}

    override fun close() {
      if (closed) {
        throw AssertionError("Already closed")
      }
      try {
        source.close()
      } catch (ignored: IOException) {
      }
      try {
        sink.close()
      } catch (ignored: IOException) {
      }
      closed = true
    }

    override fun cancel() {
      canceled = true
      sourcePipe.cancel()

        else -> {
          coordinatorWaiting = true
          coordinatorWakeUpAt = now + minDelayNanos
          try {
            backend.coordinatorWait(this@TaskRunner, minDelayNanos)
          } catch (_: InterruptedException) {
            // Will cause all tasks to exit unless more are scheduled!
            cancelAll()
          } finally {
            coordinatorWaiting = false
          }
        }

            token,
        )
    }
    add(FirErrors.CATCH_PARAMETER_WITH_DEFAULT_VALUE) { firDiagnostic ->
        CatchParameterWithDefaultValueImpl(
            firDiagnostic as KtPsiDiagnostic,
            token,
        )
    }
    add(FirErrors.REIFIED_TYPE_IN_CATCH_CLAUSE) { firDiagnostic ->
        ReifiedTypeInCatchClauseImpl(

CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

================================================================

github.com/tidwall/match
https://github.com/tidwall/match
----------------------------------------------------------------
The MIT License (MIT)

Copyright (c) 2016 Josh Baker

Let's put that data in the Pydantic `UserInDB` model first.

You should never save plaintext passwords, so, we'll use the (fake) password hashing system.

If the passwords don't match, we return the same error.

#### Password hashing

"Hashing" means: converting some content (a password in this case) into a sequence of bytes (just a string) that looks like gibberish.

    )
    server.enqueue(MockResponse(body = "ABC"))
    val latch = CountDownLatch(1)
    val responses: BlockingQueue<String?> = SynchronousQueue()
    val authenticator =
      okhttp3.Authenticator { route: Route?, response: Response? ->
        responses.offer(response!!.body.string())
        try {
          latch.await()
        } catch (e: InterruptedException) {
          throw AssertionError()
        }

That, plus the simple fact that Python is the main language for **Data Science**, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).

To see how to achieve this parallelism in production see the section about [Deployment](deployment/index.md){.internal-link target=_blank}.

            else -> realPsi as? KtExpression
        }
    }

    private inline fun <R> wrapError(element: KtElement, action: () -> R): R {
        return try {
            action()
        } catch (e: Exception) {
            rethrowExceptionWithDetails(
                "Error during resolving call ${element::class}",
                exception = e,
            ) {

            :warning: This PR creates new top-level Java classes, but has no Javadoc present. Please document all top-level classes with Javadoc.

is:
  java: {{ files | extensions | match(term='java') | every }}
  new: {{ source.diff.files | map(attr='original_file') | match(regex=r/^$/) | some }}

colors:

iptables -t nat -N ISTIO_POSTRT
iptables -t nat -A POSTROUTING -j ISTIO_POSTRT