};

// 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 {

};

class MatMulGradientFunction : public GradientFunction {
 public:
  explicit MatMulGradientFunction(vector<AbstractTensorHandle*> f_inputs,
                                  AttrBuilder f_attrs)
      : forward_inputs_(f_inputs), forward_attrs_(f_attrs) {
    for (auto input : forward_inputs_) {
      if (input) {
        input->Ref();
      }
    }
  }

  Status Compute(AbstractContext* ctx,

                                          const char* attr_name,
                                          TF_Status* status) {
  const tensorflow::AttrValue* attr = oper->node.attrs().Find(attr_name);
  if (attr == nullptr) {
    status->status = InvalidArgument("Operation '", oper->node.name(),
                                     "' has no attr named '", attr_name, "'.");
  }
  return attr;
}

        "//tensorflow/core:lib",
        "@com_google_absl//absl/memory",
    ],
)

cc_library(
    name = "tfe_op_attrs_internal",
    hdrs = ["tfe_op_attrs_internal.h"],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        ":abstract_op_attrs",
        "//tensorflow/c:conversion_macros",
        "//tensorflow/c:tf_status",
        "//tensorflow/core:protos_all_cc",
    ],

void TFE_OpAddAttrs(TFE_Op* op, const TFE_OpAttrs* attrs) {
  tensorflow::unwrap(op)->AddAttrs(tensorflow::unwrap(attrs));
}

void TFE_OpAttrsSerialize(const TFE_OpAttrs* attrs, TF_Buffer* buf,
                          TF_Status* status) {
  tensorflow::NameAttrList name_and_attrs;
  tensorflow::unwrap(attrs)->GetNameAttrList(&name_and_attrs);
  status->status = MessageToBuffer(name_and_attrs, buf);
}

namespace tensorflow {

// while `op` is alive.
TF_CAPI_EXPORT extern const TFE_OpAttrs* TFE_OpGetAttrs(const TFE_Op* op);
// Add attributes in `attrs` to `op`.
//
// Does not overwrite or update existing attributes, but adds new ones.
TF_CAPI_EXPORT extern void TFE_OpAddAttrs(TFE_Op* op, const TFE_OpAttrs* attrs);

// Serialize `attrs` as a tensorflow::NameAttrList protocol buffer (into `buf`),
// containing the op name and a map of its attributes.

  Status SetAttrFloat(const char* attr_name, float value) override {
    op_->node_builder.Attr(attr_name, value);
    return absl::OkStatus();
  }
  Status SetAttrBool(const char* attr_name, bool value) override {
    op_->node_builder.Attr(attr_name, value);
    return absl::OkStatus();
  }
  Status SetAttrType(const char* const attr_name, DataType value) override {
    if (!op_) {
      return Status(

    return;
  }

  (*(fdef_or.value()->mutable_attr()))[string(attr_name)] = attr_value;

  status->status = absl::OkStatus();
}

void TF_FunctionGetAttrValueProto(TF_Function* func, const char* attr_name,
                                  TF_Buffer* output_attr_value,
                                  TF_Status* status) {
  const auto& it = func->record->fdef().attr().find(attr_name);
  if (it == func->record->fdef().attr().end()) {

void TF_AttrBuilderSetType(TF_AttrBuilder* builder, const char* attr_name,
                           TF_DataType value) {
  auto iter = builder->attr_names.insert(attr_name).first;
  builder->Set(*iter, static_cast<tensorflow::DataType>(value));
}

void TF_AttrBuilderSetTypeList(TF_AttrBuilder* builder, const char* attr_name,
                               const TF_DataType* values, int num_values) {

ATTR_TEST_REGISTER_OP(string);
ATTR_TEST_REGISTER_OP(int);
ATTR_TEST_REGISTER_OP(float);
ATTR_TEST_REGISTER_OP(bool);
ATTR_TEST_REGISTER_OP(type);
ATTR_TEST_REGISTER_OP(shape);
ATTR_TEST_REGISTER_OP(tensor);
#undef ATTR_TEST_REGISTER_OP

class CApiAttributesTest : public ::testing::Test {
 protected:
  CApiAttributesTest()