o1 &^= 0x3ffff
		o2 &^= 0x0ffff
		o1 |= computePrefix34HI(t)
		o2 |= computeLO(int32(t))
	case objabi.R_ADDRPOWER_D34:
		o1 &^= 0x3ffff
		o2 &^= 0x0ffff
		o1 |= computePrefix34HI(t)
		o2 |= computeLO(int32(t))
	case objabi.R_ADDRPOWER:
		o1 &^= 0xffff
		o2 &^= 0xffff
		o1 |= computeHA(int32(t))
		o2 |= computeLO(int32(t))
	case objabi.R_ADDRPOWER_DS:
		o1 &^= 0xffff
		o2 &^= 0xfffc

	//
	// At a high level, this value is computed as the bytes of memory
	// allocated (cons) per unit of scan work completed (mark) in a GC
	// cycle, divided by the CPU time spent on each activity.
	//
	// Updated at the end of each GC cycle, in endCycle.
	consMark float64

	// lastConsMark is the computed cons/mark value for the previous 4 GC

				// We have already computed the scope for this namespace, just return it.
				return sc
			}
			// We need to compute this namespace
			computed := convertToSidecarScope(ps, ps.sidecarIndex.meshRootSidecarConfig, proxy.ConfigNamespace)
			ps.sidecarIndex.meshRootSidecarsByNamespace[proxy.ConfigNamespace] = computed
			return computed
		}

	if !deleted {
		a.queue.AddRateLimited(key)
	}

	return true
}

// computeReplicasForMetrics computes the desired number of replicas for the metric specifications listed in the HPA,
// returning the maximum of the computed replica counts, a description of the associated metric, and the statuses of
// all metrics computed.
// It may return both valid metricDesiredReplicas and an error,

	default:
		// Compute nanoseconds as 128-bit number.
		sec := uint64(sec)
		tmp := (sec >> 32) * 1e9
		u1 := tmp >> 32
		u0 := tmp << 32
		tmp = (sec & 0xFFFFFFFF) * 1e9
		u0x, u0 := u0, u0+tmp
		if u0 < u0x {
			u1++
		}
		u0x, u0 = u0, u0+uint64(nsec)
		if u0 < u0x {
			u1++
		}

		// Compute remainder by subtracting r<<k for decreasing k.

                                 log1p_z_div_lanczos_gamma_plus_one_half);

    // Compute the final result (modulo reflection).  t(z) may be large, and we
    // need to be careful not to overflow to infinity in the first term of
    //
    //   (z + 1/2) * log(t(z)) - t(z).
    //
    // Therefore we compute this as
    //
    //   (z + 1/2 - t(z) / log(t(z))) * log(t(z)).
    //

            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 '

  {
    Status status;
    std::unique_ptr<OpKernel> kernel =
        GetFakeKernel(device_name, op_name, node_name, &status);
    TF_EXPECT_OK(status);
    ASSERT_NE(nullptr, kernel.get());
    kernel->Compute(nullptr);
  }

  ASSERT_TRUE(delete_called);
}

TEST(TestKernel, TF_RegisterKernelBuilderWithKernelDef) {
  const char* node_name = "SomeNodeName";
  const char* op_name = "FooOp1";

// Note: Currently we are not fully utilizing this structure. We could invoke this logic
// once for all sidecars in the cluster to compute all RDS for inside the mesh and arrange
// it by listener port. However to properly use such an optimization, we need to have an
// eventing subsystem to invalidate the computed routes if any service changes/virtual Services change.
type VirtualHostWrapper struct {

  int32 targetAverageUtilization = 1;
}

// Compute resources required by a container.
message Resources {
  // The maximum amount of compute resources allowed.
  // More info: https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/
  map<string, string> limits = 1;

  // The minimum amount of compute resources required. If Requests is omitted for a container,