TEST(TFCompileTest, Add) {
  AddComp add;
  EXPECT_EQ(add.arg0_data(), add.arg_data(0));
  EXPECT_EQ(add.arg1_data(), add.arg_data(1));

  add.arg0() = 1;
  add.arg1() = 2;
  EXPECT_TRUE(add.Run());
  EXPECT_EQ(add.error_msg(), "");
  EXPECT_EQ(add.result0(), 3);
  EXPECT_EQ(add.result0_data()[0], 3);
  EXPECT_EQ(add.result0_data(), add.results()[0]);

  add.arg0_data()[0] = 123;
  add.arg1_data()[0] = 456;
  EXPECT_TRUE(add.Run());

  void set_arg0_data(const void* data) {
    set_arg_data(0, data);
  }
  float* arg0_data() {
    return static_cast<float*>(arg_data(0));
  }
  float& arg0(size_t dim0, size_t dim1) {
    return (*static_cast<float(*)[1][2]>(
        arg_data(0)))[dim0][dim1];
  }
  const float* arg0_data() const {
    return static_cast<const float*>(arg_data(0));
  }
  const float& arg0(size_t dim0, size_t dim1) const {

  }
  {{TYPE}}* arg{{NAME}}_data() {
    return static_cast<{{TYPE}}*>(arg_data({{I}}));
  }
  {{TYPE}}& arg{{NAME}}({{DIM_VARS}}) {
    return (*static_cast<{{TYPE}}(*){{DIM_SIZES}}>(
        arg_data({{I}}))){{INDICES}};
  }
  const {{TYPE}}* arg{{NAME}}_data() const {
    return static_cast<const {{TYPE}}*>(arg_data({{I}}));
  }
  const {{TYPE}}& arg{{NAME}}({{DIM_VARS}}) const {

// clang-format on

namespace tensorflow {
namespace tfcompile {
namespace {

void zero_buffers(XlaCompiledCpuFunction* computation) {
  for (int i = 0; i < computation->num_args(); ++i) {
    memset(computation->arg_data(i), 0, computation->arg_size(i));
  }
}

// Trivial test that runs the generated function to ensure it doesn't crash.
TEST(TEST_NAME, NoCrash) {
  Eigen::ThreadPool pool(port::MaxParallelism());

// clang-format on

namespace tensorflow {
namespace tfcompile {
namespace {

void zero_buffers(XlaCompiledCpuFunction* computation) {
  for (int i = 0; i < computation->num_args(); ++i) {
    memset(computation->arg_data(i), 0, computation->arg_size(i));
  }
}

// Trivial test that runs the generated function to ensure it doesn't crash.
TEST(TEST_NAME, NoCrash) {
  Eigen::ThreadPool pool(port::MaxParallelism());

type instArg uint16

const (
	_ instArg = iota
	arg_Bt
	arg_Cm
	arg_Cn
	arg_cond_AllowALNV_Normal
	arg_conditional
	arg_cond_NotAllowALNV_Invert
	arg_Da
	arg_Dd
	arg_Dm
	arg_Dn
	arg_Dt
	arg_Dt2
	arg_Hd
	arg_Hn
	arg_Ht
	arg_IAddSub
	arg_immediate_0_127_CRm_op2
	arg_immediate_0_15_CRm
	arg_immediate_0_15_nzcv
	arg_immediate_0_31_imm5
	arg_immediate_0_31_immr

	{0xffc00000, 0x2c400000, LDNP, instArgs{arg_St, arg_St2, arg_Xns_mem_optional_imm7_4_signed}, nil},
	// LDNP <Dt>, <Dt2>, [<Xn|SP>{, #<imm>}]
	{0xffc00000, 0x6c400000, LDNP, instArgs{arg_Dt, arg_Dt2, arg_Xns_mem_optional_imm7_8_signed}, nil},
	// LDNP <Qt>, <Qt2>, [<Xn|SP>{, #<imm_1>}]
	{0xffc00000, 0xac400000, LDNP, instArgs{arg_Qt, arg_Qt2, arg_Xns_mem_optional_imm7_16_signed}, nil},

  };

  StreamExecutor* executor = GetExecutor(0);
  TF_ASSERT_OK_AND_ASSIGN(auto stream, executor->CreateStream());
  size_t size = sizeof(int);
  int src_data = 34;
  int dst_data = 2;
  DeviceMemoryBase device_src(&src_data, size);
  TF_ASSERT_OK(stream->Memcpy(&dst_data, device_src, size));
  ASSERT_EQ(dst_data, 34);
}

TEST_F(StreamExecutorTest, MemcpyFromHost) {

// CHECK-LABEL: @tf__my_requantize
tfr.func @tf__my_requantize(%input: !tfr.tensor) -> !tfr.tensor {
  %raw_data = tfr.quant_raw_data(%input) : (!tfr.tensor) -> !tfr.tensor
  %scale, %zp = tfr.quant_qparam(%input) : (!tfr.tensor) -> (!tfr.tensor, !tfr.tensor)
  %result = tfr.call @tf__requantize(%raw_data, %scale, %zp) : (!tfr.tensor, !tfr.tensor, !tfr.tensor) -> !tfr.tensor
  tfr.return %result : !tfr.tensor
}

 */
class HpackRoundTripTest : HpackDecodeTestBase() {
  internal class StoriesTestProvider : SimpleProvider() {
    override fun arguments(): List<Any> = createStories(RAW_DATA)
  }

  private val bytesOut = Buffer()
  private val hpackWriter = Hpack.Writer(out = bytesOut)

  @ParameterizedTest
  @ArgumentsSource(StoriesTestProvider::class)
  fun testRoundTrip(story: Story) {