output_tensors, output_names_vec, control_output_nodes,
      control_output_names_vec, description, &fdef);
  if (TF_GetCode(status) != TF_OK) {
    return nullptr;
  }

  // Dump the op creation stacktraces for debugging purpose.
  DEBUG_DATA_DUMPER()->DumpOpCreationStackTraces(
      fn_name, kDebugGroupOpStacktrace, "initial", &fn_body->graph);

  tensorflow::StackTracesMap stack_traces;

    void testHexdumpWithOffset() {
        ByteArrayOutputStream baos = new ByteArrayOutputStream();
        PrintStream ps = new PrintStream(baos);

        // Create test data and dump from offset
        byte[] data = createTestData(64);
        int offset = 16;
        int length = 32;

        Hexdump.hexdump(ps, data, offset, length);
        String output = baos.toString();

   * output array, and may, under certain circumstances, be used to save allocation costs.
   *
   * <p>Suppose {@code x} is a queue known to contain only strings. The following code can be used
   * to dump the queue into a newly allocated array of {@code String}:
   *
   * <pre>
   *     String[] y = x.toArray(new String[0]);</pre>
   *

   * output array, and may, under certain circumstances, be used to save allocation costs.
   *
   * <p>Suppose {@code x} is a queue known to contain only strings. The following code can be used
   * to dump the queue into a newly allocated array of {@code String}:
   *
   * <pre>
   *     String[] y = x.toArray(new String[0]);</pre>
   *

labels.crawling_info_delete_all_link=Delete All
labels.crawling_info_delete_all_confirmation=Are you sure you want to delete all?
labels.crawling_info_delete_all_cancel=Cancel
labels.crawling_info_thread_dump=Thread Dump
labels.crawling_info_CrawlerStartTime=Crawler Start Time
labels.crawling_info_CrawlerEndTime=Crawler End Time
labels.crawling_info_CrawlerExecTime=Crawler Execution Time
labels.crawling_info_CrawlerStatus=Crawler Status

{* ../../docs_src/extra_models/tutorial001_py310.py hl[7,9,14,20,22,27:28,31:33,38:39] *}

### Про `**user_in.model_dump()` { #about-user-in-model-dump }

#### `.model_dump()` у Pydantic { #pydantics-model-dump }

`user_in` - це модель Pydantic класу `UserIn`.

Моделі Pydantic мають метод `.model_dump()`, який повертає `dict` з даними моделі.

Отже, якщо ми створимо об’єкт Pydantic `user_in` так:

	// Return extension of profile
	Extension() string
}

// Global profiler to be used by service go-routine.
var (
	globalProfiler   map[string]minioProfiler
	globalProfilerMu sync.Mutex
)

// dump the request into a string in JSON format.
func dumpRequest(r *http.Request) string {
	header := r.Header.Clone()
	header.Set("Host", r.Host)
	// Replace all '%' to '%%' so that printer format parser

   * output array, and may, under certain circumstances, be used to save allocation costs.
   *
   * <p>Suppose {@code x} is a queue known to contain only strings. The following code can be used
   * to dump the queue into a newly allocated array of {@code String}:
   *
   * <pre>
   *     String[] y = x.toArray(new String[0]);</pre>
   *

    # Local development
    # repos_path = Path("../docs/en/data/topic_repos.yml")
    repos_path = Path("./docs/en/data/topic_repos.yml")
    repos_old_content = repos_path.read_text(encoding="utf-8")
    new_repos_content = yaml.dump(data, sort_keys=False, width=200, allow_unicode=True)
    if repos_old_content == new_repos_content:
        logging.info("The data hasn't changed. Finishing.")
        return

    /** The key of the message: Unauthorized request. */
    public static final String ERRORS_unauthorized_request = "{errors.unauthorized_request}";

    /** The key of the message: Failed to print a thread dump. */
    public static final String ERRORS_failed_to_print_thread_dump = "{errors.failed_to_print_thread_dump}";

    /** The key of the message: {0} is not supported. */