return new PJRT_Buffer{std::move(*buffer), c_api_client->pjrt_c_client()};
}

TEST(TensorPjRtBufferUtilTest, GetPjRtCBufferFromTensorNoBuffer) {
  auto allocator = std::make_unique<AsyncValueAllocator>();
  tensorflow::Tensor tensor(allocator.get(), DT_FLOAT, {1});

  EXPECT_THAT(
      GetPjRtCBufferFromTensor(&tensor),
      StatusIs(error::INTERNAL, HasSubstr(absl::StrCat(

// Node.  A ClusterCIDR is eligible to be used for a given Node when the node
// selector matches the node in question and has free CIDRs to allocate.  In
// case of multiple matching ClusterCIDR resources, the allocator will attempt
// to break ties using internal heuristics, but any ClusterCIDR whose node
// selector matches the Node may be used.
message ClusterCIDR {
  // Standard object's metadata.

  TF_TriState dependency_optimization;
  TF_TriState auto_parallel;
  TF_TriState memory_optimization;
  TF_TriState scoped_allocator_optimization;
} TP_OptimizerConfigs;

#define TP_OPTIMIZER_CONFIGS_STRUCT_SIZE \
  TF_OFFSET_OF_END(TP_OptimizerConfigs, scoped_allocator_optimization)

// Struct for Optimizer. Plugin authors must provide an optimize function.
// Creation and deletion functions are optional.

  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(
          TF_TensorDefaultAlignment(), num_bytes));
  int64_t dims[] = {2, 3};
  bool deallocator_called = false;
  TF_Tensor* t = TF_NewTensor(TF_FLOAT, dims, 2, values, num_bytes,
                              &Deallocator, &deallocator_called);

  TF_Tensor* o = TF_TensorMaybeMove(t);

  auto* rewrite_options =
      config->mutable_graph_options()->mutable_rewrite_options();
  rewrite_options->set_scoped_allocator_optimization(
      tensorflow::RewriterConfig::ON);
  rewrite_options->mutable_scoped_allocator_opts()->add_enable_op(
      "CollectiveReduce");

  if ((tensorflow::IsGoogleCudaEnabled() || tensorflow::IsBuiltWithROCm()) &&
      num_virtual_gpus > 0) {

  CONFIG_TOGGLE(remapping);
  CONFIG_TOGGLE(loop_optimization);
  CONFIG_TOGGLE(dependency_optimization);
  CONFIG_TOGGLE(auto_parallel);
  CONFIG_TOGGLE(memory_optimization);
  CONFIG_TOGGLE(scoped_allocator_optimization);
  PluginGraphOptimizerRegistry::RegisterPluginOptimizerOrDie(
      creator, device_type, configs);
}

#undef CONFIG_TOGGLE

absl::Status InitGraphPlugin(void* dso_handle) {

///
/// Since memory that is allocated by the DSO gets transferred to core
/// TensorFlow, we need to provide a way for the allocation and deallocation to
/// match. This is why this structure also defines `plugin_memory_allocate` and
/// `plugin_memory_free` members.
///
/// All memory allocated by the plugin that will be owned by core TensorFlow
/// must be allocated using the allocator in this structure. Core TensorFlow

- Migrated `pkg/controller/nodeipam/ipam/cloud_cidr_allocator.go, pkg/controller/nodeipam/ipam/multi_cidr_range_allocator.go pkg/controller/nodeipam/ipam/range_allocator.go pkg/controller/nodelifecycle/node_lifecycle_controller.go` to structured logging ([#112670](https://github.com/kubernetes/kubernetes/pull/112670), [@yangjunmyfm192085](https://github.com/yangjunmyfm192085))...

  approximately 1% by deduplicating redundant metadata.
  Some programs may see a smaller improvement because this change adjusts the size
  class boundaries of the memory allocator, so some objects may be moved up a size
  class.
</p>
<p>
  A consequence of this change is that some objects' addresses that were previously

// until any memory allocated from it is no longer needed.
//
// An Arena must never be used concurrently by multiple goroutines.
type Arena struct {
	a unsafe.Pointer
}

// NewArena allocates a new arena.
func NewArena() *Arena {
	return &Arena{a: runtime_arena_newArena()}
}

// Free frees the arena (and all objects allocated from the arena) so that