limitations under the License.
==============================================================================*/
#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"

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_TFE_OP_ATTRS_INTERNAL_H_
#define TENSORFLOW_C_EAGER_TFE_OP_ATTRS_INTERNAL_H_

#include "tensorflow/c/conversion_macros.h"
#include "tensorflow/c/eager/abstract_op_attrs.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/core/framework/attr_value.pb.h"

  forward_op_.attrs.Set(attr_name, value);
  return parent_op_->SetAttrFloat(attr_name, value);
}
Status TapeOperation::SetAttrBool(const char* attr_name, bool value) {
  forward_op_.attrs.Set(attr_name, value);
  return parent_op_->SetAttrBool(attr_name, value);
}
Status TapeOperation::SetAttrType(const char* attr_name, DataType value) {
  forward_op_.attrs.Set(attr_name, value);

};

// TODO(vnvo2409): Add python test
class BiasAddGradientFunction : public GradientFunction {
 public:
  explicit BiasAddGradientFunction(AttrBuilder f_attrs)
      : forward_attrs_(f_attrs) {}

  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {

#include "tensorflow/c/eager/tfe_executor_internal.h"  // IWYU pragma: export
#include "tensorflow/c/eager/tfe_monitoring_internal.h"  // IWYU pragma: export
#include "tensorflow/c/eager/tfe_op_attrs_internal.h"  // IWYU pragma: export
#include "tensorflow/c/eager/tfe_tensor_debug_info_internal.h"  // IWYU pragma: export

// TODO(b/154564140): Move this to its own header. This requires splitting
// c_api_experimental.h

  virtual void SetStackTrace(ManagedStackTrace stack_trace) = 0;

  virtual const tensorflow::AbstractOpAttrs* GetOpAttrs() const = 0;
  virtual void AddAttrs(const AbstractOpAttrs* op_attrs) = 0;

  virtual void SetCancellationManager(
      CancellationManager* cancellation_manager) = 0;

  // Returns the stack trace set by `SetStackTrace` if exists.

//     return OkStatus();
//   }
//   ~AddGradientFunction() override {}
// };
//
// GradientFunction* AddRegisterer(const ForwardOperation& op) {
//   // More complex gradient functions can use inputs/attrs etc. from the
//   // forward `op`.
//   return new AddGradientFunction;
// }
//
// Status RegisterGradients(GradientRegistry* registry) {
//   return registry->Register("Add", AddRegisterer);
// }

Status AddInputList(AbstractOperation*,
                    absl::Span<AbstractTensorHandle* const> inputs,
                    ForwardOperation*);

// Sets the attrs in the `ForwardOperation`.
Status SetAttrString(AbstractOperation*, const char* attr_name,
                     const char* data, size_t length, ForwardOperation*);

// requires its type (e.g. "AddV2") to be set independently.
TF_AbstractOp* TF_NewAbstractOp(TF_ExecutionContext* ctx);
void TF_DeleteAbstractOp(TF_AbstractOp*);

// TODO(srbs): Add APIs for specifying attrs etc.
// `op_type` must outlive `op`.
void TF_AbstractOpSetOpType(TF_AbstractOp* op, const char* const op_type,
                            TF_Status* s);
// `op_name` must outlive `op`.

              &num_retvals, &forward_op, tape.get(), registry);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

  string read_message;
  s = forward_op.attrs.Get("message", &read_message);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  ASSERT_EQ(read_message, message);
}

Status RecordOperationWithNullGradientFunctionModel(