users/me", headers={"Authorization": "Basic notabase64token"}
    )
    assert response.status_code == 401, response.text
    assert response.headers["WWW-Authenticate"] == 'Basic realm="simple"'
    assert response.json() == {"detail": "Not authenticated"}

* external APIs
* authentication and authorization systems
* API usage monitoring systems
* response data injection systems
* etc.

## Simple and Powerful { #simple-and-powerful }

Although the hierarchical dependency injection system is very simple to define and use, it's still very powerful.

You can define dependencies that in turn can define dependencies themselves.

        return args;
    }

    @Override
    public String getMessage() {
        return message;
    }

    /**
     * Returns a simple message without a message code.
     *
     * @return simple message without message code
     */
    public final String getSimpleMessage() {
        return simpleMessage;
    }

        assertSame(name1, name2); // Should be same instance
    }

    /**
     * Test searcher with simple class name.
     */
    @Test
    public void test_simpleClassNameSearcher() {
        final SimpleSearcher searcher = new SimpleSearcher();
        assertEquals("simple", searcher.getName());
    }

    /**
     * Test searcher extending another searcher.
     */
    @Test

*.pbxproj
*.xcworkspace
/*.podspec
/tensorflow/lite/**/coreml/**/BUILD
/tensorflow/lite/**/ios/BUILD
/tensorflow/lite/**/objc/BUILD
/tensorflow/lite/**/swift/BUILD
/tensorflow/lite/examples/ios/simple/data/*.tflite
/tensorflow/lite/examples/ios/simple/data/*.txt
Podfile.lock
Pods

# Build execution model

At the highest level, Gradle's execution model is quite simple:

Below is the protocol in some more detail:

1. The client looks for a compatible idle daemon. If there isn't one, it starts a new daemon.
2. The client connects to the idle daemon and sends it a request to do some work. If the daemon is no longer running, the client starts again.

    @Test
    public void test_parse_quoted_values() {
        String value;
        String[] result;

        // Simple quoted value - quotes are removed
        value = "\"simple\"";
        result = KuromojiCSVUtil.parse(value);
        assertEquals(1, result.length);
        assertEquals("\"simple\"", result[0]);

        // Quoted value with comma - quotes are removed
        value = "\"value,with,commas\"";

		parities []int
		parity   int
	}{
		// More than simple majority consensus
		mkTest(15, 3, 11),
		// No simple majority consensus
		mkTest(15, 3, 7),
		// Exact simple majority consensus
		mkTest(15, 3, 8),
		// More than simple majority consensus
		mkTest(16, 4, 11),
		// No simple majority consensus
		mkTest(16, 4, 8),
		// Exact simple majority consensus
		mkTest(16, 4, 9),

Apart from all the fancy words used here, the **Dependency Injection** system is quite simple.

Just functions that look the same as the *path operation functions*.

But still, it is very powerful, and allows you to declare arbitrarily deeply nested dependency "graphs" (trees).

/// tip

All this might not seem as useful with these simple examples.

But you will see how useful it is in the chapters about **security**.