// the TFE_Op structure.
typedef struct TFE_Op TFE_Op;

namespace tensorflow {

DEFINE_CONVERSION_FUNCTIONS(tensorflow::ImmediateExecutionOperation, TFE_Op);
DEFINE_CONVERSION_FUNCTIONS(tensorflow::ImmediateExecutionOperation*, TFE_Op*);

}  // namespace tensorflow

#ifndef TENSORFLOW_C_EAGER_ABSTRACT_OP_ATTRS_H_
#define TENSORFLOW_C_EAGER_ABSTRACT_OP_ATTRS_H_

#include "absl/container/inlined_vector.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

// Attributes of an op.
class AbstractOpAttrs {
 protected:

#ifndef TENSORFLOW_C_C_OP_REQUIRES_H_
#define TENSORFLOW_C_C_OP_REQUIRES_H_

#include "tensorflow/core/platform/macros.h"

namespace tensorflow {

// Convenience macros for asserting and handling exceptional conditions, for
// C structs, including `TF_OpKernelContext`, `TF_Status`, etc. This is analogus
// to the macros in tensorflow/core/framework/op_requires.h. This is provided

typedef struct TFE_OpAttrs TFE_OpAttrs;

typedef struct TFE_Context TFE_Context;
typedef struct TFE_Op TFE_Op;

namespace tensorflow {
DEFINE_CONVERSION_FUNCTIONS(tensorflow::AbstractOpAttrs, TFE_OpAttrs);

// Set an AttrValue on the op. Doesn't handle the list types.
void SetOpAttrValueScalar(TFE_Context* ctx, TFE_Op* op,
                          const tensorflow::AttrValue& default_value,

// Return an add op multiplying `a` by `b`.
TFE_Op* AddOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b);

// Return a matmul op multiplying `a` by `b`.
TFE_Op* MatMulOp(TFE_Context* ctx, TFE_TensorHandle* a, TFE_TensorHandle* b);

// Return an identity op.
TFE_Op* IdentityOp(TFE_Context* ctx, TFE_TensorHandle* a);

// Return a shape op fetching the shape of `a`.

    const char* device, TF_Status* status) {
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context, "TPUReplicatedInput", status), TFE_DeleteOp);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrInt(op.get(), "N", num_replicas);
  for (int i = 0; i < num_replicas; ++i) {
    TFE_OpAddInput(op.get(), components[i], status);
    if (TF_GetCode(status) != TF_OK) return nullptr;
  }

    // on to one TFE_Op per device and just call TFE_ResetOp to avoid parsing
    // device names repeatedly.
    std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> const_op(
        TFE_NewOp(context, "Const", status), TFE_DeleteOp);
    if (TF_GetCode(status) != TF_OK) return nullptr;
    TFE_OpSetDevice(const_op.get(), underlying_devices_[device_index].c_str(),
                    status);

                 absl::Span<AbstractTensorHandle*> grad_inputs) override;
};
// Shorthand for registry->Register(op, new NotDifferentiableGradientFunction)
Status RegisterNotDifferentiable(GradientRegistry* registry, const string& op);
}  // namespace gradients
}  // namespace tensorflow

// Returns the serialized OpDef proto with name `op_name`, or a bad status if no
// such op exists. This can return OpDefs of functions copied into the graph.
TF_CAPI_EXPORT extern void TF_GraphGetOpDef(TF_Graph* graph,
                                            const char* op_name,
                                            TF_Buffer* output_op_def,
                                            TF_Status* status);

//     the additional sanity checks there seem unnecessary;
typedef struct TFE_Op TFE_Op;

TF_CAPI_EXPORT extern TFE_Op* TFE_NewOp(TFE_Context* ctx,
                                        const char* op_or_function_name,
                                        TF_Status* status);
TF_CAPI_EXPORT extern void TFE_DeleteOp(TFE_Op* op);

// Returns the op or function name `op` will execute.
//