val server = serverListener.assertOpen()

    // Initiate a close on the client, which will schedule a hard cancel in 500 ms.
    val closeAtNanos = System.nanoTime()
    webSocket.close(1000, "goodbye", 500L)
    serverListener.assertClosing(1000, "goodbye")

    // Confirm that the hard cancel occurred after 500 ms.
    clientListener.assertFailure()
    val elapsedUntilFailure = System.nanoTime() - closeAtNanos

Then, when it's your turn, you do actual "productive" work, you process the menu, decide what you want, get your crush's choice, pay, check that you give the correct bill or card, check that you are charged correctly, check that the order has the correct items, etc.

                        }
                    }
                }
            }
        }
    }
}

// run the parallel ITs
parallel(runITsTasks)

// JENKINS-34376 seems to make it hard to detect the aborted builds
} catch (org.jenkinsci.plugins.workflow.steps.FlowInterruptedException e) {
    echo "[FAILURE-002] FlowInterruptedException ${e}"
    // this ambiguous condition means a user probably aborted

  // For example, a user may request that a pod is deleted in 30 seconds. The Kubelet will react
  // by sending a graceful termination signal to the containers in the pod. After that 30 seconds,
  // the Kubelet will send a hard termination signal (SIGKILL) to the container and after cleanup,
  // remove the pod from the API. In the presence of network partitions, this object may still

*   Add `UnifiedGRU` as the new GRU implementation for tf2.0. Change the default
    recurrent activation function for GRU from `hard_sigmoid` to `sigmoid`, and
    `reset_after` to True in 2.0. Historically recurrent activation is
    `hard_sigmoid` since it is fast than 'sigmoid'. With new unified backend
    between CPU and GPU mode, since the CuDNN kernel is using sigmoid, we change

* `fixedquery`: has the exact value `fixedquery`.
* `$`: ends there, doesn't have any more characters after `fixedquery`.

If you feel lost with all these **"regular expression"** ideas, don't worry. They are a hard topic for many people. You can still do a lot of stuff without needing regular expressions yet.

But whenever you need them and go and learn them, know that you can already use them directly in **FastAPI**.

We use *Specification* and *Discovery* documents stored in Google Drive, but they present some downsides:

* They are rarely updated after creation and initial review, and then become hard to follow, especially after important decisions are made
* They are not synced with the code to reflect the eventual solution that is committed
* Google Docs is not a "code-oriented" tool, like asciidoc can be

                        }
                    }
                }
            }
        }
    }
}

// run the parallel ITs
parallel(runITsTasks)

// JENKINS-34376 seems to make it hard to detect the aborted builds
} catch (org.jenkinsci.plugins.workflow.steps.FlowInterruptedException e) {
    echo "[FAILURE-002] FlowInterruptedException ${e}"
    // this ambiguous condition means a user probably aborted

* 🌐 Add French translation for FastAPI People. PR [#2232](https://github.com/tiangolo/fastapi/pull/2232) by [@JulianMaurin](https://github.com/JulianMaurin).
* 🌐 Add Korean translation for Tutorial - Path Parameters. PR [#2355](https://github.com/tiangolo/fastapi/pull/2355) by [@hard-coders](https://github.com/hard-coders).

**available machines** taking into account the amount of memory needed by them, and the amount available in the machines in the cluster.

If your application is **simple**, this will probably **not be a problem**, and you might not need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines...