dataStoreNames = nameList.toArray(n -> new String[nameList.size()]);
            lastLoadedTime = now;
        }
        return dataStoreNames;
    }

    /**
     * Loads the list of available data store names by scanning plugin JAR files.
     * This method searches for 'fess_ds++.xml' configuration files within JAR files
     * in the data store plugin directory and extracts component class names.

func (sys *IAMSys) Initialized() bool {
	if sys == nil {
		return false
	}
	sys.Lock()
	defer sys.Unlock()
	return sys.store != nil
}

// Load - loads all credentials, policies and policy mappings.
func (sys *IAMSys) Load(ctx context.Context, firstTime bool) error {
	loadStartTime := time.Now()
	err := sys.store.LoadIAMCache(ctx, firstTime)
	if err != nil {

MINIO_IDENTITY_OPENID_REDIRECT_URI="http://127.0.0.1:10000/oauth_callback"
MINIO_IDENTITY_OPENID_CLAIM_NAME="groups"
```
</details>

### Redirection from OpenID Provider

#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

     * Loads the user through the authentication manager for complete user data.
     *
     * @param id the unique identifier of the user
     * @return an OptionalEntity containing the user if found
     */
    public OptionalEntity<User> getUser(final String id) {
        return userBhv.selectByPK(id).map(u -> ComponentUtil.getAuthenticationManager().load(u));
    }

    /**

        PropertiesUtil.load(properties, inputStreamReader);
    }

    /**
     * Test method for
     * {@link org.codelibs.core.io.PropertiesUtil#load(Properties, File)} .
     */
    @Test
    public void testLoadPropertiesFile() {
        final Properties properties = new Properties();
        PropertiesUtil.load(properties, inputFile);

### Server Memory { #server-memory }

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

	return *resp, nil
}

// GetSRMetrics loads site replication metrics, optionally for a specific bucket
func (client *peerRESTClient) GetSRMetrics(ctx context.Context) (SRMetricsSummary, error) {
	resp, err := getSRMetricsRPC.Call(ctx, client.gridConn(), grid.NewMSS())
	if err != nil || resp == nil {
		return SRMetricsSummary{}, err
	}
	return *resp, nil
}

// GetAllBucketStats - load replication stats for all buckets

def test_index():
    client = TestClient(app)
    with client.websocket_connect("/") as websocket:
        data = json.loads(websocket.receive_text())
        assert data == ["app", "index"]


def test_routerindex():
    client = TestClient(app)
    with client.websocket_connect("/router") as websocket:
        data = json.loads(websocket.receive_text())
        assert data == ["app", "router2", "router", "routerindex"]

          @Override
          public String load(String key) throws Exception {
            return cache.get(
                key,
                identityLoader()); // recursive load (same as the initial one), this should fail
          }
        };
    CacheLoader<String, String> proxyLoader =
        new CacheLoader<String, String>() {
          @Override
          public String load(String key) throws Exception {