* See the License for the specific language governing permissions and
 * limitations under the License.
 */

allprojects {
    tasks.withType(JavaCompile).configureEach {
        outputs.doNotCacheIf("CodeQL scanning", { true })
    }

	input       chan *queueItem           // input for workers
	queue       chan *queueItem           // output from workers in order
	err         error                     // global error state, only touched by Reader.Read
	bufferPool  bpool.Pool[[]byte]        // pool of []byte objects for input
	kvDstPool   bpool.Pool[[]jstream.KVS] // pool of []jstream.KVS used for output
	close       chan struct{}             // used for shutting down the splitter before end of stream

CHANGELOG.asciidoc  merge=union

# These files contain expected text output, and should not be changed on
# Windows

   * char} are hashed directly (in that order). The input must not be updated while this method is
   * in progress.
   *
   * <p><b>Warning:</b> This method will produce different output than most other languages do when
   * running the same hash function on the equivalent input. For cross-language compatibility, use
   * {@link #putString}, usually with a charset of UTF-8. For other use cases, use {@code

    }

    /**
     * Tests that the getter for the output stream returns the correct stream.
     * @throws CIFSException if an error occurs while getting the stream.
     */
    @Test
    public void testGetOutputStream() throws CIFSException {
        assertEquals(mockOutputStream, smbPipeHandle.getOutput(), "getOutput() should return the correct output stream.");
    }

    /**

}
```

## 出力データ向けの Pydantic `bytes` { #pydantic-bytes-for-output-data }

出力データ用にモデル設定で `ser_json_bytes` とともに `bytes` フィールドを使用することもでき、Pydantic は JSON レスポンスを生成するときにバイト列を base64 でシリアライズします。

{* ../../docs_src/json_base64_bytes/tutorial001_py310.py ln[1:2,12:16,29,38:41] hl[16] *}

## 入力・出力データ向けの Pydantic `bytes` { #pydantic-bytes-for-input-and-output-data }

{* ../../docs_src/body_nested_models/tutorial003_py310.py hl[12] *}

With this, even if you receive a request with duplicate data, it will be converted to a set of unique items.

And whenever you output that data, even if the source had duplicates, it will be output as a set of unique items.

And it will be annotated / documented accordingly too.

## Nested Models { #nested-models }

Each attribute of a Pydantic model has a type.

                .longOpt("quiet")
                .desc("Quiet output - only show errors")
                .build());
        options.addOption(Option.builder(Character.toString(VERBOSE))
                .longOpt("verbose")
                .desc("Produce execution verbose output")
                .build());
        options.addOption(Option.builder(Character.toString(ERRORS))

	if GlobalKMS == nil {
		output = input
		return output, metabytes, err
	}

	metadata := make(map[string]string)
	key, err := GlobalKMS.GenerateKey(ctx, &kms.GenerateKeyRequest{AssociatedData: kmsContext})
	if err != nil {
		return output, metabytes, err
	}

	outbuf := bytes.NewBuffer(nil)

                                    <tr>
                                        <th><la:message key="labels.dict_mapping_target"/></th>
                                        <td>${f:br(f:h(output))}<la:hidden property="output"/></td>
                                    </tr>
                                    </tbody>
                                </table>
                            </div>