labels.file_auth_scheme_samba=Samba
labels.file_auth_scheme_ftp=FTP
labels.pagination_page_guide_msg={0}/{1} ({2} elementi)
labels.list_could_not_find_crud_table=Non registrato.
labels.scheduledjob_configuration=Scheduler lavori
labels.scheduledjob_title_details=Lavoro
labels.scheduledjob_name=Nome
labels.scheduledjob_target=Destinazione
labels.scheduledjob_status=Stato
labels.scheduledjob_cronExpression=Pianificazione

name: OSV-Scanner Scheduled Scan

on:
  schedule:
    - cron: 0 4 * * 1

permissions:
  # Require writing security events to upload SARIF file to security tab
  security-events: write
  # Only need to read contents
  contents: read

jobs:
  scan-scheduled:
    if: github.repository == 'tensorflow/tensorflow'

      object : Task("task one") {
        val schedules = mutableListOf(200.µs)

        override fun runOnce(): Long {
          if (schedules.isNotEmpty()) {
            redQueue.schedule(this, schedules.removeAt(0)) // Add it at the end also.
          }
          log += "scheduledTasks=${redQueue.scheduledTasks}"
          return -1L
        }
      }
    redQueue.schedule(task, 100.µs)

    // If the task is already scheduled, take the earlier of the two times.
    val existingIndex = futureTasks.indexOf(task)
    if (existingIndex != -1) {
      if (task.nextExecuteNanoTime <= executeNanoTime) {
        taskRunner.logger.taskLog(task, this) { "already scheduled" }
        return false
      }
      futureTasks.removeAt(existingIndex) // Already scheduled later: reschedule below!
    }

 * ----------
 *
 * Tasks control their recurrence schedule. The [runOnce] function returns -1L to signify that the
 * task should not be executed again. Otherwise it returns a delay until the next execution.
 *
 * A task has at most one next execution. If the same task instance is scheduled multiple times, the
 * earliest one wins. This applies to both executions scheduled with [TaskRunner.Queue.schedule] and
 * those implied by the returned execution delay.

*   Added warmup capabilities to `tf.keras.optimizers.schedules.CosineDecay` learning rate scheduler. You can now specify an initial and target learning rate, and our scheduler will perform a linear interpolation between the two after which it will begin a decay phase.

    return new NoOpScheduledExecutorService();
  }

  /**
   * Creates a scheduled executor service that runs each task in the thread that invokes {@code
   * execute/submit/schedule}, as in {@link CallerRunsPolicy}. This applies both to individually
   * submitted tasks and to collections of tasks submitted via {@code invokeAll}, {@code invokeAny},
   * {@code schedule}, {@code scheduleAtFixedRate}, and {@code scheduleWithFixedDelay}. In the case

    return new NoOpScheduledExecutorService();
  }

  /**
   * Creates a scheduled executor service that runs each task in the thread that invokes {@code
   * execute/submit/schedule}, as in {@link CallerRunsPolicy}. This applies both to individually
   * submitted tasks and to collections of tasks submitted via {@code invokeAll}, {@code invokeAny},
   * {@code schedule}, {@code scheduleAtFixedRate}, and {@code scheduleWithFixedDelay}. In the case

      - name: Upload pip wheel to PyPI
        if: github.event_name == 'schedule' || (github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v2')) # only if it is a scheduled nightly or tagged
        shell: bash

   * addListener/SES.schedule. The later racy write in cancel() is not guaranteed to be observed,
   * however that is fine since the correctness is based on the atomic state in our base class. The
   * initial write to timer is never definitely visible to Fire.run since it is assigned after
   * SES.schedule is called. Therefore Fire.run has to check for null. However, it should be visible