<!--
			 "node.name":"search_engine",
			 "discovery.seed_hosts":"search_engine",
			 "cluster.initial_cluster_manager_nodes":"search_engine",
			 "node.roles":"cluster_manager,data,ingest,ml",
			 -->
			 "indices.breaker.total.limit":"100%",
			 "action.auto_create_index":"-*"}
		</property>
		<!-- Dictionaries -->
		<postConstruct name="addConfigFile">
			<arg>"fess"</arg>

    //                                                                              ======
    @Override
    public Map<String, Object> toSource() {
        Map<String, Object> sourceMap = new HashMap<>();
        if (businessCategory != null) {
            addFieldToSource(sourceMap, "businessCategory", businessCategory);
        }
        if (carLicense != null) {

        return source;
    }

    protected Map<String, Object> toLowerHyphen(final Map<String, Object> source) {
        return source.entrySet().stream()
                .collect(Collectors.toMap(e -> CaseFormat.UPPER_CAMEL.to(CaseFormat.LOWER_UNDERSCORE, e.getKey()), e -> {
                    final Object value = e.getValue();
                    if (value instanceof Map) {

import org.codelibs.fess.helper.UserInfoHelper;
import org.codelibs.fess.helper.ViewHelper;
import org.codelibs.fess.helper.VirtualHostHelper;
import org.codelibs.fess.indexer.IndexUpdater;
import org.codelibs.fess.ingest.IngestFactory;
import org.codelibs.fess.job.JobExecutor;
import org.codelibs.fess.ldap.LdapManager;
import org.codelibs.fess.mylasta.direction.FessConfig;
import org.codelibs.fess.mylasta.direction.FessProp;

 * use a secondary key to break ties in primary priority values. For example, here is a class that
 * applies first-in-first-out tie-breaking to comparable elements. To use it, you would insert a
 * {@code new FIFOEntry(anEntry)} instead of a plain entry object.
 *
 * <pre>
 * class FIFOEntry&lt;E extends Comparable&lt;? super E&gt;&gt;
 *     implements Comparable&lt;FIFOEntry&lt;E&gt;&gt; {
 *   final static AtomicLong seq = new AtomicLong();

#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_macros.h"

#ifdef __cplusplus
extern "C" {
#endif

typedef struct TFE_ContextOptions TFE_ContextOptions;

// Return a new options object.
TF_CAPI_EXPORT extern TFE_ContextOptions* TFE_NewContextOptions(void);

// Set the config in TF_ContextOptions.options.
// config should be a serialized tensorflow.ConfigProto proto.

```Python hl_lines="2  14"
{!../../docs_src/custom_response/tutorial007.py!}
```

#### Using `StreamingResponse` with file-like objects

If you have a file-like object (e.g. the object returned by `open()`), you can create a generator function to iterate over that file-like object.

That way, you don't have to read it all first in memory, and you can pass that generator function to the `StreamingResponse`, and return it.

                * Serialization: you could return an arbitrary object and the
                    `response_model` would be used to serialize that object into the
                    corresponding JSON.
                * Filtering: the JSON sent to the client will only contain the data
                    (fields) defined in the `response_model`. If you returned an object

  tensorflow::ClusterDef* cluster_def = server_def.mutable_cluster();
  tensorflow::JobDef* job_def = cluster_def->mutable_job(0);
  int port = tensorflow::testing::PickUnusedPortOrDie();
  job_def->mutable_tasks()->insert(
      {2, tensorflow::strings::StrCat("localhost:", port)});
  server_def.set_task_index(0);
  string serialized_update = server_def.SerializeAsString();
  TFE_ContextUpdateServerDef(ctx, 0, serialized_update.data(),

		mkTest(16, 4, 8),
		// Exact simple majority consensus
		mkTest(16, 4, 9),
		// non-tiered object require read quorum of EcM
		nonTieredTest(15, 3, 12),
		// non-tiered object with fewer than EcM in consensus
		nonTieredTest(15, 3, 11),
		// non-tiered object require read quorum of EcM
		nonTieredTest(16, 4, 12),
		// non-tiered object with fewer than EcM in consensus
		nonTieredTest(16, 4, 11),
	}
	for i, test := range tests {