};

// 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 {

}

int TF_OperationGetNumAttrs(TF_Operation* oper) {
  return oper->node.attrs().size();
}

int TF_OperationGetAttrNameLength(TF_Operation* oper, int i) {
  auto attrs = oper->node.attrs();
  int count = 0;
  AttrValueMap::const_iterator it;
  for (it = attrs.begin(); it != attrs.end(); it++) {
    if (count == i) {
      return it->first.length();
    }
    count++;
  }

  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 =
      tensorflow::OpRegistry::Global()->LookUp(node_def.op(), &op_reg_data);

              &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(

  tensorflow::FunctionDef fdef;
  status->status = tensorflow::GraphToFunctionDef(
      fn_body->graph, fn_name, append_hash_to_fn_name != 0,
      /*set_stateful_from_nodes=*/true,
      /*copy_placeholder_attrs_from_nodes=*/true, body_nodes, input_tensors,
      output_tensors, output_names_vec, control_output_nodes,
      control_output_names_vec, description, &fdef);
  if (TF_GetCode(status) != TF_OK) {
    return nullptr;
  }

  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);

  tensorflow::NameAttrList name_and_attrs;
  ASSERT_TRUE(name_and_attrs.ParseFromArray(serialized_attr_values->data,
                                            serialized_attr_values->length));
  ASSERT_EQ("VarHandleOp", name_and_attrs.name());
  ASSERT_EQ(tensorflow::DT_INT64,
            name_and_attrs.attr().find("dtype")->second.type());
  TF_DeleteBuffer(serialized_attr_values);

  }
  // TODO(b/166669239): This is needed to support AttrBuilder::Get for string
  // attributes. Number type attrs and DataType attrs work fine without this.
  // Consider getting rid of this and making the behavior between number types
  // and string consistent.
  forward_op_->attrs.BuildNodeDef();
  std::unique_ptr<GradientFunction> gradient_fn;
  TF_RETURN_IF_ERROR(registry.Lookup(*forward_op_, &gradient_fn));

};

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,

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 {