- Fixed data race in `kube-scheduler` when preemption races with a Pod update. ([#116395](https://github.com/kubernetes/kubernetes/pull/116395), [@alculquicondor](https://github.com/alculquicondor)) [SIG Scheduling]

  Before this PR, if PreFilter return `Unschedulable`, it may result in an unexpected abortion in the preemption, 
  which shouldn't happen in the default scheduler, but may happen in schedulers with a custom plugin. ([#119779](https://github.com/kubernetes/kubernetes/pull/119779), [@sanposhiho](https://github.com/sanposhiho)) [SIG Scheduling]

- Fix a bug that when PodTopologySpread rejects Pods, they may be stuck in Pending state for 5 min in a worst case scenario.
  The same problem could happen with custom plugins which have Pod/Add or Pod/Update in EventsToRegister,

prefixed version if needed\n  const prefixedProperty = getSupportedPropertyName('transform');\n\n  // now, let's make a step back and look at this code closely (wtf?)\n  // If the content of the popper grows once it's been positioned, it\n  // may happen that the popper gets misplaced because of the new content\n  // overflowing its reference element\n  // To avoid this problem, we provide two options (x and y), which allow\n  // the consumer to define the offset origin.\n  // If we position a popper...

    assertContent("This connection won't pool properly", response1)
    assertThat(server.takeRequest().exchangeIndex).isEqualTo(0)

    // Give the server time to enact the socket policy if it's one that could happen after the
    // client has received the response.
    Thread.sleep(500)
    val response2 = getResponse(newRequest("/b"))
    response1.body
      .source()
      .timeout()

- DRA: The scheduler plugin prevented abnormal filter runtimes by timing out after 10 seconds. This was configurable...

  * Note that NumPy's type promotion rules have been changed(See [NEP 50](https://numpy.org/neps/nep-0050-scalar-promotion.html#nep50)for details). This may change the precision at which computations happen, leading either to type errors or to numerical changes to results.
  * Tensorflow will continue to support NumPy 1.26 until 2025, aligning with community standard deprecation timeline [here](https://scientific-python.org/specs/spec-0000/).

	}

	// Append
	DB.Model(&users).Association("Company").Append(
		&Company{Name: "company-slice-append-1"},
		&Company{Name: "company-slice-append-2"},
		&Company{Name: "company-slice-append-3"},
	)

	AssertAssociationCount(t, users, "Company", 3, "After Append")

	DB.Model(&users).Association("Manager").Append(
		GetUser("manager-slice-belongs-to-1", Config{}),

s3 := append(s2, s0...)            // append a slice              s3 is []int{0, 0, 2, 3, 5, 7, 0, 0}
s4 := append(s3[3:6], s3[2:]...)   // append overlapping slice    s4 is []int{3, 5, 7, 2, 3, 5, 7, 0, 0}

var t []interface{}
t = append(t, 42, 3.1415, "foo")   //                             t is []interface{}{42, 3.1415, "foo"}

var b []byte

			PoolIndex: disks[0].PoolIndex,
		}
		for _, disk := range disks {
			ss.Disks = append(ss.Disks, disk)
			if disk.Healing {
				ss.HealStatus = "Healing"
				ss.HealPriority = "high"
				status.HealDisks = append(status.HealDisks, disk.Endpoint)
			}
		}
		sortDisks(ss.Disks)
		status.Sets = append(status.Sets, ss)
	}
	sort.Slice(status.Sets, func(i, j int) bool {