// along with this program.  If not, see <http://www.gnu.org/licenses/>.

package cmd

// Format related consts
const (
	// Format config file carries backend format specific details.
	formatConfigFile = "format.json"
)

const (
	// Version of the formatMetaV1
	formatMetaVersionV1 = "1"
)

// format.json currently has the format:
// {
//   "version": "1",

    validations:
      required: false

  - type: input
    id: perf-costs
    attributes:
      label: Performance Costs
      description: "What is the compile time cost? What is the run time cost? "
    validations:
      required: false

  - type: textarea
    id: prototype
    attributes:
      label: "Prototype"

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

  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;

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.

                       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.

pkg p1, const A = 1
pkg p1, const A ideal-int
pkg p1, const A //deprecated
pkg p1, const A64 = 1
pkg p1, const A64 int64
pkg p1, const AIsLowerA = 11
pkg p1, const AIsLowerA ideal-int
pkg p1, const B0 = 2
pkg p1, const B0 ideal-int
pkg p1, const ConstChase2 = 11
pkg p1, const ConstChase2 ideal-int
pkg p1, const ConversionConst = 5
pkg p1, const ConversionConst MyInt
pkg p1, const FloatConst = 1.5  // 3/2
pkg p1, const FloatConst ideal-float

  }
}

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&

                               const void* const* values, const size_t* lengths,
                               int num_values, ForwardOperation*);
absl::Status SetAttrFloatList(AbstractOperation*, const char* attr_name,
                              const float* values, int num_values,
                              ForwardOperation*);
absl::Status SetAttrIntList(AbstractOperation*, const char* attr_name,