pkg crypto/mlkem, const CiphertextSize1024 = 1568 #70122
pkg crypto/mlkem, const CiphertextSize1024 ideal-int #70122
pkg crypto/mlkem, const CiphertextSize768 = 1088 #70122
pkg crypto/mlkem, const CiphertextSize768 ideal-int #70122
pkg crypto/mlkem, const EncapsulationKeySize1024 = 1568 #70122
pkg crypto/mlkem, const EncapsulationKeySize1024 ideal-int #70122
pkg crypto/mlkem, const EncapsulationKeySize768 = 1184 #70122

  }

  tensorflow::unwrap(op)->Release();
}

const char* TFE_OpGetName(const TFE_Op* op, TF_Status* status) {
  return tensorflow::unwrap(op)->Name().c_str();
}

TFE_Context* TFE_OpGetContext(const TFE_Op* op, TF_Status* status) {
  return tensorflow::wrap(tensorflow::unwrap(op)->GetContext());
}

void TFE_OpSetDevice(TFE_Op* op, const char* device_name, TF_Status* status) {

pkg crypto/x509, method (KeyUsage) String() string #56866
pkg debug/elf, const R_LARCH_CALL36 = 110 #75562
pkg debug/elf, const R_LARCH_CALL36 R_LARCH #75562
pkg debug/elf, const R_LARCH_TLS_DESC32 = 13 #75562
pkg debug/elf, const R_LARCH_TLS_DESC32 R_LARCH #75562
pkg debug/elf, const R_LARCH_TLS_DESC64 = 14 #75562
pkg debug/elf, const R_LARCH_TLS_DESC64 R_LARCH #75562
pkg debug/elf, const R_LARCH_TLS_DESC64_HI12 = 118 #75562

namespace tensorflow {
namespace {

TFE_MonitoringCounter0* CreateCounter0(const char* counter_name);
TFE_MonitoringCounter1* CreateCounter1(const char* counter_name,
                                       const char* label);
void IncrementCounter0(TFE_MonitoringCounter0* counter, int64_t delta = 1);
void IncrementCounter1(TFE_MonitoringCounter1* counter, const char* label,
                       int64_t delta = 1);

                      device_name, "\")");
}

absl::Status ImmediateExecutionTensorHandle::SummarizeValue(
    std::string& summary) const {
  absl::Status status;
  AbstractTensorPtr resolved(
      // TODO(allenl): Resolve should be const, and the caches that get updated
      // marked mutable.
      const_cast<ImmediateExecutionTensorHandle*>(this)->Resolve(&status));
  if (!status.ok()) {
    return status;

import jetbrains.buildServer.configs.kotlin.Project
import model.CIBuildModel
import model.FunctionalTestBucketProvider
import model.Stage
import model.TestCoverage

const val DEFAULT_FUNCTIONAL_TEST_BUCKET_SIZE = 50
const val DEFAULT_LINUX_FUNCTIONAL_TEST_BUCKET_SIZE = 20
const val DEFAULT_MACOS_FUNCTIONAL_TEST_BUCKET_SIZE = 20

class FunctionalTestProject(
    val model: CIBuildModel,

import okio.utf8Size

/**
 * Trivial Dns Encoder/Decoder, basically ripped from Netty full implementation.
 */
internal object DnsRecordCodec {
  private const val SERVFAIL = 2
  private const val NXDOMAIN = 3
  const val TYPE_A = 0x0001
  const val TYPE_AAAA = 0x001c
  private const val TYPE_PTR = 0x000c
  private val ASCII = Charsets.US_ASCII

  fun encodeQuery(
    host: String,
    type: Int,
  ): ByteString =
    Buffer()

this._fixHeight()\n    this._fixScrollHeight()\n\n    $(this._element).trigger($.Event(EVENT_EXPANDED))\n  }\n\n  toggle() {\n    const $body = $('body')\n    const { target } = this._config\n\n    const notVisible = !$(target).is(':visible')\n    const shouldClose = ($body.hasClass(CLASS_NAME_CONTROL_SIDEBAR_OPEN) ||\n      $body.hasClass(CLASS_NAME_CONTROL_SIDEBAR_SLIDE))\n    const shouldToggle = notVisible && ($body.hasClass(CLASS_NAME_CONTROL_SIDEBAR_OPEN) ||\n      $body.hasClass(CLASS_NAME_CONTROL...

                       subdiv_offsets.size());

  return op;
}

TFE_Op* SendOp(TFE_Context* ctx, TFE_TensorHandle* in,
               const std::string& op_name, const std::string& send_device,
               const std::string& recv_device,
               tensorflow::uint64 send_device_incarnation) {
  TF_Status* status = TF_NewStatus();
  TFE_Op* op = TFE_NewOp(ctx, op_name.c_str(), status);

class CheckpointReader {
 public:
  CheckpointReader(const std::string& filename, TF_Status* status);

  bool HasTensor(const std::string& name) const;
  const std::string DebugString() const;

  // Returns a map from variable names to their shapes.  Slices of a partitioned
  // tensor are combined into a single entry.
  const TensorSliceReader::VarToShapeMap& GetVariableToShapeMap() const;