[]string{"fields", "should", "serial_number", "expires", "valid", "type", "proxy"},
				secretDiffColumns...),
			unexpected: []string{"alligator", "certdata"},
		},
		{
			name:   "test JSON output with a single secret diff",
			format: JSON,
			diffs: []SecretItemDiff{
				{
					Agent: "alligator",
					Proxy: "proxy",
					SecretItem: SecretItem{
						Name:        "fields",
						Data:        "certdata",

  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(

  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.

// 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.

  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) {

  TF_DeleteStatus(s);
}

void Deallocator(void* data, size_t, void* arg) {
  tensorflow::cpu_allocator()->DeallocateRaw(data);
  *reinterpret_cast<bool*>(arg) = true;
}

TEST(CAPI, Tensor) {
  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(
          TF_TensorDefaultAlignment(), num_bytes));
  int64_t dims[] = {2, 3};

  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

- 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))...

/// for the URI schemes that the plugin requests.
///
/// All pointers defined in this structure point to memory allocated by the DSO
/// using an allocator provided by core TensorFlow when calling `TF_InitPlugin`.
///
/// IMPORTANT: To maintain binary compatibility, the layout of this structure
/// must not change! In the unlikely case that a new type of file needs to be