void TF_AbstractOpSetOpType(TF_AbstractOp* op, const char* const op_type,
                            TF_Status* s) {
  tsl::Set_TF_Status_from_Status(
      s, unwrap(op)->Reset(op_type,
                           /*raw_device_name=*/nullptr));
}

void TF_AbstractOpSetOpName(TF_AbstractOp* op, const char* const op_name,
                            TF_Status* s) {
  TracingOperation* tracing_op = dyn_cast<TracingOperation>(unwrap(op));

GradientFunction* ReluRegisterer(const ForwardOperation& op) {
  return new ReluGradientFunction(op.outputs);
}

GradientFunction* SparseSoftmaxCrossEntropyWithLogitsRegisterer(
    const ForwardOperation& op) {
  return new SparseSoftmaxCrossEntropyWithLogitsGradientFunction(op.outputs);
}

GradientFunction* BiasAddRegisterer(const ForwardOperation& op) {
  return new BiasAddGradientFunction(op.attrs);
}

}  // namespace gradients

  delete lib_handle;
}

TF_Buffer* TF_GetAllOpList() {
  std::vector<tensorflow::OpDef> op_defs;
  tensorflow::OpRegistry::Global()->GetRegisteredOps(&op_defs);
  tensorflow::OpList op_list;
  for (const auto& op : op_defs) {
    *(op_list.add_op()) = op;
  }
  TF_Buffer* ret = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(op_list, ret));
  return ret;
}

  NodeDef node_def;
  tensorflow::ImmediateExecutionOperation* op = tensorflow::unwrap(tfe_op);
  node_def.set_name(op->Name());
  node_def.set_op(op->Name());
  for (int i = 0; i < num_inputs; ++i) {
    node_def.add_input("dummy_input");
  }
  OperationFromInterface(op)->Attrs().FillAttrValueMap(node_def.mutable_attr());

  const tensorflow::OpRegistrationData* op_reg_data;
  status->status =

      }
      grad_inputs[i] = grad_input;
    }
    return absl::OkStatus();
  }
  ~IdentityNGradientFunction() override {}
};
}  // namespace

GradientFunction* IdentityNRegisterer(const ForwardOperation& op) {
  return new IdentityNGradientFunction;
}

}  // namespace gradients

namespace internal {
Status Reset(AbstractOperation* op_, const char* op,
             const char* raw_device_name, ForwardOperation* forward_op_) {
  forward_op_->op_name = op;
  forward_op_->attrs.Reset(op);
  return op_->Reset(op, raw_device_name);
}
Status AddInput(AbstractOperation* op_, AbstractTensorHandle* input,
                ForwardOperation* forward_op_) {
  TF_RETURN_IF_ERROR(op_->AddInput(input));

    TF_DeleteStatus(status);
    ASSERT_EQ(TF_OK, code) << status_msg;

    // Test op list.
    TF_Buffer op_list_buf = TF_GetOpList(lib);
    tensorflow::OpList op_list;
    EXPECT_TRUE(op_list.ParseFromArray(op_list_buf.data, op_list_buf.length));
    ASSERT_EQ(op_list.op_size(), 1);
    EXPECT_EQ("TestCApi1", op_list.op(0).name());
    TF_DeleteLibraryHandle(lib);
  }
#endif  // !defined(TENSORFLOW_NO_SHARED_OBJECTS)

  }

  AbstractOperationPtr check_numerics_op(ctx->CreateOperation());
  ForwardOperation forward_op;
  Status s = Reset(check_numerics_op.get(), "CheckNumerics",
                   /*raw_device_name=*/nullptr, &forward_op);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  if (isa<TracingOperation>(check_numerics_op.get())) {
    s = dyn_cast<TracingOperation>(check_numerics_op.get())
            ->SetOpName("check_numerics");

        return;
      } else if (execution_state_ == ExecutionState::kReadyToExecute) {
        // op_outputs_ may have been std::moved
        op_outputs_ = std::vector<TensorHandlePtr>();
        Execute(context_, operation_name_, std::move(op_inputs_), attributes_,
                expected_max_outputs_, &op_outputs_, status_.get());
        execution_state_ = ExecutionState::kHasResult;
      }
    }

  TFE_OpAddInput(op, in, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);

  TFE_OpSetAttrType(op, "T", TFE_TensorHandleDataType(in));
  TFE_OpSetAttrInt(op, "group_size", group_size);
  TFE_OpSetAttrInt(op, "group_key", 123);
  TFE_OpSetAttrInt(op, "instance_key", 456);
  TFE_OpSetAttrString(op, "merge_op", "Add", 3);
  TFE_OpSetAttrString(op, "final_op", "Id", 2);