pkg debug/pe, const IMAGE_FILE_DEBUG_STRIPPED = 512
pkg debug/pe, const IMAGE_FILE_DEBUG_STRIPPED ideal-int
pkg debug/pe, const IMAGE_FILE_DLL = 8192
pkg debug/pe, const IMAGE_FILE_DLL ideal-int
pkg debug/pe, const IMAGE_FILE_EXECUTABLE_IMAGE = 2
pkg debug/pe, const IMAGE_FILE_EXECUTABLE_IMAGE ideal-int
pkg debug/pe, const IMAGE_FILE_LARGE_ADDRESS_AWARE = 32

  const val INITIAL_MAX_FRAME_SIZE = 0x4000 // 16384

  const val TYPE_DATA = 0x0
  const val TYPE_HEADERS = 0x1
  const val TYPE_PRIORITY = 0x2
  const val TYPE_RST_STREAM = 0x3
  const val TYPE_SETTINGS = 0x4
  const val TYPE_PUSH_PROMISE = 0x5
  const val TYPE_PING = 0x6
  const val TYPE_GOAWAY = 0x7
  const val TYPE_WINDOW_UPDATE = 0x8
  const val TYPE_CONTINUATION = 0x9

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);

// Return a RecvOp op `op_name` with the attributes `send_device`,
// `recv_device`, and `send_device_incarnation` set.
TFE_Op* RecvOp(TFE_Context* ctx, const std::string& op_name,

  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

  virtual absl::Status InputLength(const char* input_name, int* length) = 0;
  virtual absl::Status OutputLength(const char* output_name, int* length) = 0;

  // Set stack trace to be used for potential async error reporting.

  virtual absl::Status SetAttrInt(const char* attr_name, int64_t value) = 0;
  virtual absl::Status SetAttrFloat(const char* attr_name, float value) = 0;
  virtual absl::Status SetAttrBool(const char* attr_name, bool value) = 0;
  virtual absl::Status SetAttrType(const char* attr_name, DataType value) = 0;
  virtual absl::Status SetAttrShape(const char* attr_name, const int64_t* dims,
                                    const int num_dims) = 0;

  }
}

bool CheckpointReader::HasTensor(const string& name) const {
  if (reader_ != nullptr) {
    return reader_->HasTensor(name, nullptr, nullptr);
  }
  return v2_reader_->Contains(name);
}

const TensorSliceReader::VarToShapeMap&
CheckpointReader::GetVariableToShapeMap() const {
  CHECK(var_to_shape_map_);
  return *var_to_shape_map_;
}

const TensorSliceReader::VarToDataTypeMap&

/** `100 Continue` (HTTP/1.1 - RFC 7231)  */
const val HTTP_CONTINUE = 100

/** `101 Switching Protocols` (HTTP/1.1 - RFC 9110)  */
const val HTTP_SWITCHING_PROTOCOLS = 101

/** `102 Processing` (WebDAV - RFC 2518)  */
const val HTTP_PROCESSING = 102

/** `103 Early Hints (Early Hints - RFC 8297)` */
const val HTTP_EARLY_HINTS = 103

/** `307 Temporary Redirect` (HTTP/1.1 - RFC 7231)  */
const val HTTP_TEMP_REDIRECT = 307

                       GradientFunction* gradient_function,
                       const string& op_name = "");
  // Returns whether any tensor in a list of tensors is being watched and has
  // a trainable dtype.
  bool ShouldRecord(
      absl::Span<const AbstractTensorHandle* const> tensors) const;
  // Unwatches this tensor on the tape. Mainly used for cleanup when deleting
  // eager tensors.

                          const char* name = "feed",
                          TF_DataType dtype = TF_INT32,
                          const std::vector<int64_t>& dims = {});

TF_Operation* Const(TF_Tensor* t, TF_Graph* graph, TF_Status* s,
                    const char* name = "const");

TF_Operation* ScalarConst(bool v, TF_Graph* graph, TF_Status* s,
                          const char* name = "scalar");

internal const val USERNAME_ENCODE_SET = " \"':;<=>@[]^`{}|/\\?#"
internal const val PASSWORD_ENCODE_SET = " \"':;<=>@[]^`{}|/\\?#"
internal const val PATH_SEGMENT_ENCODE_SET = " \"<>^`{}|/\\?#"
internal const val PATH_SEGMENT_ENCODE_SET_URI = "[]"
internal const val QUERY_ENCODE_SET = " \"'<>#"
internal const val QUERY_COMPONENT_REENCODE_SET = " \"'<>#&="