.withFeatures(COLLECTION_FEATURES_REMOVE_ORDER)
            .withFeatures(CollectionFeature.SUPPORTS_ITERATOR_REMOVE)
            .createTestSuite());

    Function<String, String> removeFirstCharacter =
        new Function<String, String>() {
          @Override
          public String apply(String input) {
            return input.substring(1);
          }
        };

    suite.addTest(

            .withFeatures(COLLECTION_FEATURES_REMOVE_ORDER)
            .withFeatures(CollectionFeature.SUPPORTS_ITERATOR_REMOVE)
            .createTestSuite());

    Function<String, String> removeFirstCharacter =
        new Function<String, String>() {
          @Override
          public String apply(String input) {
            return input.substring(1);
          }
        };

    suite.addTest(

		config.Net.SASL.Mechanism = sarama.SASLMechanism(sarama.SASLTypePlaintext)
	}
}

// KafkaSHA256 is a function that returns a crypto/sha256 hasher and should be used
// to create Client objects configured for SHA-256 hashing.
var KafkaSHA256 scram.HashGeneratorFcn = sha256.New

// KafkaSHA512 is a function that returns a crypto/sha512 hasher and should be used
// to create Client objects configured for SHA-512 hashing.

 * either express or implied. See the License for the specific language
 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.util;

import java.util.function.Consumer;

import org.codelibs.fess.Constants;

/**
 * A wrapper class that manages a system process for job execution.
 * This class provides access to the underlying process and manages

# Body - Alanlar { #body-fields }

`Query`, `Path` ve `Body` ile *path operation function* parametrelerinde ek doğrulama ve metadata tanımlayabildiğiniz gibi, Pydantic modellerinin içinde de Pydantic'in `Field`'ını kullanarak doğrulama ve metadata tanımlayabilirsiniz.

## `Field`'ı import edin { #import-field }

Önce import etmeniz gerekir:

{* ../../docs_src/body_fields/tutorial001_an_py310.py hl[4] *}

/// warning | Uyarı

        return java.util.stream.Stream.of(Arguments.of("getTreeType()", (org.junit.jupiter.api.function.Executable) () -> {
            CIFSContext ctx = mock(CIFSContext.class);
            new SmbTreeConnectionTrace(ctx).getTreeType();
        }), Arguments.of("getConnectedShare()", (org.junit.jupiter.api.function.Executable) () -> {
            CIFSContext ctx = mock(CIFSContext.class);

* `close()`: Dosyayı kapatır.

Bu method’ların hepsi `async` olduğundan, bunları "await" etmeniz gerekir.

Örneğin, bir `async` *path operation function* içinde içeriği şöyle alabilirsiniz:

```Python
contents = await myfile.read()
```

Normal bir `def` *path operation function* içindeyseniz `UploadFile.file`’a doğrudan erişebilirsiniz, örneğin:

```Python
contents = myfile.file.read()
```

import java.io.ObjectInputStream;
import java.io.ObjectOutputStream;
import java.util.Collection;
import java.util.Comparator;
import java.util.Map;
import java.util.Map.Entry;
import java.util.function.Function;
import java.util.stream.Collector;
import java.util.stream.Stream;
import org.jspecify.annotations.Nullable;

/**
 * A {@link ListMultimap} whose contents will never change, with many other important properties

#ifndef TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_
#define TENSORFLOW_C_EAGER_GRAPH_FUNCTION_H_

#include "tensorflow/c/eager/abstract_function.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/platform/refcount.h"
namespace tensorflow {
namespace tracing {
namespace graph {
using tensorflow::AbstractFunction;
// Thin wrapper around a FunctionDef.

## Un `StreamingResponse` con `yield` { #a-streamingresponse-with-yield }

Si declaras un `response_class=StreamingResponse` en tu *path operation function*, puedes usar `yield` para enviar cada bloque de datos a su vez.

{* ../../docs_src/stream_data/tutorial001_py310.py ln[1:23] hl[20,23] *}