That "wait for something else" normally refers to <abbr title="Input and Output">I/O</abbr> operations that are relatively "slow" (compared to the speed of the processor and the RAM memory), like waiting for:

* the data from the client to be sent through the network
* the data sent by your program to be received by the client through the network

        return getEffectiveProperties(null);
    }

    /**
     * Each invocation computes a new map of effective properties. To be used in interpolation.
     * <p>
     * Effective properties are computed from system, user and optionally project properties, layered with
     * defined precedence onto each other to achieve proper precedence. Precedence is defined as:
     * <ul>

func (p *xlStorageDiskIDCheck) monitorDiskWritable(ctx context.Context) {
	var (
		// We check every 15 seconds if the disk is writable and we can read back.
		checkEvery = 15 * time.Second

		// If the disk has completed an operation successfully within last 5 seconds, don't check it.
		skipIfSuccessBefore = 5 * time.Second
	)

	// if disk max timeout is smaller than checkEvery window
	// reduce checks by a second.

 * form}), the memory requirement is 8*N bytes for the copy of the dataset plus an overhead which is
 * independent of N (but depends on the quantiles being computed). When calling {@link
 * ScaleAndIndex#computeInPlace computeInPlace} (in {@linkplain ScaleAndIndexes#computeInPlace
 * either form}), only the overhead is required. The number of object allocations is independent of

Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).

This way, containers consume **little resources**, an amount comparable to running the processes directly (a virtual machine would consume much more).

     * than two.)
     *
     * Read operations can thus proceed without locking, but rely on selected uses of volatiles to
     * ensure that completed write operations performed by other threads are noticed. For most
     * purposes, the "count" field, tracking the number of elements, serves as that volatile

    on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for
    NVIDIA Ampere based GPUs and is enabled by default.

*   A major refactoring of the internals of the Keras Functional API has been
    completed, that should improve the reliability, stability, and performance
    of constructing Functional models.

*   Keras mixed precision API

          // cancelled
          assertTrue(setFuture.isCancelled());
          assertTrue(setFuture.wasInterrupted()); // we only call cancel(true)
        }
      } else {
        // set on the future completed
        assertFalse(cancellationSuccess.get());
        assertTrue(setFutureSetSuccess.get());
        assertTrue(setFutureCompletionSuccess.get());
      }
      // reset for next iteration

                } catch (Exception se) {
                    log.debug("Session setup failed", se);
                    if (this.connectionState.compareAndSet(1, 0)) {
                        // only try to logoff if we have not completed the session setup, ignore errors from chained
                        // responses
                        logoff(true, true);
                    }
                    throw se;
                } finally {

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