Depending on the value returned from QueueingHintFn, the scheduling queue changes how it queues each Pod:
  - if more than one QueueingHintFn returns QueueImmediately, it queues Pod to activeQ.
  - If no QueueingHintFn returns QueueImmediately and more than one plugin returns QueueAfterBackoff, it queues Pod to backoffQ if Pod is backing off, or to activeQ if Pod's backoff has already finished.

etween","inclusivity","localFrom","localTo","isSame","inputMs","isSameOrAfter","isSameOrBefore","parsingFlags","prioritized","unitsObj","u","getPrioritizedUnits","toArray","toObject","toDate","toISOString","keepOffset","inspect","prefix","suffix","zone","isLocal","Symbol","for","toJSON","unix","creationData","eraName","since","until","eraNarrow","eraAbbr","eraYear","dir","isoWeeks","weekInfo","weeksInWeekYear","isoWeeksInYear","isoWeeksInISOWeekYear","keepLocalTime","keepMinutes","localAdjust","...

- In the event that more than one IngressClass is designated "default", the DefaultIngressClass admission controller will choose one rather than fail. ([#110974](https://github.com/kubernetes/kubernetes/pull/110974), [@kidddddddddddddddddddddd](https://github.com/kidddddddddddddddddddddd)) [SIG Network]

- Admission control plugin "DefaultStorageClass": If more than one StorageClass is designated as default (via the "storageclass.kubernetes.io/is-default-class" annotation), choose the newest one instead of throwing an error. ([#110559](https://github.com/kubernetes/kub...

        of built-in training loops such as `tf.keras` `compile` and `fit`, we
        expect reduction value to be `SUM` or `NONE`. Using `AUTO` in that case
        will raise an error.
    *   `NONE`: Weighted losses with one dimension reduced (axis=-1, or axis
        specified by loss function). When this reduction type used with built-in

repeated in a wondering tone.

  `Why, what are YOUR shoes done with?' said the Gryphon.  `I
mean, what makes them so shiny?'

  Alice looked down at them, and considered a little before she
gave her answer.  `They're done with blacking, I believe.'

  `Boots and shoes under the sea,' the Gryphon went on in a deep
voice, `are done with a whiting.  Now you know.'

repeated in a wondering tone.

  `Why, what are YOUR shoes done with?' said the Gryphon.  `I
mean, what makes them so shiny?'

  Alice looked down at them, and considered a little before she
gave her answer.  `They're done with blacking, I believe.'

  `Boots and shoes under the sea,' the Gryphon went on in a deep
voice, `are done with a whiting.  Now you know.'

    query: str,
    after: str | None = None,
    category_id: str | None = None,
    discussion_number: int | None = None,
    discussion_id: str | None = None,
    comment_id: str | None = None,
    body: str | None = None,
) -> dict[str, Any]:
    headers = {"Authorization": f"token {settings.github_token.get_secret_value()}"}
    variables = {
        "after": after,
        "category_id": category_id,

   * that returns a {@code ClosingFuture} to its value. The function can use a {@link
   * DeferredCloser} to capture objects to be closed when the pipeline is done (other than those
   * captured by the returned {@link ClosingFuture}).
   *
   * <p>If this {@code ClosingFuture} succeeds, the derived one will be equivalent to the one
   * returned by the function.
   *

# This generates a pure python wheel of the format "*-py3-none-any.whl"
bazel run --HERMETIC_PYTHON_VERSION=3.13 //tensorflow/tools/pip_package:setup_py_binary -- bdist_wheel --dist-dir "$TFCI_OUTPUT_DIR"

# Get the name of the pure python wheel that was built. This should
# resolve to either
# 1. tensorflow-a.b.c-py3-none-any.whl  or, for nightly builds,
# 2. tf_nightly-a.b.c.devYYYYMMDD-py3-none-any.whl