#include "tensorflow/c/experimental/gradients/tape/tape_context.h"

#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/experimental/gradients/tape/tape_operation.h"

namespace tensorflow {
namespace gradients {
TapeContext::TapeContext(AbstractContext* c, Tape* tape,
                         const GradientRegistry& registry)
    : AbstractContext(kTape), parent_ctx_(c), tape_(tape), registry_(registry) {

  // For LLVM style RTTI.
  static bool classof(const AbstractContext* ptr) {
    return ptr->getKind() == kTape;
  }
  ~TapeContext() override;

 private:
  AbstractContext* parent_ctx_;  // Not owned.
  Tape* tape_;
  const GradientRegistry& registry_;
};
}  // namespace gradients
}  // namespace tensorflow

  static bool classof(const AbstractOperation* ptr) {
    return ptr->getKind() == kTape;
  }
  ~TapeOperation() override;

 private:
  AbstractOperation* parent_op_;
  ForwardOperation forward_op_;
  Tape* tape_;
  const GradientRegistry& registry_;
};

}  // namespace gradients
}  // namespace tensorflow

==============================================================================*/
#include "tensorflow/c/experimental/gradients/tape/tape_operation.h"

#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/gradients.h"

namespace tensorflow {
namespace gradients {
TapeOperation::TapeOperation(AbstractOperation* parent_op, Tape* tape,
                             const GradientRegistry& registry)
    : AbstractOperation(kTape),

// watched and not the result of any operation in the tape.
using TensorTape = std::unordered_map<int64_t, int64_t>;

// Map from operation-id to tape entry.
template <typename BackwardFunction, typename TapeTensor>
using OpTape =
    std::unordered_map<int64_t, OpTapeEntry<BackwardFunction, TapeTensor>>;

// Operations the tape needs to perform on tensors to do backpropagation. Named

	{as: AMOVFL, a1: C_REG, a6: C_CREG, type_: 69, size: 4},
	{as: AMOVFL, a1: C_REG, a6: C_32CON, type_: 69, size: 4},

	{as: ASYSCALL, type_: 5, size: 4},
	{as: ASYSCALL, a1: C_REG, type_: 77, size: 12},
	{as: ASYSCALL, a1: C_U15CON, type_: 77, size: 12},
	{as: ABEQ, a6: C_BRA, type_: 16, size: 4},
	{as: ABEQ, a1: C_CREG, a6: C_BRA, type_: 16, size: 4},

		`//go:noinline
func {{.Name}}_{{.Type_}}_{{.FNumber}}(a {{.Type_}}) {{.Type_}} { return a {{.Symbol}} {{.Number}} }
`))
	fncCnst2 := template.Must(template.New("fnc").Parse(
		`//go:noinline
func {{.Name}}_{{.FNumber}}_{{.Type_}}(a {{.Type_}}) {{.Type_}} { return {{.Number}} {{.Symbol}} a }
`))

	type fncData struct {
		Name, Type_, Symbol, FNumber, Number string
	}

	for _, s := range szs {

  explicit TaggedValue(float f32) : type_(FLOAT32), data_(Float32(f32)) {}
  /// TaggedValue constructor for type INT64.
  explicit TaggedValue(int64_t i64) : type_(INT64), data_(Int64(i64)) {}
  /// TaggedValue constructor for type FLOAT32.
  explicit TaggedValue(Float32 f32) : type_(FLOAT32), data_(f32) {}
  /// TaggedValue constructor for type INT64.
  explicit TaggedValue(Int64 i64) : type_(INT64), data_(i64) {}

// A TF_ExecutionContext stores knowledge about how to execute an operation.
// E.g. it could know whether we're in eager mode or graph mode, keeps track
// of gradient tapes, etc.
typedef struct TF_ExecutionContext TF_ExecutionContext;

// A TF_AbstractTensor is an input to an operation. E.g. it could be a union
// type of eager and graph tensors. It is also the result of executing an

  AbstractTensorHandle* GetHandle() const;

 private:
  AbstractTensorHandle* handle_;
};

// A tracing/immediate-execution agnostic tape.
//
// Gradient functions defined for this tape must support handling null incoming
// gradients.
class Tape : protected eager::GradientTape<AbstractTensorHandle,
                                           GradientFunction, TapeTensor> {
 public: