<system>jira</system>
    <url>https://issues.apache.org/jira/browse/MNG</url>
  </issueManagement>
  <ciManagement>
    <system>Jenkins</system>
    <url>https://ci-maven.apache.org/job/Maven/job/maven-box/job/maven/</url>
  </ciManagement>
  <distributionManagement>
    <site>
      <id>apache.website</id>
      <url>scm:svn:https://svn.apache.org/repos/asf/maven/website/components/${maven.site.path}</url>
    </site>

  Until kubeadm supports the userns functionality out of the box, users can continue using the deprecated  `RootlessControlPlane` feature gate, or  opt-in `UserNamespacesSupport` by using kubeadm patches on the static pod manifests. ([#124997](https://github.com/kubernetes/kubernetes/pull/124997), [@neolit123](h...

with previously selected boxes.  Bounding boxes with score less than
`score_threshold` are removed.  Bounding boxes are supplied as
[y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any
diagonal pair of box corners and the coordinates can be provided as normalized
(i.e., lying in the interval [0, 1]) or absolute.  Note that this algorithm
is agnostic to where the origin is in the coordinate system and more

	if len(mfiles) > 0 {
		cgoLDFLAGS = append(cgoLDFLAGS, "-lobjc")
	}

	// Likewise for Fortran, except there are many Fortran compilers.
	// Support gfortran out of the box and let others pass the correct link options
	// via CGO_LDFLAGS
	if len(ffiles) > 0 {
		fc := cfg.Getenv("FC")
		if fc == "" {
			fc = "gfortran"
		}
		if strings.Contains(fc, "gfortran") {

  fi
}

# SSH to a node by name ($1) and run a command ($2).
function ssh-to-node() {
  local node="$1"
  local cmd="$2"
  # Loop until we can successfully ssh into the box
  for (( i=0; i<5; i++)); do
    if gcloud compute ssh --ssh-flag='-o LogLevel=quiet' --ssh-flag='-o ConnectTimeout=30' --project "${PROJECT}" --zone="${ZONE}" "${node}" --command 'echo test > /dev/null'; then
      break

    [`tf.distribute.Strategy`](https://www.tensorflow.org/beta/guide/distribute_strategy)
    API to distribute training with minimal code changes, yielding great
    out-of-the-box performance. It supports distributed training with Keras
    model.fit, as well as with custom training loops. Multi-GPU support is
    available, along with experimental support for multi worker and Cloud TPUs.
    Check out the