It's not required to specify all the tags used.

                The tags that are not declared MAY be organized randomly or based
                on the tools' logic. Each tag name in the list MUST be unique.

                The value of each item is a `dict` containing:

                * `name`: The name of the tag.
                * `description`: A short description of the tag.

                }
            }
        }
        if (processorTypes != null) {
            context.put(CONTEXT_KEY, processorTypes);
        }
        return root;
    }

    /**
     * Builds a unique key for an artifact based on the same identity components
     * used by conflict resolution: groupId, artifactId, extension, and classifier.
     */
    static String conflictKey(DependencyNode node) {

     * Represents an active Fess instance discovered via heartbeat.
     */
    public static class InstanceInfo {
        /** Default constructor. */
        public InstanceInfo() {
        }

        /** The unique instance ID. */
        public String instanceId;
        /** The hostname of the instance. */
        public String hostname;
        /** The display name of the instance. */
        public String name;

	Close() error
}
</pre>

<p>
The name of each explicitly specified method must be <a href="#Uniqueness_of_identifiers">unique</a>
and not <a href="#Blank_identifier">blank</a>.
</p>

<pre>
interface {
	String() string
	String() string  // illegal: String not unique
	_(x int)         // illegal: method must have non-blank name
}
</pre>

<p>

    public void setForceStop(final boolean b) {
        forceStop.set(b);
    }

    /**
     * Generates a document ID.
     *
     * @param map A map of data for the document.
     * @return A unique document ID.
     */
    public String generateDocId(final Map<String, Object> map) {
        return UUID.randomUUID().toString().replace("-", StringUtil.EMPTY);
    }

    /**

    *   Promoting `tf.data.experimental.ThreadingOptions` API to
        `tf.data.ThreadingOptions` and deprecating the experimental endpoint.
    *   Promoting `tf.data.experimental.unique` API to `tf.data.Dataset.unique`
        and deprecating the experimental endpoint.
    *   Added `stop_on_empty_dataset` parameter to `sample_from_datasets` and
        `choose_from_datasets`. Setting `stop_on_empty_dataset=True` will stop