type: KClass<T>,
  compute: () -> T,
): T {
  var computed: T? = null

  while (true) {
    val tags = get()

    // If the element is already present. Return it.
    val existing = tags[type]
    if (existing != null) return existing

    if (computed == null) {
      computed = compute()
    }

    // If we successfully add the computed element, we're done.

  TF_Status* s = TF_NewStatus();
  TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s);
  TF_DeleteStatus(s);
  return NULL;
}

// A compute function. This will never actually get called in this test, it's
// just nice to know that it compiles.
void compute(void* kernel, TF_OpKernelContext* ctx) {
  TF_Tensor* input;
  TF_Status* s = TF_NewStatus();
  TF_GetInput(ctx, 0, &input, s);
  TF_DeleteTensor(input);

export DEBIAN_FRONTEND=noninteractive

# Fetch the NVIDIA key.
apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub;

# Set up sources for NVIDIA CUDNN.
cat >/etc/apt/sources.list.d/nvidia.list <<SOURCES
# NVIDIA
deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /

            print(
                'ERROR: TensorFlow only supports small CUDA compute'
                ' capabilities of sm_30 and higher. Please re-specify the list'
                ' of compute capabilities excluding version %s.' % ver)
            all_valid = False
          if ver < 35:
            print('WARNING: XLA does not support CUDA compute capabilities '

     */
    @Nullable
    <T> T get(@Nonnull Key<T> key);

    /**
     * Retrieve of compute the data associated with the specified key.
     *
     * @param key the key for which to retrieve the session data, must not be {@code null}
     * @param supplier the supplier will compute the new value
     * @return the session data associated with the key
     */
    @Nullable

// Copyright 2009 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

//go:build ignore

// Compute Fibonacci numbers with two goroutines
// that pass integers back and forth.  No actual
// concurrency, just threads and synchronization
// and foreign code on multiple pthreads.

package main

import (
	big "."
	"runtime"
)

}

includeBuild("../build-logic-settings")

// Shared basics for all
include("basics")

// Platform: defines shared dependency versions
include("build-platform")

// Compute the identity/version we are building and related details (like current git commit)
include("module-identity")

// Code quality rules common to :build-logic and the root build
include("code-quality-rules")

    assertEquals(0x388ee898bad75cbfL, hasher.hash().asLong());
  }

  /** Convenience method to compute a fingerprint on a full bytes array. */
  private static long fingerprint(byte[] bytes) {
    return fingerprint(bytes, bytes.length);
  }

  /** Convenience method to compute a fingerprint on a subset of a byte array. */
  private static long fingerprint(byte[] bytes, int length) {

   *
   * @since 21.0
   */
  @CanIgnoreReturnValue
  public long updateAndGet(K key, LongUnaryOperator updaterFunction) {
    checkNotNull(updaterFunction);
    Long result =
        map.compute(
            key,
            (K k, @Nullable Long value) ->
                updaterFunction.applyAsLong(value == null ? 0L : value.longValue()));
    return requireNonNull(result);
  }

  /**

Kubernetes manages stateless Spark and Hive containers elastically on the compute nodes. Spark has native scheduler integration with Kubernetes. Hive, for legacy reasons, uses YARN scheduler on top of Kubernetes.