* 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

          // 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

     * 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

    // Ensure that referenceMap, which doesn't use WithExpectedSize, ends up with the same table
    // size as the other two maps. If it ended up with a smaller size that would imply that we
    // computed the wrong initial capacity.
    for (int i = 0; i < size; i++) {
      referenceMap.put(i, null);
    }
    assertWithMessage("table size after adding %s elements", size)
        .that(initialBuckets)

                } 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 {

	}
	return hex.EncodeToString(md5sumCurr)
}

// WithEncryption sets up encrypted reader and the sealing for content md5sum
// using objEncKey. Unsealed md5sum is computed from the rawReader setup when
// NewPutObjReader was called. It returns an error if called on an uninitialized
// PutObjReader.

		p.errorf("expected g or R0 through R15; found %s", name)
		return 0
	}
	return uint16(reg)
}

// Note: There are two changes in the expression handling here
// compared to the old yacc/C implementations. Neither has
// much practical consequence because the expressions we
// see in assembly code are simple, but for the record:
//
// 1) Evaluation uses uint64; the old one used int64.

- Support for Indexed Job: a Job that is considered completed when Pods associated to indexes from 0 to (.spec.completions-1) have succeeded. ([#98812](https://github.com/kubernetes/kubernetes/pull/98812), [@alculquicondor](https://github.com/alculquicondor)) [SIG Apps and CLI]

-comment-dollar:before{content:"\f651"}.fa-comment-dots:before{content:"\f4ad"}.fa-comment-medical:before{content:"\f7f5"}.fa-comment-slash:before{content:"\f4b3"}.fa-comments:before{content:"\f086"}.fa-comments-dollar:before{content:"\f653"}.fa-compact-disc:before{content:"\f51f"}.fa-compass:before{content:"\f14e"}.fa-compress:before{content:"\f066"}.fa-compress-alt:before{content:"\f422"}.fa-compress-arrows-alt:before{content:"\f78c"}.fa-concierge-bell:before{content:"\f562"}.fa-confluence:bef...

    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