}

// getKubeletMappings returns the range of IDs that can be used to configure user namespaces.
// If subordinate user or group ID ranges are specified for the kubelet user and the getsubids tool
// is installed, then the single mapping specified both for user and group IDs will be used.
// If the tool is not installed, or there are no IDs configured, the default mapping is returned.

import gradlebuild.basics.isBundleGroovy4
import gradlebuild.basics.tasks.PackageListGenerator

plugins {
    id("gradlebuild.distribution.implementation-java")
}

description = "A library that aids in testing Gradle plugins and build logic in general"

errorprone {
    disabledChecks.addAll(
        "CatchAndPrintStackTrace", // 1 occurrences
        "ImmutableEnumChecker", // 1 occurrences
    )
}

dependencies {

	//
	// cluster/group/kind/namespace/name/section-name
	//
	// As an example, a ServiceEntry with two WorkloadEntries inlined could become
	// two Workloads with the following UIDs:
	// - cluster1/networking.istio.io/v1alpha3/ServiceEntry/default/external-svc/endpoint1
	// - cluster1/networking.istio.io/v1alpha3/ServiceEntry/default/external-svc/endpoint2
	//

  # Ensure TasksMax is sufficient for docker.
  # (https://github.com/kubernetes/kubernetes/issues/51977)
  echo "Extend the docker.service configuration to set a higher pids limit"
  mkdir -p /etc/systemd/system/docker.service.d
  cat <<EOF >/etc/systemd/system/docker.service.d/01tasksmax.conf
[Service]
TasksMax=infinity
EOF

    systemctl daemon-reload

embedding_indices and aggregation_weights into rows.
It corresponds to ids.row_splits in embedding_lookup(), when ids is a
RaggedTensor.}]>:$sample_splits,
    Arg<Variadic<TF_I32OrI64Tensor>, [{A list of rank 1 Tensors, indices into the embedding tables.
It corresponds to ids.values in embedding_lookup(), when ids is a RaggedTensor.}]>:$embedding_indices,

    *   Benchmark for tf.scan in graph and eager modes.
    *   Added complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D.
    *   Making ids unique in `nn.embedding_lookup_sparse`. This helps to reduce
        RPC calls for looking up the embeddings when there are repeated ids in
        the batch.
    *   Support indicator column in boosted trees.
    *   Prevent `tf.gradients()` from backpropagating through integer tensors.