&& queue.removeLastOccurrence(submittedTask);
        // If the delegate is directExecutor(), the submitted runnable could have thrown a REE. But
        // that's handled by the log check that catches RuntimeExceptions in the queue worker.
        if (!(t instanceof RejectedExecutionException) || removed) {
          throw t;
        }
      }
      return;
    }

    /*

   * #transform}.
   *
   * @param exceptionType the exception type that triggers use of {@code fallback}. The exception
   *     type is matched against the input's exception. "The input's exception" means the cause of
   *     the {@link ExecutionException} thrown by {@code input.get()} or, if {@code get()} throws a

     */
    public int getPointer() {
        return this.pointer;
    }

    /**
     * Validates the provided string against this structure's metadata.
     * Checks that the string length matches the expected length and that
     * null strings have a zero pointer.
     *
     * @param string the string to validate
     * @return the validated string
     * @throws PACDecodingException if validation fails
     */

<%@page pageEncoding="UTF-8" contentType="text/html; charset=UTF-8"%>
<%-- query matched some document --%>
<div id="subheader" class="row">
	<div class="col">
		<p>
			<c:if test="${allRecordCountRelation=='EQUAL_TO'}">
				<la:message key="labels.search_result_status"
					arg0="${displayQuery}" arg1="${fe:formatNumber(allRecordCount,'###,###')}"
					arg2="${f:h(currentStartRecordNumber)}"
					arg3="${f:h(currentEndRecordNumber)}" />

     * @return file representing the tool executable, or null if the tool cannot be found
     */
    String findTool(String toolName);

    /**
     * Let the toolchain decide if it matches requirements defined
     * in the toolchain plugin configuration.
     *
     * @param requirements key value pair, may not be {@code null}
     * @return {@code true} if the requirements match, otherwise {@code false}

potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.

It is important to keep TensorFlow up to date with the latest security patches
and follow the sandboxing guideline above to protect against these types of
vulnerabilities.

## Security properties of execution modes

TensorFlow has several execution modes, with Eager-mode being the default in v2.

import org.apache.maven.api.services.Result;

/**
 * Interface for caching request results in Maven. This cache implementation provides
 * methods for executing and optionally caching both single requests and batches of requests.
 * <p>
 * The cache behavior is determined by the cache retention specified in the request's metadata.
 * Results can be cached at different policies (forever, session, request, or not at all)

   * @since 14.0
   */
  @GwtIncompatible // regular expressions
  public static @Nullable Float tryParse(String string) {
    if (Doubles.FLOATING_POINT_PATTERN.matcher(string).matches()) {
      // TODO(lowasser): could be potentially optimized, but only with
      // extensive testing
      try {
        return Float.parseFloat(string);
      } catch (NumberFormatException e) {

    }

  fun assertFailureMatches(vararg patterns: String) =
    apply {
      val message = failure!!.message!!
      assertThat(
        patterns.firstOrNull { pattern ->
          message.matches(pattern.toRegex())
        },
      ).isNotNull()
    }

  fun assertSentRequestAtMillis(
    minimum: Long,
    maximum: Long,
  ) = apply {

		return db.CreateInBatches(value, db.CreateBatchSize)
	}

	tx = db.getInstance()
	tx.Statement.Dest = value
	return tx.callbacks.Create().Execute(tx)
}

// CreateInBatches inserts value in batches of batchSize
func (db *DB) CreateInBatches(value interface{}, batchSize int) (tx *DB) {
	reflectValue := reflect.Indirect(reflect.ValueOf(value))

	switch reflectValue.Kind() {
	case reflect.Slice, reflect.Array: