```

Use one of the following approaches to view job output:

View output in the Scala shell:

```
scala> counts.count()
364
```

To view the output from MinIO exit the Scala shell. View WordCount job status:

```
hadoop fs -ls s3a://testbucket/wordcount
```

The output should be similar to the following:

```
Found 3 items

// Prepare tracing to the expected number of output for an operation.
void TF_OutputListSetNumOutputs(TF_OutputList* o, int num_outputs, TF_Status*);
// Return the number of outputs in the list.
int TF_OutputListNumOutputs(TF_OutputList* o);
// Return the `i`th output in the list.
TF_AbstractTensor* TF_OutputListGet(TF_OutputList* o, int i);
// Append a tensor at the end of the output list, growing its size by one.

}

sourceSets {
  integTest {
    compileClasspath += sourceSets["main"].output + configurations["testRuntimeClasspath"]
    runtimeClasspath += output + compileClasspath
  }
}

tasks.withType(JavaCompile).configureEach {
  options.encoding = 'UTF-8'
}

tasks.named('licenseHeaders').configure {

            if it should exclude from the output the fields that had the same default
            value, even when they were explicitly set.
            """
        ),
    ] = False,
    exclude_none: Annotated[
        bool,
        Doc(
            """
            Pydantic's `exclude_none` parameter, passed to Pydantic models to define
            if it should exclude from the output any fields that have a `None` value.

     * This method is synchronized to ensure thread safety when managing processes.
     *
     * @param sessionId unique identifier for the process session
     * @param cmdList list of command and arguments to execute
     * @param pbCall callback to configure the ProcessBuilder
     * @param bufferSize size of the buffer for process output

    /**
     * Wraps exception handling for {@link Properties#store(OutputStream, String)}.
     *
     * <p>
     * The output stream is not closed.
     * </p>
     *
     * @param props
     *            Property set. Must not be {@literal null}.
     * @param out
     *            Output stream. Must not be {@literal null}.
     * @param comments
     *            Comments.
     */

      // requireNonNull is safe because of the hasNext check.
      Node<K, V> node = requireNonNull(next);
      next = node.nextInKeyInsertionOrder;
      toRemove = node;
      remaining--;
      return output(node);
    }

    @Override
    public final void remove() {
      if (biMap.modCount != expectedModCount) {
        throw new ConcurrentModificationException();
      }
      if (toRemove == null) {

 */
public class ReleaseHighlightsGenerator {
    static void update(File templateFile, File outputFile, List<ChangelogEntry> entries) throws IOException {
        try (FileWriter output = new FileWriter(outputFile)) {
            output.write(
                generateFile(QualifiedVersion.of(VersionProperties.getElasticsearch()), Files.readString(templateFile.toPath()), entries)
            );
        }
    }

}
```

## 출력 데이터용 Pydantic `bytes` { #pydantic-bytes-for-output-data }

출력 데이터에도 모델 설정에서 `ser_json_bytes`와 함께 `bytes` 필드를 사용할 수 있습니다. 그러면 Pydantic이 JSON 응답을 생성할 때 바이트를 base64로 “직렬화”합니다.

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

## 입력과 출력 데이터용 Pydantic `bytes` { #pydantic-bytes-for-input-and-output-data }

    }
  }

  /**
   * Implementation of {@link Stream#flatMap} with a primitive spliterator output type.
   *
   * @param <InElementT> the element type of the input spliterator
   * @param <OutElementT> the (boxed) element type of the output spliterators
   * @param <OutConsumerT> the specialized consumer type for the primitive output type
   * @param <OutSpliteratorT> the primitive spliterator type associated with {@code OutElementT}