} TP_OptimizerRegistrationParams;

#define TP_OPTIMIZER_REGISTRATION_PARAMS_STRUCT_SIZE \
  TF_OFFSET_OF_END(TP_OptimizerRegistrationParams, optimizer)

// TF_InitGraph is used to do graph optimizer registration.
// Plugin should implement TF_InitGraph to register graph optimizers.
void TF_InitGraph(TP_OptimizerRegistrationParams* params, TF_Status* status);

// * Backing store for tensor memory has an associated deallocation
//   function.  This deallocation function will point to client code
//   for tensors populated by the client.  So the client can do things
//   like shadowing a numpy array.
// * We do not provide TF_OK since it is not strictly necessary and we
//   are not optimizing for convenience.
// * We make assumption that one session has one graph.  This should be

#ifndef TENSORFLOW_C_EAGER_TAPE_H_
#define TENSORFLOW_C_EAGER_TAPE_H_

// Language-agnostic gradient tape. Does not perform backpropagation, just
// maintains the data structures required to do so.

#include <stack>
#include <unordered_map>
#include <unordered_set>
#include <vector>

#include "tensorflow/core/config/flag_defs.h"
#include "tensorflow/core/config/flags.h"

// Returns the function content in a human-readable format, with length set in
// `len`. The format is subject to change in the future.
// The returned string is heap-allocated, and caller should call free() on it.
//
// Do not return const char*, because some foreign language binding
// (e.g. swift) cannot then call free() on the returned pointer.
TF_CAPI_EXPORT extern char* TF_FunctionDebugString(TF_Function* func,

// https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api/C_API_versioning_strategy.md
#define TF_VALIDATE_STRUCT_SIZE(STRUCT_NAME, STRUCT_OBJ, SIZE_VALUE_NAME) \
  do {                                                                    \
    if (STRUCT_OBJ.struct_size == 0) {                                    \
      return tensorflow::Status(absl::StatusCode::kFailedPrecondition,    \

//
// Very similar to TF_OperationDescription with some differences:
// (1) TF_Output or TFE_TensorHandle* as arguments to TF_AddInput,
//     TF_AddInputList
// (2) TF_ColocateWith, TF_AddControlInput etc. do not make sense.
// (3) Implementation detail: Avoid use of NodeBuilder/NodeDefBuilder since
//     the additional sanity checks there seem unnecessary;
typedef struct TFE_Op TFE_Op;

// to the macros in tensorflow/core/framework/op_requires.h. This is provided
// for plugin OpKernel developer's convenience.

#define C_OPKERNELCONTEXT_REQUIRES_OK(CTX, C_STATUS, __VA_ARGS__) \
  do {                                                            \
    ::tensorflow::Status _s(__VA_ARGS__);                         \
    if (!TF_PREDICT_TRUE(_s.ok())) {                              \

/// type or the constness of a parameter, etc.) results in an ABI breakage.
/// Thus, we should not do any of these types of changes, except, potentially,
/// when we are releasing a new major version of TensorFlow. This is an escape
/// hatch, to be used rarely, preferably only to cleanup these structures.
/// Whenever we do these changes, the ABI number must be increased.
///