diff --git a/TEST_MAPPING b/TEST_MAPPING
index 98e125b..0e18f48 100644
--- a/TEST_MAPPING
+++ b/TEST_MAPPING
@@ -1,6 +1,12 @@
 {
   "presubmit": [
     {
+      "name": "vts_treble_vintf_framework_test"
+    },
+    {
+      "name": "vts_treble_vintf_vendor_test"
+    },
+    {
       "name": "hidl_implementation_test"
     }
   ]
diff --git a/audio/6.0/IDevice.hal b/audio/6.0/IDevice.hal
index e885fe2..122c550 100644
--- a/audio/6.0/IDevice.hal
+++ b/audio/6.0/IDevice.hal
@@ -280,4 +280,19 @@
      */
     setConnectedState(DeviceAddress address, bool connected)
             generates (Result retval);
+
+    /**
+     * Called by the framework to deinitialize the device and free up
+     * all currently allocated resources. It is recommended to close
+     * the device on the client side as soon as it is becomes unused.
+     *
+     * Note that all streams must be closed by the client before
+     * attempting to close the device they belong to.
+     *
+     * @return retval OK in case the success.
+     *                INVALID_STATE if the device was already closed
+     *                or there are streams currently opened.
+     */
+    @exit
+    close() generates (Result retval);
 };
diff --git a/audio/6.0/IStream.hal b/audio/6.0/IStream.hal
index 451e116..d7d3c84 100644
--- a/audio/6.0/IStream.hal
+++ b/audio/6.0/IStream.hal
@@ -277,7 +277,7 @@
      * @return retval OK in case the success.
      *                NOT_SUPPORTED on non mmap mode streams
      *                NOT_INITIALIZED in case of memory allocation error
-     *                INVALID_ARGUMENTS if the requested buffer size is too large
+     *                INVALID_ARGUMENTS if the requested buffer size is invalid
      *                INVALID_STATE if called out of sequence
      * @return info    a MmapBufferInfo struct containing information on the MMMAP buffer created.
      */
@@ -300,13 +300,17 @@
 
     /**
      * Called by the framework to deinitialize the stream and free up
-     * all the currently allocated resources. It is recommended to close
+     * all currently allocated resources. It is recommended to close
      * the stream on the client side as soon as it is becomes unused.
      *
+     * The client must ensure that this function is not called while
+     * audio data is being transferred through the stream's message queues.
+     *
      * @return retval OK in case the success.
      *                NOT_SUPPORTED if called on IStream instead of input or
      *                              output stream interface.
      *                INVALID_STATE if the stream was already closed.
      */
+    @exit
     close() generates (Result retval);
 };
diff --git a/audio/common/all-versions/test/utility/include/utility/PrettyPrintAudioTypes.h b/audio/common/all-versions/test/utility/include/utility/PrettyPrintAudioTypes.h
deleted file mode 100644
index 3833fd0..0000000
--- a/audio/common/all-versions/test/utility/include/utility/PrettyPrintAudioTypes.h
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * Copyright (C) 2018 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#ifndef ANDROID_HARDWARE_AUDIO_COMMON_TEST_UTILITY_PRETTY_PRINT_AUDIO_TYPES_H
-#define ANDROID_HARDWARE_AUDIO_COMMON_TEST_UTILITY_PRETTY_PRINT_AUDIO_TYPES_H
-
-#include <iosfwd>
-#include <utility>
-
-/** @file Use HIDL generated toString methods to pretty print gtest errors
- *        Unfortunately Gtest does not offer a template to specialize, only
- *        overloading PrintTo.
- *  @note that this overload can NOT be template because
- *        the fallback is already template, resulting in ambiguity.
- *  @note that the overload MUST be in the exact namespace
- *        the type is declared in, as per the ADL rules.
- */
-
-namespace android {
-namespace hardware {
-namespace audio {
-
-#define DEFINE_GTEST_PRINT_TO(T) \
-    inline void PrintTo(const T& val, ::std::ostream* os) { *os << toString(val); }
-
-namespace CPP_VERSION {
-DEFINE_GTEST_PRINT_TO(IPrimaryDevice::TtyMode)
-DEFINE_GTEST_PRINT_TO(Result)
-}  // namespace CPP_VERSION
-
-namespace common {
-namespace CPP_VERSION {
-DEFINE_GTEST_PRINT_TO(AudioConfig)
-DEFINE_GTEST_PRINT_TO(AudioMode)
-DEFINE_GTEST_PRINT_TO(AudioDevice)
-DEFINE_GTEST_PRINT_TO(AudioFormat)
-DEFINE_GTEST_PRINT_TO(AudioChannelMask)
-}  // namespace CPP_VERSION
-}  // namespace common
-
-#undef DEFINE_GTEST_PRINT_TO
-
-}  // namespace audio
-}  // namespace hardware
-}  // namespace android
-
-#endif  // ANDROID_HARDWARE_AUDIO_COMMON_TEST_UTILITY_PRETTY_PRINT_AUDIO_TYPES_H
diff --git a/audio/core/all-versions/default/Device.cpp b/audio/core/all-versions/default/Device.cpp
index 1a9df21..21dab00 100644
--- a/audio/core/all-versions/default/Device.cpp
+++ b/audio/core/all-versions/default/Device.cpp
@@ -39,11 +39,10 @@
 
 using ::android::hardware::audio::common::CPP_VERSION::implementation::HidlUtils;
 
-Device::Device(audio_hw_device_t* device) : mDevice(device) {}
+Device::Device(audio_hw_device_t* device) : mIsClosed(false), mDevice(device) {}
 
 Device::~Device() {
-    int status = audio_hw_device_close(mDevice);
-    ALOGW_IF(status, "Error closing audio hw device %p: %s", mDevice, strerror(-status));
+    (void)doClose();
     mDevice = nullptr;
 }
 
@@ -54,10 +53,14 @@
 
 void Device::closeInputStream(audio_stream_in_t* stream) {
     mDevice->close_input_stream(mDevice, stream);
+    LOG_ALWAYS_FATAL_IF(mOpenedStreamsCount == 0, "mOpenedStreamsCount is already 0");
+    --mOpenedStreamsCount;
 }
 
 void Device::closeOutputStream(audio_stream_out_t* stream) {
     mDevice->close_output_stream(mDevice, stream);
+    LOG_ALWAYS_FATAL_IF(mOpenedStreamsCount == 0, "mOpenedStreamsCount is already 0");
+    --mOpenedStreamsCount;
 }
 
 char* Device::halGetParameters(const char* keys) {
@@ -159,6 +162,7 @@
     sp<IStreamOut> streamOut;
     if (status == OK) {
         streamOut = new StreamOut(this, halStream);
+        ++mOpenedStreamsCount;
     }
     HidlUtils::audioConfigFromHal(halConfig, suggestedConfig);
     return {analyzeStatus("open_output_stream", status, {EINVAL} /*ignore*/), streamOut};
@@ -185,6 +189,7 @@
     sp<IStreamIn> streamIn;
     if (status == OK) {
         streamIn = new StreamIn(this, halStream);
+        ++mOpenedStreamsCount;
     }
     HidlUtils::audioConfigFromHal(halConfig, suggestedConfig);
     return {analyzeStatus("open_input_stream", status, {EINVAL} /*ignore*/), streamIn};
@@ -383,6 +388,18 @@
 }
 #endif
 
+Result Device::doClose() {
+    if (mIsClosed || mOpenedStreamsCount != 0) return Result::INVALID_STATE;
+    mIsClosed = true;
+    return analyzeStatus("close", audio_hw_device_close(mDevice));
+}
+
+#if MAJOR_VERSION >= 6
+Return<Result> Device::close() {
+    return doClose();
+}
+#endif
+
 }  // namespace implementation
 }  // namespace CPP_VERSION
 }  // namespace audio
diff --git a/audio/core/all-versions/default/PrimaryDevice.cpp b/audio/core/all-versions/default/PrimaryDevice.cpp
index 99590b0..3cf0932 100644
--- a/audio/core/all-versions/default/PrimaryDevice.cpp
+++ b/audio/core/all-versions/default/PrimaryDevice.cpp
@@ -31,7 +31,11 @@
 
 PrimaryDevice::PrimaryDevice(audio_hw_device_t* device) : mDevice(new Device(device)) {}
 
-PrimaryDevice::~PrimaryDevice() {}
+PrimaryDevice::~PrimaryDevice() {
+    // Do not call mDevice->close here. If there are any unclosed streams,
+    // they only hold IDevice instance, not IPrimaryDevice, thus IPrimaryDevice
+    // "part" of a device can be destroyed before the streams.
+}
 
 // Methods from ::android::hardware::audio::CPP_VERSION::IDevice follow.
 Return<Result> PrimaryDevice::initCheck() {
@@ -160,6 +164,11 @@
     return mDevice->setConnectedState(address, connected);
 }
 #endif
+#if MAJOR_VERSION >= 6
+Return<Result> PrimaryDevice::close() {
+    return mDevice->close();
+}
+#endif
 
 // Methods from ::android::hardware::audio::CPP_VERSION::IPrimaryDevice follow.
 Return<Result> PrimaryDevice::setVoiceVolume(float volume) {
diff --git a/audio/core/all-versions/default/StreamIn.cpp b/audio/core/all-versions/default/StreamIn.cpp
index d316f83..f1152ca 100644
--- a/audio/core/all-versions/default/StreamIn.cpp
+++ b/audio/core/all-versions/default/StreamIn.cpp
@@ -139,8 +139,7 @@
 }  // namespace
 
 StreamIn::StreamIn(const sp<Device>& device, audio_stream_in_t* stream)
-    : mIsClosed(false),
-      mDevice(device),
+    : mDevice(device),
       mStream(stream),
       mStreamCommon(new Stream(&stream->common)),
       mStreamMmap(new StreamMmap<audio_stream_in_t>(stream)),
@@ -159,7 +158,9 @@
         status_t status = EventFlag::deleteEventFlag(&mEfGroup);
         ALOGE_IF(status, "read MQ event flag deletion error: %s", strerror(-status));
     }
+#if MAJOR_VERSION <= 5
     mDevice->closeInputStream(mStream);
+#endif
     mStream = nullptr;
 }
 
@@ -303,14 +304,16 @@
 }
 
 Return<Result> StreamIn::close() {
-    if (mIsClosed) return Result::INVALID_STATE;
-    mIsClosed = true;
-    if (mReadThread.get()) {
-        mStopReadThread.store(true, std::memory_order_release);
+    if (mStopReadThread.load(std::memory_order_relaxed)) {  // only this thread writes
+        return Result::INVALID_STATE;
     }
+    mStopReadThread.store(true, std::memory_order_release);
     if (mEfGroup) {
         mEfGroup->wake(static_cast<uint32_t>(MessageQueueFlagBits::NOT_FULL));
     }
+#if MAJOR_VERSION >= 6
+    mDevice->closeInputStream(mStream);
+#endif
     return Result::OK;
 }
 
diff --git a/audio/core/all-versions/default/StreamOut.cpp b/audio/core/all-versions/default/StreamOut.cpp
index 82cc408..396d354 100644
--- a/audio/core/all-versions/default/StreamOut.cpp
+++ b/audio/core/all-versions/default/StreamOut.cpp
@@ -138,8 +138,7 @@
 }  // namespace
 
 StreamOut::StreamOut(const sp<Device>& device, audio_stream_out_t* stream)
-    : mIsClosed(false),
-      mDevice(device),
+    : mDevice(device),
       mStream(stream),
       mStreamCommon(new Stream(&stream->common)),
       mStreamMmap(new StreamMmap<audio_stream_out_t>(stream)),
@@ -148,7 +147,7 @@
 
 StreamOut::~StreamOut() {
     ATRACE_CALL();
-    close();
+    (void)close();
     if (mWriteThread.get()) {
         ATRACE_NAME("mWriteThread->join");
         status_t status = mWriteThread->join();
@@ -159,10 +158,12 @@
         ALOGE_IF(status, "write MQ event flag deletion error: %s", strerror(-status));
     }
     mCallback.clear();
+#if MAJOR_VERSION <= 5
     mDevice->closeOutputStream(mStream);
     // Closing the output stream in the HAL waits for the callback to finish,
     // and joins the callback thread. Thus is it guaranteed that the callback
     // thread will not be accessing our object anymore.
+#endif
     mStream = nullptr;
 }
 
@@ -291,14 +292,16 @@
 #endif
 
 Return<Result> StreamOut::close() {
-    if (mIsClosed) return Result::INVALID_STATE;
-    mIsClosed = true;
-    if (mWriteThread.get()) {
-        mStopWriteThread.store(true, std::memory_order_release);
+    if (mStopWriteThread.load(std::memory_order_relaxed)) {  // only this thread writes
+        return Result::INVALID_STATE;
     }
+    mStopWriteThread.store(true, std::memory_order_release);
     if (mEfGroup) {
         mEfGroup->wake(static_cast<uint32_t>(MessageQueueFlagBits::NOT_EMPTY));
     }
+#if MAJOR_VERSION >= 6
+    mDevice->closeOutputStream(mStream);
+#endif
     return Result::OK;
 }
 
diff --git a/audio/core/all-versions/default/include/core/default/Device.h b/audio/core/all-versions/default/include/core/default/Device.h
index e64f00f..11ab607 100644
--- a/audio/core/all-versions/default/include/core/default/Device.h
+++ b/audio/core/all-versions/default/include/core/default/Device.h
@@ -114,6 +114,9 @@
     Return<void> getMicrophones(getMicrophones_cb _hidl_cb) override;
     Return<Result> setConnectedState(const DeviceAddress& address, bool connected) override;
 #endif
+#if MAJOR_VERSION >= 6
+    Return<Result> close() override;
+#endif
 
     Return<void> debug(const hidl_handle& fd, const hidl_vec<hidl_string>& options) override;
 
@@ -124,11 +127,15 @@
     void closeOutputStream(audio_stream_out_t* stream);
     audio_hw_device_t* device() const { return mDevice; }
 
-   private:
+  private:
+    bool mIsClosed;
     audio_hw_device_t* mDevice;
+    int mOpenedStreamsCount = 0;
 
     virtual ~Device();
 
+    Result doClose();
+
     // Methods from ParametersUtil.
     char* halGetParameters(const char* keys) override;
     int halSetParameters(const char* keysAndValues) override;
diff --git a/audio/core/all-versions/default/include/core/default/PrimaryDevice.h b/audio/core/all-versions/default/include/core/default/PrimaryDevice.h
index 9d69cb0..f5f3848 100644
--- a/audio/core/all-versions/default/include/core/default/PrimaryDevice.h
+++ b/audio/core/all-versions/default/include/core/default/PrimaryDevice.h
@@ -96,6 +96,9 @@
     Return<void> getMicrophones(getMicrophones_cb _hidl_cb) override;
     Return<Result> setConnectedState(const DeviceAddress& address, bool connected) override;
 #endif
+#if MAJOR_VERSION >= 6
+    Return<Result> close() override;
+#endif
 
     Return<void> debug(const hidl_handle& fd, const hidl_vec<hidl_string>& options) override;
 
diff --git a/audio/core/all-versions/default/include/core/default/StreamIn.h b/audio/core/all-versions/default/include/core/default/StreamIn.h
index 6209b8f..24f9944 100644
--- a/audio/core/all-versions/default/include/core/default/StreamIn.h
+++ b/audio/core/all-versions/default/include/core/default/StreamIn.h
@@ -120,7 +120,6 @@
                                          uint64_t* time);
 
    private:
-    bool mIsClosed;
     const sp<Device> mDevice;
     audio_stream_in_t* mStream;
     const sp<Stream> mStreamCommon;
diff --git a/audio/core/all-versions/default/include/core/default/StreamOut.h b/audio/core/all-versions/default/include/core/default/StreamOut.h
index b098005..6334785 100644
--- a/audio/core/all-versions/default/include/core/default/StreamOut.h
+++ b/audio/core/all-versions/default/include/core/default/StreamOut.h
@@ -126,7 +126,6 @@
                                               TimeSpec* timeStamp);
 
    private:
-    bool mIsClosed;
     const sp<Device> mDevice;
     audio_stream_out_t* mStream;
     const sp<Stream> mStreamCommon;
diff --git a/audio/core/all-versions/vts/functional/4.0/AudioPrimaryHidlHalTest.cpp b/audio/core/all-versions/vts/functional/4.0/AudioPrimaryHidlHalTest.cpp
index e267a5e..709b7cd 100644
--- a/audio/core/all-versions/vts/functional/4.0/AudioPrimaryHidlHalTest.cpp
+++ b/audio/core/all-versions/vts/functional/4.0/AudioPrimaryHidlHalTest.cpp
@@ -22,18 +22,16 @@
         GTEST_SKIP() << "No primary device on this factory";  // returns
     }
 
-    struct WaitExecutor {
-        ~WaitExecutor() { DeviceManager::waitForInstanceDestruction(); }
-    } waitExecutor;  // Make sure we wait for the device destruction on exiting from the test.
-    Result result;
-    sp<IDevice> baseDevice;
-    ASSERT_OK(getDevicesFactory()->openDevice("primary", returnIn(result, baseDevice)));
-    ASSERT_OK(result);
-    ASSERT_TRUE(baseDevice != nullptr);
-
-    Return<sp<IPrimaryDevice>> primaryDevice = IPrimaryDevice::castFrom(baseDevice);
-    ASSERT_TRUE(primaryDevice.isOk());
-    ASSERT_TRUE(sp<IPrimaryDevice>(primaryDevice) != nullptr);
+    {  // Scope for device SPs
+        sp<IDevice> baseDevice =
+                DeviceManager::getInstance().get(getFactoryName(), DeviceManager::kPrimaryDevice);
+        ASSERT_TRUE(baseDevice != nullptr);
+        Return<sp<IPrimaryDevice>> primaryDevice = IPrimaryDevice::castFrom(baseDevice);
+        EXPECT_TRUE(primaryDevice.isOk());
+        EXPECT_TRUE(sp<IPrimaryDevice>(primaryDevice) != nullptr);
+    }
+    EXPECT_TRUE(
+            DeviceManager::getInstance().reset(getFactoryName(), DeviceManager::kPrimaryDevice));
 }
 
 //////////////////////////////////////////////////////////////////////////////
@@ -54,7 +52,6 @@
         doc::test(
             "Make sure getMicrophones always succeeds"
             "and getActiveMicrophones always succeeds when recording from these microphones.");
-        AudioIoHandle ioHandle = (AudioIoHandle)AudioHandleConsts::AUDIO_IO_HANDLE_NONE;
         AudioConfig config{};
         config.channelMask = mkEnumBitfield(AudioChannelMask::IN_MONO);
         config.sampleRateHz = 8000;
@@ -67,18 +64,14 @@
                 continue;
             }
             sp<IStreamIn> stream;
+            StreamHelper<IStreamIn> helper(stream);
             AudioConfig suggestedConfig{};
-            ASSERT_OK(getDevice()->openInputStream(ioHandle, microphone.deviceAddress, config,
-                                                   flags, initMetadata,
-                                                   returnIn(res, stream, suggestedConfig)));
-            if (res != Result::OK) {
-                ASSERT_TRUE(stream == nullptr);
-                AudioConfig suggestedConfigRetry{};
-                ASSERT_OK(getDevice()->openInputStream(
-                        ioHandle, microphone.deviceAddress, suggestedConfig, flags, initMetadata,
-                        returnIn(res, stream, suggestedConfigRetry)));
-            }
-            ASSERT_OK(res);
+            ASSERT_NO_FATAL_FAILURE(helper.open(
+                    [&](AudioIoHandle handle, AudioConfig config, auto cb) {
+                        return getDevice()->openInputStream(handle, microphone.deviceAddress,
+                                                            config, flags, initMetadata, cb);
+                    },
+                    config, &res, &suggestedConfig));
             hidl_vec<MicrophoneInfo> activeMicrophones;
             Result readRes;
             typedef MessageQueue<IStreamIn::ReadParameters, kSynchronizedReadWrite> CommandMQ;
@@ -112,11 +105,8 @@
                 ASSERT_OK(res);
                 ASSERT_NE(0U, activeMicrophones.size());
             }
-            stream->close();
-            // Workaround for b/139329877. Ensures the stream gets closed on the audio hal side.
-            stream.clear();
-            IPCThreadState::self()->flushCommands();
-            usleep(1000);
+            helper.close(true /*clear*/, &res);
+            ASSERT_OK(res);
             if (efGroup) {
                 EventFlag::deleteEventFlag(&efGroup);
             }
diff --git a/audio/core/all-versions/vts/functional/6.0/AudioPrimaryHidlHalTest.cpp b/audio/core/all-versions/vts/functional/6.0/AudioPrimaryHidlHalTest.cpp
index 30f8a7a..22e60be 100644
--- a/audio/core/all-versions/vts/functional/6.0/AudioPrimaryHidlHalTest.cpp
+++ b/audio/core/all-versions/vts/functional/6.0/AudioPrimaryHidlHalTest.cpp
@@ -100,7 +100,8 @@
                             special = true;
                         }
                         if ((flags & AUDIO_OUTPUT_FLAG_DIRECT) &&
-                            !(flags & AUDIO_OUTPUT_FLAG_HW_AV_SYNC)) {
+                            !(flags &
+                              (AUDIO_OUTPUT_FLAG_HW_AV_SYNC | AUDIO_OUTPUT_FLAG_MMAP_NOIRQ))) {
                             result.emplace_back(device, config,
                                                 AudioOutputFlag(AUDIO_OUTPUT_FLAG_DIRECT));
                             special = true;
@@ -144,3 +145,49 @@
     }();
     return parameters;
 }
+
+TEST_P(AudioHidlDeviceTest, CloseDeviceWithOpenedOutputStreams) {
+    doc::test("Verify that a device can't be closed if there are streams opened");
+    DeviceAddress address{.device = AudioDevice::OUT_DEFAULT};
+    AudioConfig config{};
+    auto flags = hidl_bitfield<AudioOutputFlag>(AudioOutputFlag::NONE);
+    SourceMetadata initMetadata = {{{AudioUsage::MEDIA, AudioContentType::MUSIC, 1 /* gain */}}};
+    sp<IStreamOut> stream;
+    StreamHelper<IStreamOut> helper(stream);
+    AudioConfig suggestedConfig{};
+    ASSERT_NO_FATAL_FAILURE(helper.open(
+            [&](AudioIoHandle handle, AudioConfig config, auto cb) {
+                return getDevice()->openOutputStream(handle, address, config, flags, initMetadata,
+                                                     cb);
+            },
+            config, &res, &suggestedConfig));
+    ASSERT_RESULT(Result::INVALID_STATE, getDevice()->close());
+    ASSERT_NO_FATAL_FAILURE(helper.close(true /*clear*/, &res));
+    ASSERT_OK(getDevice()->close());
+    ASSERT_TRUE(resetDevice());
+}
+
+TEST_P(AudioHidlDeviceTest, CloseDeviceWithOpenedInputStreams) {
+    doc::test("Verify that a device can't be closed if there are streams opened");
+    auto module = getCachedPolicyConfig().getModuleFromName(getDeviceName());
+    if (module->getInputProfiles().empty()) {
+        GTEST_SKIP() << "Device doesn't have input profiles";
+    }
+    DeviceAddress address{.device = AudioDevice::IN_DEFAULT};
+    AudioConfig config{};
+    auto flags = hidl_bitfield<AudioInputFlag>(AudioInputFlag::NONE);
+    SinkMetadata initMetadata = {{{.source = AudioSource::MIC, .gain = 1}}};
+    sp<IStreamIn> stream;
+    StreamHelper<IStreamIn> helper(stream);
+    AudioConfig suggestedConfig{};
+    ASSERT_NO_FATAL_FAILURE(helper.open(
+            [&](AudioIoHandle handle, AudioConfig config, auto cb) {
+                return getDevice()->openInputStream(handle, address, config, flags, initMetadata,
+                                                    cb);
+            },
+            config, &res, &suggestedConfig));
+    ASSERT_RESULT(Result::INVALID_STATE, getDevice()->close());
+    ASSERT_NO_FATAL_FAILURE(helper.close(true /*clear*/, &res));
+    ASSERT_OK(getDevice()->close());
+    ASSERT_TRUE(resetDevice());
+}
diff --git a/audio/core/all-versions/vts/functional/AudioPrimaryHidlHalTest.h b/audio/core/all-versions/vts/functional/AudioPrimaryHidlHalTest.h
index 468f9b2..d0d39e8 100644
--- a/audio/core/all-versions/vts/functional/AudioPrimaryHidlHalTest.h
+++ b/audio/core/all-versions/vts/functional/AudioPrimaryHidlHalTest.h
@@ -58,7 +58,6 @@
 
 #include "utility/AssertOk.h"
 #include "utility/Documentation.h"
-#include "utility/PrettyPrintAudioTypes.h"
 #include "utility/ReturnIn.h"
 #include "utility/ValidateXml.h"
 
@@ -719,12 +718,6 @@
                 ::testing::Values(AudioInputFlag::NONE)),
         &DeviceConfigParameterToString);
 INSTANTIATE_TEST_CASE_P(
-        SupportedInputBufferSize, RequiredInputBufferSizeTest,
-        ::testing::Combine(::testing::ValuesIn(getDeviceParameters()),
-                           ::testing::ValuesIn(ConfigHelper::getSupportedCaptureAudioConfig()),
-                           ::testing::Values(AudioInputFlag::NONE)),
-        &DeviceConfigParameterToString);
-INSTANTIATE_TEST_CASE_P(
         RecommendedCaptureAudioConfigSupport, OptionalInputBufferSizeTest,
         ::testing::Combine(
                 ::testing::ValuesIn(getDeviceParametersForPrimaryDeviceTests()),
@@ -816,60 +809,84 @@
 ////////////////////////// open{Output,Input}Stream //////////////////////////
 //////////////////////////////////////////////////////////////////////////////
 
+// This class is also used by some device tests.
 template <class Stream>
-class OpenStreamTest : public AudioHidlTestWithDeviceConfigParameter {
-  protected:
+class StreamHelper {
+  public:
+    // StreamHelper doesn't own the stream, this is for simpler stream lifetime management.
+    explicit StreamHelper(sp<Stream>& stream) : mStream(stream) {}
     template <class Open>
-    void testOpen(Open openStream, const AudioConfig& config) {
+    void open(Open openStream, const AudioConfig& config, Result* res,
+              AudioConfig* suggestedConfigPtr) {
         // FIXME: Open a stream without an IOHandle
         //        This is not required to be accepted by hal implementations
         AudioIoHandle ioHandle = (AudioIoHandle)AudioHandleConsts::AUDIO_IO_HANDLE_NONE;
         AudioConfig suggestedConfig{};
-        ASSERT_OK(openStream(ioHandle, config, returnIn(res, stream, suggestedConfig)));
-
-        // TODO: only allow failure for RecommendedPlaybackAudioConfig
-        switch (res) {
+        bool retryWithSuggestedConfig = true;
+        if (suggestedConfigPtr == nullptr) {
+            suggestedConfigPtr = &suggestedConfig;
+            retryWithSuggestedConfig = false;
+        }
+        ASSERT_OK(openStream(ioHandle, config, returnIn(*res, mStream, *suggestedConfigPtr)));
+        switch (*res) {
             case Result::OK:
-                ASSERT_TRUE(stream != nullptr);
-                audioConfig = config;
+                ASSERT_TRUE(mStream != nullptr);
+                *suggestedConfigPtr = config;
                 break;
             case Result::INVALID_ARGUMENTS:
-                ASSERT_TRUE(stream == nullptr);
-                AudioConfig suggestedConfigRetry;
-                // Could not open stream with config, try again with the
-                // suggested one
-                ASSERT_OK(openStream(ioHandle, suggestedConfig,
-                                     returnIn(res, stream, suggestedConfigRetry)));
-                // This time it must succeed
-                ASSERT_OK(res);
-                ASSERT_TRUE(stream != nullptr);
-                audioConfig = suggestedConfig;
+                ASSERT_TRUE(mStream == nullptr);
+                if (retryWithSuggestedConfig) {
+                    AudioConfig suggestedConfigRetry;
+                    ASSERT_OK(openStream(ioHandle, *suggestedConfigPtr,
+                                         returnIn(*res, mStream, suggestedConfigRetry)));
+                    ASSERT_OK(*res);
+                    ASSERT_TRUE(mStream != nullptr);
+                }
                 break;
             default:
-                FAIL() << "Invalid return status: " << ::testing::PrintToString(res);
+                FAIL() << "Invalid return status: " << ::testing::PrintToString(*res);
         }
+    }
+    void close(bool clear, Result* res) {
+        auto ret = mStream->close();
+        EXPECT_TRUE(ret.isOk());
+        *res = ret;
+        if (clear) {
+            mStream.clear();
+#if MAJOR_VERSION <= 5
+            // FIXME: there is no way to know when the remote IStream is being destroyed
+            //        Binder does not support testing if an object is alive, thus
+            //        wait for 100ms to let the binder destruction propagates and
+            //        the remote device has the time to be destroyed.
+            //        flushCommand makes sure all local command are sent, thus should reduce
+            //        the latency between local and remote destruction.
+            IPCThreadState::self()->flushCommands();
+            usleep(100 * 1000);
+#endif
+        }
+    }
+
+  private:
+    sp<Stream>& mStream;
+};
+
+template <class Stream>
+class OpenStreamTest : public AudioHidlTestWithDeviceConfigParameter {
+  protected:
+    OpenStreamTest() : AudioHidlTestWithDeviceConfigParameter(), helper(stream) {}
+    template <class Open>
+    void testOpen(Open openStream, const AudioConfig& config) {
+        // TODO: only allow failure for RecommendedPlaybackAudioConfig
+        ASSERT_NO_FATAL_FAILURE(helper.open(openStream, config, &res, &audioConfig));
         open = true;
     }
 
-    Return<Result> closeStream() {
+    Result closeStream(bool clear = true) {
         open = false;
-        auto res = stream->close();
-        stream.clear();
-        waitForStreamDestruction();
+        helper.close(clear, &res);
         return res;
     }
 
-    static void waitForStreamDestruction() {
-        // FIXME: there is no way to know when the remote IStream is being destroyed
-        //        Binder does not support testing if an object is alive, thus
-        //        wait for 100ms to let the binder destruction propagates and
-        //        the remote device has the time to be destroyed.
-        //        flushCommand makes sure all local command are sent, thus should reduce
-        //        the latency between local and remote destruction.
-        IPCThreadState::self()->flushCommands();
-        usleep(100 * 1000);
-    }
-
   private:
     void TearDown() override {
         if (open) {
@@ -882,6 +899,7 @@
     AudioConfig audioConfig;
     DeviceAddress address = {};
     sp<Stream> stream;
+    StreamHelper<Stream> helper;
     bool open = false;
 };
 
@@ -930,12 +948,6 @@
                 ::testing::Values(AudioOutputFlag::NONE)),
         &DeviceConfigParameterToString);
 INSTANTIATE_TEST_CASE_P(
-        SupportedOutputStreamConfig, OutputStreamTest,
-        ::testing::Combine(::testing::ValuesIn(getDeviceParameters()),
-                           ::testing::ValuesIn(ConfigHelper::getSupportedPlaybackAudioConfig()),
-                           ::testing::Values(AudioOutputFlag::NONE)),
-        &DeviceConfigParameterToString);
-INSTANTIATE_TEST_CASE_P(
         RecommendedOutputStreamConfigSupport, OutputStreamTest,
         ::testing::Combine(
                 ::testing::ValuesIn(getDeviceParametersForPrimaryDeviceTests()),
@@ -945,7 +957,7 @@
 #elif MAJOR_VERSION >= 6
 // For V6 and above test according to the audio policy manager configuration.
 // This is more correct as CDD is written from the apps perspective.
-// Audio system provides necessary format conversions for the missing configurations.
+// Audio system provides necessary format conversions for missing configurations.
 INSTANTIATE_TEST_CASE_P(DeclaredOutputStreamConfigSupport, OutputStreamTest,
                         ::testing::ValuesIn(getOutputDeviceConfigParameters()),
                         &DeviceConfigParameterToString);
@@ -991,12 +1003,6 @@
                 ::testing::Values(AudioInputFlag::NONE)),
         &DeviceConfigParameterToString);
 INSTANTIATE_TEST_CASE_P(
-        SupportedInputStreamConfig, InputStreamTest,
-        ::testing::Combine(::testing::ValuesIn(getDeviceParameters()),
-                           ::testing::ValuesIn(ConfigHelper::getSupportedCaptureAudioConfig()),
-                           ::testing::Values(AudioInputFlag::NONE)),
-        &DeviceConfigParameterToString);
-INSTANTIATE_TEST_CASE_P(
         RecommendedInputStreamConfigSupport, InputStreamTest,
         ::testing::Combine(
                 ::testing::ValuesIn(getDeviceParametersForPrimaryDeviceTests()),
@@ -1006,7 +1012,7 @@
 #elif MAJOR_VERSION >= 6
 // For V6 and above test according to the audio policy manager configuration.
 // This is more correct as CDD is written from the apps perspective.
-// Audio system provides necessary format conversions for the missing configurations.
+// Audio system provides necessary format conversions for missing configurations.
 INSTANTIATE_TEST_CASE_P(DeclaredInputStreamConfigSupport, InputStreamTest,
                         ::testing::ValuesIn(getInputDeviceConfigParameters()),
                         &DeviceConfigParameterToString);
@@ -1195,26 +1201,21 @@
 TEST_IO_STREAM(close, "Make sure a stream can be closed", ASSERT_OK(closeStream()))
 // clang-format off
 TEST_IO_STREAM(closeTwice, "Make sure a stream can not be closed twice",
-        auto streamCopy = stream;
-        ASSERT_OK(closeStream());
-        ASSERT_RESULT(Result::INVALID_STATE, streamCopy->close());
-        streamCopy.clear();
-        waitForStreamDestruction())
+        ASSERT_OK(closeStream(false /*clear*/));
+        ASSERT_EQ(Result::INVALID_STATE, closeStream()))
 // clang-format on
 
-static void testCreateTooBigMmapBuffer(IStream* stream) {
-    MmapBufferInfo info;
-    Result res;
-    // Assume that int max is a value too big to be allocated
-    // This is true currently with a 32bit media server, but might not when it
-    // will run in 64 bit
-    auto minSizeFrames = std::numeric_limits<int32_t>::max();
-    ASSERT_OK(stream->createMmapBuffer(minSizeFrames, returnIn(res, info)));
-    ASSERT_RESULT(invalidArgsOrNotSupported, res);
+static void testMmapBufferOfInvalidSize(IStream* stream) {
+    for (int32_t value : {-1, 0, std::numeric_limits<int32_t>::max()}) {
+        MmapBufferInfo info;
+        Result res;
+        EXPECT_OK(stream->createMmapBuffer(value, returnIn(res, info)));
+        EXPECT_RESULT(invalidArgsOrNotSupported, res) << "value=" << value;
+    }
 }
 
-TEST_IO_STREAM(CreateTooBigMmapBuffer, "Create mmap buffer too big should fail",
-               testCreateTooBigMmapBuffer(stream.get()))
+TEST_IO_STREAM(CreateTooBigMmapBuffer, "Create mmap buffer of invalid size must fail",
+               testMmapBufferOfInvalidSize(stream.get()))
 
 static void testGetMmapPositionOfNonMmapedStream(IStream* stream) {
     Result res;
diff --git a/audio/core/all-versions/vts/functional/ConfigHelper.h b/audio/core/all-versions/vts/functional/ConfigHelper.h
index 48aae8c..a2f4116 100644
--- a/audio/core/all-versions/vts/functional/ConfigHelper.h
+++ b/audio/core/all-versions/vts/functional/ConfigHelper.h
@@ -57,12 +57,6 @@
                                   {24000, 48000}, {AudioFormat::PCM_16_BIT});
     }
 
-    static const vector<AudioConfig> getSupportedPlaybackAudioConfig() {
-        // TODO: retrieve audio config supported by the platform
-        // as declared in the policy configuration
-        return {};
-    }
-
     static const vector<AudioConfig> getRequiredSupportCaptureAudioConfig() {
         if (!primaryHasMic()) return {};
         return combineAudioConfig({AudioChannelMask::IN_MONO}, {8000, 11025, 16000, 44100},
@@ -73,11 +67,6 @@
         return combineAudioConfig({AudioChannelMask::IN_STEREO}, {22050, 48000},
                                   {AudioFormat::PCM_16_BIT});
     }
-    static const vector<AudioConfig> getSupportedCaptureAudioConfig() {
-        // TODO: retrieve audio config supported by the platform
-        // as declared in the policy configuration
-        return {};
-    }
 
     static vector<AudioConfig> combineAudioConfig(vector<audio_channel_mask_t> channelMasks,
                                                   vector<uint32_t> sampleRates,
diff --git a/audio/core/all-versions/vts/functional/DeviceManager.h b/audio/core/all-versions/vts/functional/DeviceManager.h
index b6e2db0..d849f85 100644
--- a/audio/core/all-versions/vts/functional/DeviceManager.h
+++ b/audio/core/all-versions/vts/functional/DeviceManager.h
@@ -22,25 +22,33 @@
 template <class Derived, class Key, class Interface>
 class InterfaceManager {
   public:
+    sp<Interface> getExisting(const Key& name) {
+        auto existing = instances.find(name);
+        return existing != instances.end() ? existing->second : sp<Interface>();
+    }
+
     sp<Interface> get(const Key& name) {
         auto existing = instances.find(name);
         if (existing != instances.end()) return existing->second;
         auto [inserted, _] = instances.emplace(name, Derived::createInterfaceInstance(name));
         if (inserted->second) {
-            environment->registerTearDown([name]() { (void)Derived::getInstance().reset(name); });
+            environment->registerTearDown(
+                    [name]() { (void)Derived::getInstance().reset(name, false); });
         }
         return inserted->second;
     }
 
     // The test must check that reset was successful. Reset failure means that the test code
     // is holding a strong reference to the device.
-    bool reset(const Key& name) __attribute__((warn_unused_result)) {
+    bool reset(const Key& name, bool waitForDestruction) __attribute__((warn_unused_result)) {
         auto iter = instances.find(name);
         if (iter == instances.end()) return true;
         ::android::wp<Interface> weak = iter->second;
         instances.erase(iter);
         if (weak.promote() != nullptr) return false;
-        waitForInstanceDestruction();
+        if (waitForDestruction) {
+            waitForInstanceDestruction();
+        }
         return true;
     }
 
@@ -100,7 +108,15 @@
     }
     bool reset(const std::string& factoryName, const std::string& name)
             __attribute__((warn_unused_result)) {
-        return InterfaceManager::reset(std::make_tuple(factoryName, name));
+#if MAJOR_VERSION <= 5
+        return InterfaceManager::reset(std::make_tuple(factoryName, name), true);
+#elif MAJOR_VERSION >= 6
+        {
+            sp<IDevice> device = getExisting(std::make_tuple(factoryName, name));
+            if (device != nullptr) device->close();
+        }
+        return InterfaceManager::reset(std::make_tuple(factoryName, name), false);
+#endif
     }
     bool resetPrimary(const std::string& factoryName) __attribute__((warn_unused_result)) {
         return reset(factoryName, kPrimaryDevice);
diff --git a/audio/effect/6.0/IEffect.hal b/audio/effect/6.0/IEffect.hal
index b35afee..f4c50a2 100644
--- a/audio/effect/6.0/IEffect.hal
+++ b/audio/effect/6.0/IEffect.hal
@@ -407,9 +407,12 @@
 
     /**
      * Called by the framework to deinitialize the effect and free up
-     * all the currently allocated resources. It is recommended to close
+     * all currently allocated resources. It is recommended to close
      * the effect on the client side as soon as it is becomes unused.
      *
+     * The client must ensure that this function is not called while
+     * audio data is being transferred through the effect's message queues.
+     *
      * @return retval OK in case the success.
      *                INVALID_STATE if the effect was already closed.
      */
diff --git a/audio/effect/all-versions/default/Effect.cpp b/audio/effect/all-versions/default/Effect.cpp
index 3c0d878..0afa779 100644
--- a/audio/effect/all-versions/default/Effect.cpp
+++ b/audio/effect/all-versions/default/Effect.cpp
@@ -138,11 +138,11 @@
 const char* Effect::sContextCallFunction = sContextCallToCommand;
 
 Effect::Effect(effect_handle_t handle)
-    : mIsClosed(false), mHandle(handle), mEfGroup(nullptr), mStopProcessThread(false) {}
+    : mHandle(handle), mEfGroup(nullptr), mStopProcessThread(false) {}
 
 Effect::~Effect() {
     ATRACE_CALL();
-    close();
+    (void)close();
     if (mProcessThread.get()) {
         ATRACE_NAME("mProcessThread->join");
         status_t status = mProcessThread->join();
@@ -154,8 +154,10 @@
     }
     mInBuffer.clear();
     mOutBuffer.clear();
+#if MAJOR_VERSION <= 5
     int status = EffectRelease(mHandle);
     ALOGW_IF(status, "Error releasing effect %p: %s", mHandle, strerror(-status));
+#endif
     EffectMap::getInstance().remove(mHandle);
     mHandle = 0;
 }
@@ -699,15 +701,20 @@
 }
 
 Return<Result> Effect::close() {
-    if (mIsClosed) return Result::INVALID_STATE;
-    mIsClosed = true;
-    if (mProcessThread.get()) {
-        mStopProcessThread.store(true, std::memory_order_release);
+    if (mStopProcessThread.load(std::memory_order_relaxed)) {  // only this thread modifies
+        return Result::INVALID_STATE;
     }
+    mStopProcessThread.store(true, std::memory_order_release);
     if (mEfGroup) {
         mEfGroup->wake(static_cast<uint32_t>(MessageQueueFlagBits::REQUEST_QUIT));
     }
+#if MAJOR_VERSION <= 5
     return Result::OK;
+#elif MAJOR_VERSION >= 6
+    // No need to join the processing thread, it is part of the API contract that the client
+    // must finish processing before closing the effect.
+    return analyzeStatus("EffectRelease", "", sContextCallFunction, EffectRelease(mHandle));
+#endif
 }
 
 Return<void> Effect::debug(const hidl_handle& fd, const hidl_vec<hidl_string>& /* options */) {
diff --git a/audio/effect/all-versions/default/Effect.h b/audio/effect/all-versions/default/Effect.h
index 3d99a0e..181e542 100644
--- a/audio/effect/all-versions/default/Effect.h
+++ b/audio/effect/all-versions/default/Effect.h
@@ -170,7 +170,6 @@
     static const char* sContextCallToCommand;
     static const char* sContextCallFunction;
 
-    bool mIsClosed;
     effect_handle_t mHandle;
     sp<AudioBufferWrapper> mInBuffer;
     sp<AudioBufferWrapper> mOutBuffer;
diff --git a/biometrics/face/1.0/vts/functional/Android.bp b/biometrics/face/1.0/vts/functional/Android.bp
index fa68c4e..f2598a7 100644
--- a/biometrics/face/1.0/vts/functional/Android.bp
+++ b/biometrics/face/1.0/vts/functional/Android.bp
@@ -19,6 +19,6 @@
     defaults: ["VtsHalTargetTestDefaults"],
     srcs: ["VtsHalBiometricsFaceV1_0TargetTest.cpp"],
     static_libs: ["android.hardware.biometrics.face@1.0"],
-    test_suites: ["general-tests"],
+    test_suites: ["general-tests", "vts-core"],
 }
 
diff --git a/biometrics/face/1.0/vts/functional/VtsHalBiometricsFaceV1_0TargetTest.cpp b/biometrics/face/1.0/vts/functional/VtsHalBiometricsFaceV1_0TargetTest.cpp
index a4e95ed..7ac44a4 100644
--- a/biometrics/face/1.0/vts/functional/VtsHalBiometricsFaceV1_0TargetTest.cpp
+++ b/biometrics/face/1.0/vts/functional/VtsHalBiometricsFaceV1_0TargetTest.cpp
@@ -20,9 +20,10 @@
 #include <android/hardware/biometrics/face/1.0/IBiometricsFaceClientCallback.h>
 
 #include <VtsHalHidlTargetCallbackBase.h>
-#include <VtsHalHidlTargetTestBase.h>
-#include <VtsHalHidlTargetTestEnvBase.h>
 #include <android-base/logging.h>
+#include <gtest/gtest.h>
+#include <hidl/GtestPrinter.h>
+#include <hidl/ServiceManagement.h>
 
 #include <chrono>
 #include <cstdint>
@@ -124,27 +125,11 @@
     }
 };
 
-// Test environment for the BiometricsFace HAL.
-class FaceHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
-  public:
-    // Get the test environment singleton.
-    static FaceHidlEnvironment* Instance() {
-        static FaceHidlEnvironment* instance = new FaceHidlEnvironment;
-        return instance;
-    }
-
-    void registerTestServices() override { registerTestService<IBiometricsFace>(); }
-
-  private:
-    FaceHidlEnvironment() = default;
-};
-
 // Test class for the BiometricsFace HAL.
-class FaceHidlTest : public ::testing::VtsHalHidlTargetTestBase {
+class FaceHidlTest : public ::testing::TestWithParam<std::string> {
   public:
     void SetUp() override {
-        mService = ::testing::VtsHalHidlTargetTestBase::getService<IBiometricsFace>(
-                FaceHidlEnvironment::Instance()->getServiceName<IBiometricsFace>());
+        mService = IBiometricsFace::getService(GetParam());
         ASSERT_NE(mService, nullptr);
         mCallback = new FaceCallback();
         mCallback->SetWaitTimeoutDefault(kTimeout);
@@ -167,7 +152,7 @@
 
 // generateChallenge should always return a unique, cryptographically secure,
 // non-zero number.
-TEST_F(FaceHidlTest, GenerateChallengeTest) {
+TEST_P(FaceHidlTest, GenerateChallengeTest) {
     std::map<uint64_t, int> m;
     for (int i = 0; i < kGenerateChallengeIterations; ++i) {
         Return<void> ret =
@@ -182,7 +167,7 @@
 }
 
 // enroll with an invalid (all zeroes) HAT should fail.
-TEST_F(FaceHidlTest, EnrollZeroHatTest) {
+TEST_P(FaceHidlTest, EnrollZeroHatTest) {
     // Filling HAT with zeros
     hidl_vec<uint8_t> token(69);
     for (size_t i = 0; i < 69; i++) {
@@ -200,7 +185,7 @@
 }
 
 // enroll with an invalid HAT should fail.
-TEST_F(FaceHidlTest, EnrollGarbageHatTest) {
+TEST_P(FaceHidlTest, EnrollGarbageHatTest) {
     // Filling HAT with pseudorandom invalid data.
     // Using default seed to make the test reproducible.
     std::mt19937 gen(std::mt19937::default_seed);
@@ -221,7 +206,7 @@
 }
 
 // setFeature with an invalid (all zeros) HAT should fail.
-TEST_F(FaceHidlTest, SetFeatureZeroHatTest) {
+TEST_P(FaceHidlTest, SetFeatureZeroHatTest) {
     hidl_vec<uint8_t> token(69);
     for (size_t i = 0; i < 69; i++) {
         token[i] = 0;
@@ -232,7 +217,7 @@
 }
 
 // setFeature with an invalid HAT should fail.
-TEST_F(FaceHidlTest, SetFeatureGarbageHatTest) {
+TEST_P(FaceHidlTest, SetFeatureGarbageHatTest) {
     // Filling HAT with pseudorandom invalid data.
     // Using default seed to make the test reproducible.
     std::mt19937 gen(std::mt19937::default_seed);
@@ -254,16 +239,16 @@
     ASSERT_TRUE(res.isOk());
 }
 
-TEST_F(FaceHidlTest, GetFeatureRequireAttentionTest) {
+TEST_P(FaceHidlTest, GetFeatureRequireAttentionTest) {
     assertGetFeatureFails(mService, 0 /* faceId */, Feature::REQUIRE_ATTENTION);
 }
 
-TEST_F(FaceHidlTest, GetFeatureRequireDiversityTest) {
+TEST_P(FaceHidlTest, GetFeatureRequireDiversityTest) {
     assertGetFeatureFails(mService, 0 /* faceId */, Feature::REQUIRE_DIVERSITY);
 }
 
 // revokeChallenge should always return within the timeout
-TEST_F(FaceHidlTest, RevokeChallengeTest) {
+TEST_P(FaceHidlTest, RevokeChallengeTest) {
     auto start = std::chrono::system_clock::now();
     Return<Status> ret = mService->revokeChallenge();
     auto elapsed = std::chrono::system_clock::now() - start;
@@ -272,14 +257,14 @@
 }
 
 // The call to getAuthenticatorId should succeed.
-TEST_F(FaceHidlTest, GetAuthenticatorIdTest) {
+TEST_P(FaceHidlTest, GetAuthenticatorIdTest) {
     Return<void> ret = mService->getAuthenticatorId(
             [](const OptionalUint64& res) { ASSERT_EQ(Status::OK, res.status); });
     ASSERT_TRUE(ret.isOk());
 }
 
 // The call to enumerate should succeed.
-TEST_F(FaceHidlTest, EnumerateTest) {
+TEST_P(FaceHidlTest, EnumerateTest) {
     Return<Status> ret = mService->enumerate();
     ASSERT_EQ(Status::OK, static_cast<Status>(ret));
     auto res = mCallback->WaitForCallback(kCallbackNameOnEnumerate);
@@ -288,21 +273,21 @@
 }
 
 // The call to remove should succeed for any faceId
-TEST_F(FaceHidlTest, RemoveFaceTest) {
+TEST_P(FaceHidlTest, RemoveFaceTest) {
     // Remove a face
     Return<Status> ret = mService->remove(kFaceId);
     ASSERT_EQ(Status::OK, static_cast<Status>(ret));
 }
 
 // Remove should accept 0 to delete all faces
-TEST_F(FaceHidlTest, RemoveAllFacesTest) {
+TEST_P(FaceHidlTest, RemoveAllFacesTest) {
     // Remove all faces
     Return<Status> ret = mService->remove(0);
     ASSERT_EQ(Status::OK, static_cast<Status>(ret));
 }
 
 // Active user should successfully set to a writable location.
-TEST_F(FaceHidlTest, SetActiveUserTest) {
+TEST_P(FaceHidlTest, SetActiveUserTest) {
     // Create an active user
     Return<Status> ret = mService->setActiveUser(2, kFacedataDir);
     ASSERT_EQ(Status::OK, static_cast<Status>(ret));
@@ -313,7 +298,7 @@
 }
 
 // Active user should fail to set to an unwritable location.
-TEST_F(FaceHidlTest, SetActiveUserUnwritableTest) {
+TEST_P(FaceHidlTest, SetActiveUserUnwritableTest) {
     // Create an active user to an unwritable location (device root dir)
     Return<Status> ret = mService->setActiveUser(3, "/");
     ASSERT_NE(Status::OK, static_cast<Status>(ret));
@@ -324,7 +309,7 @@
 }
 
 // Active user should fail to set to a null location.
-TEST_F(FaceHidlTest, SetActiveUserNullTest) {
+TEST_P(FaceHidlTest, SetActiveUserNullTest) {
     // Create an active user to a null location.
     Return<Status> ret = mService->setActiveUser(4, nullptr);
     ASSERT_NE(Status::OK, static_cast<Status>(ret));
@@ -336,7 +321,7 @@
 
 // Cancel should always return CANCELED from any starting state including
 // the IDLE state.
-TEST_F(FaceHidlTest, CancelTest) {
+TEST_P(FaceHidlTest, CancelTest) {
     Return<Status> ret = mService->cancel();
     // check that we were able to make an IPC request successfully
     ASSERT_EQ(Status::OK, static_cast<Status>(ret));
@@ -347,7 +332,7 @@
     EXPECT_EQ(FaceError::CANCELED, res.args->error);
 }
 
-TEST_F(FaceHidlTest, OnLockoutChangedTest) {
+TEST_P(FaceHidlTest, OnLockoutChangedTest) {
     // Update active user and ensure onLockoutChanged was called.
     Return<Status> ret = mService->setActiveUser(kUserId + 1, kFacedataDir);
     ASSERT_EQ(Status::OK, static_cast<Status>(ret));
@@ -359,11 +344,7 @@
 
 }  // anonymous namespace
 
-int main(int argc, char** argv) {
-    ::testing::AddGlobalTestEnvironment(FaceHidlEnvironment::Instance());
-    ::testing::InitGoogleTest(&argc, argv);
-    FaceHidlEnvironment::Instance()->init(&argc, argv);
-    int status = RUN_ALL_TESTS();
-    LOG(INFO) << "Test result = " << status;
-    return status;
-}
+INSTANTIATE_TEST_SUITE_P(
+        PerInstance, FaceHidlTest,
+        testing::ValuesIn(android::hardware::getAllHalInstanceNames(IBiometricsFace::descriptor)),
+        android::hardware::PrintInstanceNameToString);
diff --git a/bluetooth/1.0/default/bluetooth_hci.cc b/bluetooth/1.0/default/bluetooth_hci.cc
index e14e3d7..a2211f4 100644
--- a/bluetooth/1.0/default/bluetooth_hci.cc
+++ b/bluetooth/1.0/default/bluetooth_hci.cc
@@ -89,6 +89,9 @@
         if (!hidl_status.isOk()) {
           ALOGE("VendorInterface -> Unable to call scoDataReceived()");
         }
+      },
+      [cb](const hidl_vec<uint8_t>&) {
+        ALOGE("VendorInterface -> No callback for ISO packets in HAL V1_0");
       });
   if (!rc) {
     auto hidl_status = cb->initializationComplete(Status::INITIALIZATION_ERROR);
diff --git a/bluetooth/1.0/default/h4_protocol.cc b/bluetooth/1.0/default/h4_protocol.cc
index 98e3273..8c24f76 100644
--- a/bluetooth/1.0/default/h4_protocol.cc
+++ b/bluetooth/1.0/default/h4_protocol.cc
@@ -58,6 +58,9 @@
     case HCI_PACKET_TYPE_SCO_DATA:
       sco_cb_(hci_packetizer_.GetPacket());
       break;
+    case HCI_PACKET_TYPE_ISO_DATA:
+      iso_cb_(hci_packetizer_.GetPacket());
+      break;
     default:
       LOG_ALWAYS_FATAL("%s: Unimplemented packet type %d", __func__,
                        static_cast<int>(hci_packet_type_));
diff --git a/bluetooth/1.0/default/h4_protocol.h b/bluetooth/1.0/default/h4_protocol.h
index 0d0a1ca..0c82fd6 100644
--- a/bluetooth/1.0/default/h4_protocol.h
+++ b/bluetooth/1.0/default/h4_protocol.h
@@ -31,11 +31,12 @@
 class H4Protocol : public HciProtocol {
  public:
   H4Protocol(int fd, PacketReadCallback event_cb, PacketReadCallback acl_cb,
-             PacketReadCallback sco_cb)
+             PacketReadCallback sco_cb, PacketReadCallback iso_cb)
       : uart_fd_(fd),
         event_cb_(event_cb),
         acl_cb_(acl_cb),
         sco_cb_(sco_cb),
+        iso_cb_(iso_cb),
         hci_packetizer_([this]() { OnPacketReady(); }) {}
 
   size_t Send(uint8_t type, const uint8_t* data, size_t length);
@@ -50,6 +51,7 @@
   PacketReadCallback event_cb_;
   PacketReadCallback acl_cb_;
   PacketReadCallback sco_cb_;
+  PacketReadCallback iso_cb_;
 
   HciPacketType hci_packet_type_{HCI_PACKET_TYPE_UNKNOWN};
   hci::HciPacketizer hci_packetizer_;
diff --git a/bluetooth/1.0/default/hci_internals.h b/bluetooth/1.0/default/hci_internals.h
index 1e1f300..24e944f 100644
--- a/bluetooth/1.0/default/hci_internals.h
+++ b/bluetooth/1.0/default/hci_internals.h
@@ -24,7 +24,8 @@
   HCI_PACKET_TYPE_COMMAND = 1,
   HCI_PACKET_TYPE_ACL_DATA = 2,
   HCI_PACKET_TYPE_SCO_DATA = 3,
-  HCI_PACKET_TYPE_EVENT = 4
+  HCI_PACKET_TYPE_EVENT = 4,
+  HCI_PACKET_TYPE_ISO_DATA = 5,
 };
 
 // 2 bytes for opcode, 1 byte for parameter length (Volume 2, Part E, 5.4.1)
diff --git a/bluetooth/1.0/default/test/h4_protocol_unittest.cc b/bluetooth/1.0/default/test/h4_protocol_unittest.cc
index ba56c0d..283243d 100644
--- a/bluetooth/1.0/default/test/h4_protocol_unittest.cc
+++ b/bluetooth/1.0/default/test/h4_protocol_unittest.cc
@@ -42,11 +42,15 @@
 static char sample_data1[100] = "A point is that which has no part.";
 static char sample_data2[100] = "A line is breadthless length.";
 static char sample_data3[100] = "The ends of a line are points.";
+static char sample_data4[100] =
+    "A plane surface is a surface which lies evenly with the straight ...";
 static char acl_data[100] =
     "A straight line is a line which lies evenly with the points on itself.";
 static char sco_data[100] =
     "A surface is that which has length and breadth only.";
 static char event_data[100] = "The edges of a surface are lines.";
+static char iso_data[100] =
+    "A plane angle is the inclination to one another of two lines in a ...";
 
 MATCHER_P3(HidlVecMatches, preamble, preamble_length, payload, "") {
   size_t length = strlen(payload) + preamble_length;
@@ -75,9 +79,9 @@
 
     int sockfd[2];
     socketpair(AF_LOCAL, SOCK_STREAM, 0, sockfd);
-    H4Protocol* h4_hci =
-        new H4Protocol(sockfd[0], event_cb_.AsStdFunction(),
-                       acl_cb_.AsStdFunction(), sco_cb_.AsStdFunction());
+    H4Protocol* h4_hci = new H4Protocol(
+        sockfd[0], event_cb_.AsStdFunction(), acl_cb_.AsStdFunction(),
+        sco_cb_.AsStdFunction(), iso_cb_.AsStdFunction());
     fd_watcher_.WatchFdForNonBlockingReads(
         sockfd[0], [h4_hci](int fd) { h4_hci->OnDataReady(fd); });
     protocol_ = h4_hci;
@@ -184,9 +188,35 @@
     }
   }
 
+  void WriteAndExpectInboundIsoData(char* payload) {
+    // h4 type[1] + handle[2] + size[1]
+    char preamble[4] = {HCI_PACKET_TYPE_ISO_DATA, 20, 17, 0};
+    preamble[3] = strlen(payload) & 0xFF;
+
+    ALOGD("%s writing", __func__);
+    TEMP_FAILURE_RETRY(write(fake_uart_, preamble, sizeof(preamble)));
+    TEMP_FAILURE_RETRY(write(fake_uart_, payload, strlen(payload)));
+
+    ALOGD("%s waiting", __func__);
+    std::mutex mutex;
+    std::condition_variable done;
+    EXPECT_CALL(iso_cb_, Call(HidlVecMatches(preamble + 1, sizeof(preamble) - 1,
+                                             payload)))
+        .WillOnce(Notify(&mutex, &done));
+
+    // Fail if it takes longer than 100 ms.
+    auto timeout_time =
+        std::chrono::steady_clock::now() + std::chrono::milliseconds(100);
+    {
+      std::unique_lock<std::mutex> lock(mutex);
+      done.wait_until(lock, timeout_time);
+    }
+  }
+
   testing::MockFunction<void(const hidl_vec<uint8_t>&)> event_cb_;
   testing::MockFunction<void(const hidl_vec<uint8_t>&)> acl_cb_;
   testing::MockFunction<void(const hidl_vec<uint8_t>&)> sco_cb_;
+  testing::MockFunction<void(const hidl_vec<uint8_t>&)> iso_cb_;
   async::AsyncFdWatcher fd_watcher_;
   H4Protocol* protocol_;
   int fake_uart_;
@@ -197,6 +227,7 @@
   SendAndReadUartOutbound(HCI_PACKET_TYPE_COMMAND, sample_data1);
   SendAndReadUartOutbound(HCI_PACKET_TYPE_ACL_DATA, sample_data2);
   SendAndReadUartOutbound(HCI_PACKET_TYPE_SCO_DATA, sample_data3);
+  SendAndReadUartOutbound(HCI_PACKET_TYPE_ISO_DATA, sample_data4);
 }
 
 // Ensure we properly parse data coming from the UART
@@ -204,6 +235,7 @@
   WriteAndExpectInboundAclData(acl_data);
   WriteAndExpectInboundScoData(sco_data);
   WriteAndExpectInboundEvent(event_data);
+  WriteAndExpectInboundIsoData(iso_data);
 }
 
 }  // namespace implementation
diff --git a/bluetooth/1.0/default/vendor_interface.cc b/bluetooth/1.0/default/vendor_interface.cc
index d56e344..d809313 100644
--- a/bluetooth/1.0/default/vendor_interface.cc
+++ b/bluetooth/1.0/default/vendor_interface.cc
@@ -162,14 +162,14 @@
 bool VendorInterface::Initialize(
     InitializeCompleteCallback initialize_complete_cb,
     PacketReadCallback event_cb, PacketReadCallback acl_cb,
-    PacketReadCallback sco_cb) {
+    PacketReadCallback sco_cb, PacketReadCallback iso_cb) {
   if (g_vendor_interface) {
     ALOGE("%s: No previous Shutdown()?", __func__);
     return false;
   }
   g_vendor_interface = new VendorInterface();
   return g_vendor_interface->Open(initialize_complete_cb, event_cb, acl_cb,
-                                  sco_cb);
+                                  sco_cb, iso_cb);
 }
 
 void VendorInterface::Shutdown() {
@@ -185,7 +185,8 @@
 bool VendorInterface::Open(InitializeCompleteCallback initialize_complete_cb,
                            PacketReadCallback event_cb,
                            PacketReadCallback acl_cb,
-                           PacketReadCallback sco_cb) {
+                           PacketReadCallback sco_cb,
+                           PacketReadCallback iso_cb) {
   initialize_complete_cb_ = initialize_complete_cb;
 
   // Initialize vendor interface
@@ -248,7 +249,7 @@
 
   if (fd_count == 1) {
     hci::H4Protocol* h4_hci =
-        new hci::H4Protocol(fd_list[0], intercept_events, acl_cb, sco_cb);
+        new hci::H4Protocol(fd_list[0], intercept_events, acl_cb, sco_cb, iso_cb);
     fd_watcher_.WatchFdForNonBlockingReads(
         fd_list[0], [h4_hci](int fd) { h4_hci->OnDataReady(fd); });
     hci_ = h4_hci;
diff --git a/bluetooth/1.0/default/vendor_interface.h b/bluetooth/1.0/default/vendor_interface.h
index 1d69040..040f31a 100644
--- a/bluetooth/1.0/default/vendor_interface.h
+++ b/bluetooth/1.0/default/vendor_interface.h
@@ -38,7 +38,7 @@
  public:
   static bool Initialize(InitializeCompleteCallback initialize_complete_cb,
                          PacketReadCallback event_cb, PacketReadCallback acl_cb,
-                         PacketReadCallback sco_cb);
+                         PacketReadCallback sco_cb, PacketReadCallback iso_cb);
   static void Shutdown();
   static VendorInterface* get();
 
@@ -51,7 +51,7 @@
 
   bool Open(InitializeCompleteCallback initialize_complete_cb,
             PacketReadCallback event_cb, PacketReadCallback acl_cb,
-            PacketReadCallback sco_cb);
+            PacketReadCallback sco_cb, PacketReadCallback iso_cb);
   void Close();
 
   void OnTimeout();
diff --git a/bluetooth/1.0/vts/functional/VtsHalBluetoothV1_0TargetTest.cpp b/bluetooth/1.0/vts/functional/VtsHalBluetoothV1_0TargetTest.cpp
index beb9a3e..ef02eff 100644
--- a/bluetooth/1.0/vts/functional/VtsHalBluetoothV1_0TargetTest.cpp
+++ b/bluetooth/1.0/vts/functional/VtsHalBluetoothV1_0TargetTest.cpp
@@ -24,8 +24,9 @@
 #include <utils/Log.h>
 
 #include <VtsHalHidlTargetCallbackBase.h>
-#include <VtsHalHidlTargetTestBase.h>
-#include <VtsHalHidlTargetTestEnvBase.h>
+#include <gtest/gtest.h>
+#include <hidl/GtestPrinter.h>
+#include <hidl/ServiceManagement.h>
 
 #include <chrono>
 #include <queue>
@@ -137,30 +138,12 @@
   std::chrono::steady_clock::time_point start_time_;
 };
 
-// Test environment for Bluetooth HIDL HAL.
-class BluetoothHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
- public:
-  // get the test environment singleton
-  static BluetoothHidlEnvironment* Instance() {
-    static BluetoothHidlEnvironment* instance = new BluetoothHidlEnvironment;
-    return instance;
-  }
-
-  virtual void registerTestServices() override {
-    registerTestService<IBluetoothHci>();
-  }
-
- private:
-  BluetoothHidlEnvironment() {}
-};
-
 // The main test class for Bluetooth HIDL HAL.
-class BluetoothHidlTest : public ::testing::VtsHalHidlTargetTestBase {
+class BluetoothHidlTest : public ::testing::TestWithParam<std::string> {
  public:
   virtual void SetUp() override {
     // currently test passthrough mode only
-    bluetooth =
-        ::testing::VtsHalHidlTargetTestBase::getService<IBluetoothHci>();
+    bluetooth = IBluetoothHci::getService(GetParam());
     ASSERT_NE(bluetooth, nullptr);
     ALOGI("%s: getService() for bluetooth is %s", __func__,
           bluetooth->isRemote() ? "remote" : "local");
@@ -617,10 +600,10 @@
 }
 
 // Empty test: Initialize()/Close() are called in SetUp()/TearDown().
-TEST_F(BluetoothHidlTest, InitializeAndClose) {}
+TEST_P(BluetoothHidlTest, InitializeAndClose) {}
 
 // Send an HCI Reset with sendHciCommand and wait for a command complete event.
-TEST_F(BluetoothHidlTest, HciReset) {
+TEST_P(BluetoothHidlTest, HciReset) {
   hidl_vec<uint8_t> cmd = COMMAND_HCI_RESET;
   bluetooth->sendHciCommand(cmd);
 
@@ -628,7 +611,7 @@
 }
 
 // Read and check the HCI version of the controller.
-TEST_F(BluetoothHidlTest, HciVersionTest) {
+TEST_P(BluetoothHidlTest, HciVersionTest) {
   hidl_vec<uint8_t> cmd = COMMAND_HCI_READ_LOCAL_VERSION_INFORMATION;
   bluetooth->sendHciCommand(cmd);
 
@@ -649,7 +632,7 @@
 }
 
 // Send an unknown HCI command and wait for the error message.
-TEST_F(BluetoothHidlTest, HciUnknownCommand) {
+TEST_P(BluetoothHidlTest, HciUnknownCommand) {
   hidl_vec<uint8_t> cmd = COMMAND_HCI_SHOULD_BE_UNKNOWN;
   bluetooth->sendHciCommand(cmd);
 
@@ -676,14 +659,14 @@
 }
 
 // Enter loopback mode, but don't send any packets.
-TEST_F(BluetoothHidlTest, WriteLoopbackMode) {
+TEST_P(BluetoothHidlTest, WriteLoopbackMode) {
   std::vector<uint16_t> sco_connection_handles;
   std::vector<uint16_t> acl_connection_handles;
   enterLoopbackMode(sco_connection_handles, acl_connection_handles);
 }
 
 // Enter loopback mode and send single packets.
-TEST_F(BluetoothHidlTest, LoopbackModeSinglePackets) {
+TEST_P(BluetoothHidlTest, LoopbackModeSinglePackets) {
   setBufferSizes();
 
   std::vector<uint16_t> sco_connection_handles;
@@ -720,7 +703,7 @@
 }
 
 // Enter loopback mode and send packets for bandwidth measurements.
-TEST_F(BluetoothHidlTest, LoopbackModeBandwidth) {
+TEST_P(BluetoothHidlTest, LoopbackModeBandwidth) {
   setBufferSizes();
 
   std::vector<uint16_t> sco_connection_handles;
@@ -758,11 +741,8 @@
   }
 }
 
-int main(int argc, char** argv) {
-  ::testing::AddGlobalTestEnvironment(BluetoothHidlEnvironment::Instance());
-  ::testing::InitGoogleTest(&argc, argv);
-  BluetoothHidlEnvironment::Instance()->init(&argc, argv);
-  int status = RUN_ALL_TESTS();
-  ALOGI("Test result = %d", status);
-  return status;
-}
+INSTANTIATE_TEST_SUITE_P(
+    PerInstance, BluetoothHidlTest,
+    testing::ValuesIn(
+        android::hardware::getAllHalInstanceNames(IBluetoothHci::descriptor)),
+    android::hardware::PrintInstanceNameToString);
diff --git a/bluetooth/1.1/Android.bp b/bluetooth/1.1/Android.bp
new file mode 100644
index 0000000..4204aed
--- /dev/null
+++ b/bluetooth/1.1/Android.bp
@@ -0,0 +1,18 @@
+// This file is autogenerated by hidl-gen -Landroidbp.
+
+hidl_interface {
+    name: "android.hardware.bluetooth@1.1",
+    root: "android.hardware",
+    vndk: {
+        enabled: true,
+    },
+    srcs: [
+        "IBluetoothHci.hal",
+        "IBluetoothHciCallbacks.hal",
+    ],
+    interfaces: [
+        "android.hardware.bluetooth@1.0",
+        "android.hidl.base@1.0",
+    ],
+    gen_java: true,
+}
diff --git a/bluetooth/1.1/IBluetoothHci.hal b/bluetooth/1.1/IBluetoothHci.hal
new file mode 100644
index 0000000..0f69c6e
--- /dev/null
+++ b/bluetooth/1.1/IBluetoothHci.hal
@@ -0,0 +1,44 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.bluetooth@1.1;
+
+import @1.0::HciPacket;
+import @1.0::IBluetoothHci;
+import IBluetoothHciCallbacks;
+
+/**
+ * The Host Controller Interface (HCI) is the layer defined by the Bluetooth
+ * specification between the software that runs on the host and the Bluetooth
+ * controller chip. This boundary is the natural choice for a Hardware
+ * Abstraction Layer (HAL). Dealing only in HCI packets and events simplifies
+ * the stack and abstracts away power management, initialization, and other
+ * implementation-specific details related to the hardware.
+ */
+interface IBluetoothHci extends @1.0::IBluetoothHci {
+    /**
+     * Same as @1.0, but uses 1.1 Callbacks version
+     */
+    initialize_1_1(@1.1::IBluetoothHciCallbacks callback);
+
+    /**
+     * Send an ISO data packet (as specified in the Bluetooth Specification
+     * V6.0) to the Bluetooth controller.
+     * Packets must be processed in order.
+     * @param data HCI data packet to be sent
+     */
+    sendIsoData(HciPacket data);
+};
diff --git a/bluetooth/1.1/IBluetoothHciCallbacks.hal b/bluetooth/1.1/IBluetoothHciCallbacks.hal
new file mode 100644
index 0000000..62cbe45
--- /dev/null
+++ b/bluetooth/1.1/IBluetoothHciCallbacks.hal
@@ -0,0 +1,31 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.bluetooth@1.1;
+
+import @1.0::HciPacket;
+import @1.0::IBluetoothHciCallbacks;
+
+/**
+ * The interface from the Bluetooth Controller to the stack.
+ */
+interface IBluetoothHciCallbacks extends @1.0::IBluetoothHciCallbacks {
+    /**
+     * Send a ISO data packet form the controller to the host.
+     * @param data the ISO HCI packet to be passed to the host stack
+     */
+    isoDataReceived(HciPacket data);
+};
diff --git a/bluetooth/1.1/default/Android.bp b/bluetooth/1.1/default/Android.bp
new file mode 100644
index 0000000..4f7fecb
--- /dev/null
+++ b/bluetooth/1.1/default/Android.bp
@@ -0,0 +1,42 @@
+//
+// Copyright (C) 2019 The Android Open Source Project
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+cc_binary {
+    name: "android.hardware.bluetooth@1.1-service",
+    defaults: ["hidl_defaults"],
+    relative_install_path: "hw",
+    vendor: true,
+    init_rc: ["android.hardware.bluetooth@1.1-service.rc"],
+    srcs: [
+        "service.cpp",
+        "bluetooth_hci.cc",
+    ],
+
+    shared_libs: [
+        "liblog",
+        "libcutils",
+        "libdl",
+        "libbase",
+        "libutils",
+        "libhardware",
+        "libhidlbase",
+        "android.hardware.bluetooth@1.0-impl",
+        "android.hardware.bluetooth@1.1",
+    ],
+
+    static_libs: [
+        "android.hardware.bluetooth-hci",
+    ],
+}
diff --git a/bluetooth/1.1/default/OWNERS b/bluetooth/1.1/default/OWNERS
new file mode 100644
index 0000000..0c01df6
--- /dev/null
+++ b/bluetooth/1.1/default/OWNERS
@@ -0,0 +1,3 @@
+zachoverflow@google.com
+mylesgw@google.com
+jpawlowski@google.com
diff --git a/bluetooth/1.1/default/android.hardware.bluetooth@1.1-service.rc b/bluetooth/1.1/default/android.hardware.bluetooth@1.1-service.rc
new file mode 100644
index 0000000..49f0be3
--- /dev/null
+++ b/bluetooth/1.1/default/android.hardware.bluetooth@1.1-service.rc
@@ -0,0 +1,9 @@
+service vendor.bluetooth-1-1 /vendor/bin/hw/android.hardware.bluetooth@1.1-service
+    interface android.hardware.bluetooth@1.1::IBluetoothHci default
+    interface android.hardware.bluetooth@1.0::IBluetoothHci default
+    class hal
+    capabilities BLOCK_SUSPEND NET_ADMIN SYS_NICE
+    user bluetooth
+    group bluetooth
+    writepid /dev/stune/foreground/tasks
+
diff --git a/bluetooth/1.1/default/bluetooth_hci.cc b/bluetooth/1.1/default/bluetooth_hci.cc
new file mode 100644
index 0000000..7ccce80
--- /dev/null
+++ b/bluetooth/1.1/default/bluetooth_hci.cc
@@ -0,0 +1,197 @@
+/*
+ * Copyright 2016 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "android.hardware.bluetooth@1.1-impl"
+#include "bluetooth_hci.h"
+
+#include <log/log.h>
+
+#include "vendor_interface.h"
+
+using android::hardware::bluetooth::V1_0::implementation::VendorInterface;
+
+namespace android {
+namespace hardware {
+namespace bluetooth {
+namespace V1_1 {
+namespace implementation {
+
+static const uint8_t HCI_DATA_TYPE_COMMAND = 1;
+static const uint8_t HCI_DATA_TYPE_ACL = 2;
+static const uint8_t HCI_DATA_TYPE_SCO = 3;
+static const uint8_t HCI_DATA_TYPE_ISO = 5;
+
+class BluetoothDeathRecipient : public hidl_death_recipient {
+ public:
+  BluetoothDeathRecipient(const sp<IBluetoothHci> hci) : mHci(hci) {}
+
+  virtual void serviceDied(
+      uint64_t /*cookie*/,
+      const wp<::android::hidl::base::V1_0::IBase>& /*who*/) {
+    ALOGE("BluetoothDeathRecipient::serviceDied - Bluetooth service died");
+    has_died_ = true;
+    mHci->close();
+  }
+  sp<IBluetoothHci> mHci;
+  bool getHasDied() const { return has_died_; }
+  void setHasDied(bool has_died) { has_died_ = has_died; }
+
+ private:
+  bool has_died_;
+};
+
+BluetoothHci::BluetoothHci()
+    : death_recipient_(new BluetoothDeathRecipient(this)) {}
+
+Return<void> BluetoothHci::initialize_1_1(
+    const ::android::sp<V1_1::IBluetoothHciCallbacks>& cb) {
+  ALOGI("BluetoothHci::initialize_1_1()");
+  if (cb == nullptr) {
+    ALOGE("cb == nullptr! -> Unable to call initializationComplete(ERR)");
+    return Void();
+  }
+
+  death_recipient_->setHasDied(false);
+  cb->linkToDeath(death_recipient_, 0);
+
+  bool rc = VendorInterface::Initialize(
+      [cb](bool status) {
+        auto hidl_status = cb->initializationComplete(
+            status ? V1_0::Status::SUCCESS
+                   : V1_0::Status::INITIALIZATION_ERROR);
+        if (!hidl_status.isOk()) {
+          ALOGE("VendorInterface -> Unable to call initializationComplete()");
+        }
+      },
+      [cb](const hidl_vec<uint8_t>& packet) {
+        auto hidl_status = cb->hciEventReceived(packet);
+        if (!hidl_status.isOk()) {
+          ALOGE("VendorInterface -> Unable to call hciEventReceived()");
+        }
+      },
+      [cb](const hidl_vec<uint8_t>& packet) {
+        auto hidl_status = cb->aclDataReceived(packet);
+        if (!hidl_status.isOk()) {
+          ALOGE("VendorInterface -> Unable to call aclDataReceived()");
+        }
+      },
+      [cb](const hidl_vec<uint8_t>& packet) {
+        auto hidl_status = cb->scoDataReceived(packet);
+        if (!hidl_status.isOk()) {
+          ALOGE("VendorInterface -> Unable to call scoDataReceived()");
+        }
+      },
+      [cb](const hidl_vec<uint8_t>& packet) {
+        auto hidl_status = cb->isoDataReceived(packet);
+        if (!hidl_status.isOk()) {
+          ALOGE("VendorInterface -> Unable to call isoDataReceived()");
+        }
+      });
+  if (!rc) {
+    auto hidl_status =
+        cb->initializationComplete(V1_0::Status::INITIALIZATION_ERROR);
+    if (!hidl_status.isOk()) {
+      ALOGE("VendorInterface -> Unable to call initializationComplete(ERR)");
+    }
+  }
+
+  unlink_cb_ = [cb](sp<BluetoothDeathRecipient>& death_recipient) {
+    if (death_recipient->getHasDied())
+      ALOGI("Skipping unlink call, service died.");
+    else
+      cb->unlinkToDeath(death_recipient);
+  };
+
+  return Void();
+}
+
+class OldCbWrapper : public V1_1::IBluetoothHciCallbacks {
+ public:
+  const ::android::sp<V1_0::IBluetoothHciCallbacks> old_cb_;
+  OldCbWrapper(const ::android::sp<V1_0::IBluetoothHciCallbacks>& old_cb)
+      : old_cb_(old_cb) {}
+
+  virtual ~OldCbWrapper() = default;
+
+  Return<void> initializationComplete(V1_0::Status status) override {
+    return old_cb_->initializationComplete(status);
+  };
+
+  Return<void> hciEventReceived(
+      const ::android::hardware::hidl_vec<uint8_t>& event) override {
+    return old_cb_->hciEventReceived(event);
+  };
+
+  Return<void> aclDataReceived(
+      const ::android::hardware::hidl_vec<uint8_t>& data) override {
+    return old_cb_->aclDataReceived(data);
+  };
+
+  Return<void> scoDataReceived(
+      const ::android::hardware::hidl_vec<uint8_t>& data) override {
+    return old_cb_->scoDataReceived(data);
+  };
+
+  Return<void> isoDataReceived(
+      const ::android::hardware::hidl_vec<uint8_t>&) override {
+    ALOGE("Please use HAL V1_1 for ISO.");
+    return Void();
+  };
+};
+
+Return<void> BluetoothHci::initialize(
+    const ::android::sp<V1_0::IBluetoothHciCallbacks>& cb) {
+  ALOGE("Using initialize from HAL V1_0 instead of initialize_1_1.");
+  return initialize_1_1(new OldCbWrapper(cb));
+}
+
+Return<void> BluetoothHci::close() {
+  ALOGI("BluetoothHci::close()");
+  unlink_cb_(death_recipient_);
+  VendorInterface::Shutdown();
+  return Void();
+}
+
+Return<void> BluetoothHci::sendHciCommand(const hidl_vec<uint8_t>& command) {
+  sendDataToController(HCI_DATA_TYPE_COMMAND, command);
+  return Void();
+}
+
+Return<void> BluetoothHci::sendAclData(const hidl_vec<uint8_t>& data) {
+  sendDataToController(HCI_DATA_TYPE_ACL, data);
+  return Void();
+}
+
+Return<void> BluetoothHci::sendScoData(const hidl_vec<uint8_t>& data) {
+  sendDataToController(HCI_DATA_TYPE_SCO, data);
+  return Void();
+}
+
+Return<void> BluetoothHci::sendIsoData(const hidl_vec<uint8_t>& data) {
+  sendDataToController(HCI_DATA_TYPE_ISO, data);
+  return Void();
+}
+
+void BluetoothHci::sendDataToController(const uint8_t type,
+                                        const hidl_vec<uint8_t>& data) {
+  VendorInterface::get()->Send(type, data.data(), data.size());
+}
+
+}  // namespace implementation
+}  // namespace V1_1
+}  // namespace bluetooth
+}  // namespace hardware
+}  // namespace android
diff --git a/bluetooth/1.1/default/bluetooth_hci.h b/bluetooth/1.1/default/bluetooth_hci.h
new file mode 100644
index 0000000..5f59cb0
--- /dev/null
+++ b/bluetooth/1.1/default/bluetooth_hci.h
@@ -0,0 +1,63 @@
+/*
+ * Copyright 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef HIDL_GENERATED_android_hardware_bluetooth_V1_1_BluetoothHci_H_
+#define HIDL_GENERATED_android_hardware_bluetooth_V1_1_BluetoothHci_H_
+
+#include <android/hardware/bluetooth/1.1/IBluetoothHci.h>
+#include <android/hardware/bluetooth/1.1/IBluetoothHciCallbacks.h>
+
+#include <hidl/MQDescriptor.h>
+
+#include <functional>
+
+namespace android {
+namespace hardware {
+namespace bluetooth {
+namespace V1_1 {
+namespace implementation {
+
+using ::android::hardware::hidl_vec;
+using ::android::hardware::Return;
+
+class BluetoothDeathRecipient;
+
+class BluetoothHci : public V1_1::IBluetoothHci {
+ public:
+  BluetoothHci();
+  Return<void> initialize(
+      const ::android::sp<V1_0::IBluetoothHciCallbacks>& cb) override;
+  Return<void> initialize_1_1(
+      const ::android::sp<V1_1::IBluetoothHciCallbacks>& cb) override;
+  Return<void> sendHciCommand(const hidl_vec<uint8_t>& packet) override;
+  Return<void> sendAclData(const hidl_vec<uint8_t>& data) override;
+  Return<void> sendScoData(const hidl_vec<uint8_t>& data) override;
+  Return<void> sendIsoData(const hidl_vec<uint8_t>& data) override;
+  Return<void> close() override;
+
+ private:
+  void sendDataToController(const uint8_t type, const hidl_vec<uint8_t>& data);
+  ::android::sp<BluetoothDeathRecipient> death_recipient_;
+  std::function<void(sp<BluetoothDeathRecipient>&)> unlink_cb_;
+};
+
+}  // namespace implementation
+}  // namespace V1_1
+}  // namespace bluetooth
+}  // namespace hardware
+}  // namespace android
+
+#endif  // HIDL_GENERATED_android_hardware_bluetooth_V1_1_BluetoothHci_H_
diff --git a/bluetooth/1.1/default/service.cpp b/bluetooth/1.1/default/service.cpp
new file mode 100644
index 0000000..affa855
--- /dev/null
+++ b/bluetooth/1.1/default/service.cpp
@@ -0,0 +1,43 @@
+//
+// Copyright 2019 The Android Open Source Project
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+#define LOG_TAG "android.hardware.bluetooth@1.1-service"
+
+#include <android/hardware/bluetooth/1.1/IBluetoothHci.h>
+#include <hidl/HidlTransportSupport.h>
+
+#include "bluetooth_hci.h"
+
+// Generated HIDL files
+using android::hardware::bluetooth::V1_1::IBluetoothHci;
+using android::hardware::bluetooth::V1_1::implementation::BluetoothHci;
+
+using android::sp;
+using android::status_t;
+
+int main() {
+  ::android::hardware::configureRpcThreadpool(1 /*threads*/, true /*willJoin*/);
+
+  sp bluetoothHci = new BluetoothHci();
+  const status_t status = bluetoothHci->registerAsService();
+  if (status != ::android::OK) {
+    ALOGE("Cannot register Bluetooth HAL service");
+    return 1;  // or handle error
+  }
+
+  ::android::hardware::joinRpcThreadpool();
+  return 1;  // joinRpcThreadpool should never return
+}
diff --git a/bluetooth/1.1/vts/OWNERS b/bluetooth/1.1/vts/OWNERS
new file mode 100644
index 0000000..ff6fd93
--- /dev/null
+++ b/bluetooth/1.1/vts/OWNERS
@@ -0,0 +1,6 @@
+zachoverflow@google.com
+siyuanh@google.com
+mylesgw@google.com
+jpawlowski@google.com
+hsz@google.com
+
diff --git a/vibrator/1.4/vts/functional/Android.bp b/bluetooth/1.1/vts/functional/Android.bp
similarity index 65%
copy from vibrator/1.4/vts/functional/Android.bp
copy to bluetooth/1.1/vts/functional/Android.bp
index 202a824..8d6d749 100644
--- a/vibrator/1.4/vts/functional/Android.bp
+++ b/bluetooth/1.1/vts/functional/Android.bp
@@ -15,19 +15,13 @@
 //
 
 cc_test {
-    name: "VtsHalVibratorV1_4TargetTest",
+    name: "VtsHalBluetoothV1_1TargetTest",
     defaults: ["VtsHalTargetTestDefaults"],
-    srcs: ["VtsHalVibratorV1_4TargetTest.cpp"],
+    srcs: ["VtsHalBluetoothV1_1TargetTest.cpp"],
     static_libs: [
-        "android.hardware.vibrator@1.0",
-        "android.hardware.vibrator@1.1",
-        "android.hardware.vibrator@1.2",
-        "android.hardware.vibrator@1.3",
-        "android.hardware.vibrator@1.4",
+        "android.hardware.bluetooth@1.1",
+        "android.hardware.bluetooth@1.0",
+        "libbluetooth-types",
     ],
-    test_suites: [
-        "general-tests",
-        "vts-core",
-    ],
+    test_suites: ["general-tests", "vts-core"],
 }
-
diff --git a/bluetooth/1.1/vts/functional/VtsHalBluetoothV1_1TargetTest.cpp b/bluetooth/1.1/vts/functional/VtsHalBluetoothV1_1TargetTest.cpp
new file mode 100644
index 0000000..659b2c8
--- /dev/null
+++ b/bluetooth/1.1/vts/functional/VtsHalBluetoothV1_1TargetTest.cpp
@@ -0,0 +1,760 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#define LOG_TAG "bluetooth_hidl_hal_test"
+#include <android-base/logging.h>
+
+#include <android/hardware/bluetooth/1.0/types.h>
+#include <android/hardware/bluetooth/1.1/IBluetoothHci.h>
+#include <android/hardware/bluetooth/1.1/IBluetoothHciCallbacks.h>
+#include <hardware/bluetooth.h>
+#include <utils/Log.h>
+
+#include <VtsHalHidlTargetCallbackBase.h>
+#include <gtest/gtest.h>
+#include <hidl/GtestPrinter.h>
+#include <hidl/ServiceManagement.h>
+
+#include <chrono>
+#include <queue>
+#include <thread>
+
+using ::android::sp;
+using ::android::hardware::hidl_death_recipient;
+using ::android::hardware::hidl_vec;
+using ::android::hardware::Return;
+using ::android::hardware::Void;
+using ::android::hardware::bluetooth::V1_0::Status;
+using ::android::hardware::bluetooth::V1_1::IBluetoothHci;
+using ::android::hardware::bluetooth::V1_1::IBluetoothHciCallbacks;
+
+#define HCI_MINIMUM_HCI_VERSION 5  // Bluetooth Core Specification 3.0 + HS
+#define HCI_MINIMUM_LMP_VERSION 5  // Bluetooth Core Specification 3.0 + HS
+#define NUM_HCI_COMMANDS_BANDWIDTH 1000
+#define NUM_SCO_PACKETS_BANDWIDTH 1000
+#define NUM_ACL_PACKETS_BANDWIDTH 1000
+#define WAIT_FOR_INIT_TIMEOUT std::chrono::milliseconds(2000)
+#define WAIT_FOR_HCI_EVENT_TIMEOUT std::chrono::milliseconds(2000)
+#define WAIT_FOR_SCO_DATA_TIMEOUT std::chrono::milliseconds(1000)
+#define WAIT_FOR_ACL_DATA_TIMEOUT std::chrono::milliseconds(1000)
+#define INTERFACE_CLOSE_DELAY_MS std::chrono::milliseconds(200)
+
+#define COMMAND_HCI_SHOULD_BE_UNKNOWN \
+  { 0xff, 0x3B, 0x08, 0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07 }
+#define COMMAND_HCI_READ_LOCAL_VERSION_INFORMATION \
+  { 0x01, 0x10, 0x00 }
+#define COMMAND_HCI_READ_BUFFER_SIZE \
+  { 0x05, 0x10, 0x00 }
+#define COMMAND_HCI_WRITE_LOOPBACK_MODE_LOCAL \
+  { 0x02, 0x18, 0x01, 0x01 }
+#define COMMAND_HCI_RESET \
+  { 0x03, 0x0c, 0x00 }
+#define COMMAND_HCI_WRITE_LOCAL_NAME \
+  { 0x13, 0x0c, 0xf8 }
+#define HCI_STATUS_SUCCESS 0x00
+#define HCI_STATUS_UNKNOWN_HCI_COMMAND 0x01
+
+#define EVENT_CONNECTION_COMPLETE 0x03
+#define EVENT_COMMAND_COMPLETE 0x0e
+#define EVENT_COMMAND_STATUS 0x0f
+#define EVENT_NUMBER_OF_COMPLETED_PACKETS 0x13
+#define EVENT_LOOPBACK_COMMAND 0x19
+
+#define EVENT_CODE_BYTE 0
+#define EVENT_LENGTH_BYTE 1
+#define EVENT_FIRST_PAYLOAD_BYTE 2
+#define EVENT_COMMAND_STATUS_STATUS_BYTE 2
+#define EVENT_COMMAND_STATUS_ALLOWED_PACKETS_BYTE 3
+#define EVENT_COMMAND_STATUS_OPCODE_LSBYTE 4  // Bytes 4 and 5
+#define EVENT_COMMAND_COMPLETE_ALLOWED_PACKETS_BYTE 2
+#define EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE 3  // Bytes 3 and 4
+#define EVENT_COMMAND_COMPLETE_STATUS_BYTE 5
+#define EVENT_COMMAND_COMPLETE_FIRST_PARAM_BYTE 6
+#define EVENT_LOCAL_HCI_VERSION_BYTE EVENT_COMMAND_COMPLETE_FIRST_PARAM_BYTE
+#define EVENT_LOCAL_LMP_VERSION_BYTE EVENT_LOCAL_HCI_VERSION_BYTE + 3
+
+#define EVENT_CONNECTION_COMPLETE_PARAM_LENGTH 11
+#define EVENT_CONNECTION_COMPLETE_TYPE 11
+#define EVENT_CONNECTION_COMPLETE_TYPE_SCO 0
+#define EVENT_CONNECTION_COMPLETE_TYPE_ACL 1
+#define EVENT_CONNECTION_COMPLETE_HANDLE_LSBYTE 3
+#define EVENT_COMMAND_STATUS_LENGTH 4
+
+#define EVENT_NUMBER_OF_COMPLETED_PACKETS_NUM_HANDLES 2
+
+#define ACL_BROADCAST_FLAG_OFFSET 6
+#define ACL_BROADCAST_FLAG_POINT_TO_POINT 0x0
+#define ACL_BROADCAST_POINT_TO_POINT \
+  (ACL_BROADCAST_FLAG_POINT_TO_POINT << ACL_BROADCAST_FLAG_OFFSET)
+
+#define ACL_PACKET_BOUNDARY_FLAG_OFFSET 4
+#define ACL_PACKET_BOUNDARY_FLAG_FIRST_AUTO_FLUSHABLE 0x2
+#define ACL_PACKET_BOUNDARY_FIRST_AUTO_FLUSHABLE \
+  (ACL_PACKET_BOUNDARY_FLAG_FIRST_AUTO_FLUSHABLE \
+   << ACL_PACKET_BOUNDARY_FLAG_OFFSET)
+
+// To be removed in VTS release builds
+#define ACL_HANDLE_QCA_DEBUG_MESSAGE 0xedc
+
+constexpr char kCallbackNameAclEventReceived[] = "aclDataReceived";
+constexpr char kCallbackNameHciEventReceived[] = "hciEventReceived";
+constexpr char kCallbackNameInitializationComplete[] = "initializationComplete";
+constexpr char kCallbackNameScoEventReceived[] = "scoDataReceived";
+constexpr char kCallbackNameIsoEventReceived[] = "isoDataReceived";
+
+class ThroughputLogger {
+ public:
+  ThroughputLogger(std::string task)
+      : task_(task), start_time_(std::chrono::steady_clock::now()) {}
+
+  ~ThroughputLogger() {
+    if (total_bytes_ == 0) return;
+    std::chrono::duration<double> duration =
+        std::chrono::steady_clock::now() - start_time_;
+    double s = duration.count();
+    if (s == 0) return;
+    double rate_kb = (static_cast<double>(total_bytes_) / s) / 1024;
+    ALOGD("%s %.1f KB/s (%zu bytes in %.3fs)", task_.c_str(), rate_kb,
+          total_bytes_, s);
+  }
+
+  void setTotalBytes(size_t total_bytes) { total_bytes_ = total_bytes; }
+
+ private:
+  size_t total_bytes_;
+  std::string task_;
+  std::chrono::steady_clock::time_point start_time_;
+};
+
+// The main test class for Bluetooth HIDL HAL.
+class BluetoothHidlTest : public ::testing::TestWithParam<std::string> {
+ public:
+  virtual void SetUp() override {
+    // currently test passthrough mode only
+    bluetooth = IBluetoothHci::getService(GetParam());
+    ASSERT_NE(bluetooth, nullptr);
+    ALOGI("%s: getService() for bluetooth is %s", __func__,
+          bluetooth->isRemote() ? "remote" : "local");
+
+    bluetooth_hci_death_recipient = new BluetoothHciDeathRecipient();
+    ASSERT_NE(bluetooth_hci_death_recipient, nullptr);
+    ASSERT_TRUE(
+        bluetooth->linkToDeath(bluetooth_hci_death_recipient, 0).isOk());
+
+    bluetooth_cb = new BluetoothHciCallbacks(*this);
+    ASSERT_NE(bluetooth_cb, nullptr);
+
+    max_acl_data_packet_length = 0;
+    max_sco_data_packet_length = 0;
+    max_acl_data_packets = 0;
+    max_sco_data_packets = 0;
+
+    initialized = false;
+    event_cb_count = 0;
+    acl_cb_count = 0;
+    sco_cb_count = 0;
+
+    ASSERT_FALSE(initialized);
+    // Should not be checked in production code
+    ASSERT_TRUE(bluetooth->initialize(bluetooth_cb).isOk());
+
+    bluetooth_cb->SetWaitTimeout(kCallbackNameInitializationComplete,
+                                 WAIT_FOR_INIT_TIMEOUT);
+    bluetooth_cb->SetWaitTimeout(kCallbackNameHciEventReceived,
+                                 WAIT_FOR_HCI_EVENT_TIMEOUT);
+    bluetooth_cb->SetWaitTimeout(kCallbackNameAclEventReceived,
+                                 WAIT_FOR_ACL_DATA_TIMEOUT);
+    bluetooth_cb->SetWaitTimeout(kCallbackNameScoEventReceived,
+                                 WAIT_FOR_SCO_DATA_TIMEOUT);
+
+    EXPECT_TRUE(
+        bluetooth_cb->WaitForCallback(kCallbackNameInitializationComplete)
+            .no_timeout);
+
+    ASSERT_TRUE(initialized);
+  }
+
+  virtual void TearDown() override {
+    ALOGI("TearDown");
+    // Should not be checked in production code
+    ASSERT_TRUE(bluetooth->close().isOk());
+    std::this_thread::sleep_for(INTERFACE_CLOSE_DELAY_MS);
+    handle_no_ops();
+    EXPECT_EQ(static_cast<size_t>(0), event_queue.size());
+    EXPECT_EQ(static_cast<size_t>(0), sco_queue.size());
+    EXPECT_EQ(static_cast<size_t>(0), acl_queue.size());
+    EXPECT_EQ(static_cast<size_t>(0), iso_queue.size());
+  }
+
+  void setBufferSizes();
+
+  // Functions called from within tests in loopback mode
+  void sendAndCheckHCI(int num_packets);
+  void sendAndCheckSCO(int num_packets, size_t size, uint16_t handle);
+  void sendAndCheckACL(int num_packets, size_t size, uint16_t handle);
+
+  // Helper functions to try to get a handle on verbosity
+  void enterLoopbackMode(std::vector<uint16_t>* sco_handles,
+                         std::vector<uint16_t>* acl_handles);
+  void handle_no_ops();
+  void wait_for_event(bool timeout_is_error);
+  void wait_for_command_complete_event(hidl_vec<uint8_t> cmd);
+  int wait_for_completed_packets_event(uint16_t handle);
+
+  class BluetoothHciDeathRecipient : public hidl_death_recipient {
+   public:
+    void serviceDied(
+        uint64_t /*cookie*/,
+        const android::wp<::android::hidl::base::V1_0::IBase>& /*who*/)
+        override {
+      FAIL();
+    }
+  };
+
+  // A simple test implementation of BluetoothHciCallbacks.
+  class BluetoothHciCallbacks
+      : public ::testing::VtsHalHidlTargetCallbackBase<BluetoothHidlTest>,
+        public IBluetoothHciCallbacks {
+    BluetoothHidlTest& parent_;
+
+   public:
+    BluetoothHciCallbacks(BluetoothHidlTest& parent) : parent_(parent){};
+
+    virtual ~BluetoothHciCallbacks() = default;
+
+    Return<void> initializationComplete(Status status) override {
+      parent_.initialized = (status == Status::SUCCESS);
+      NotifyFromCallback(kCallbackNameInitializationComplete);
+      ALOGV("%s (status = %d)", __func__, static_cast<int>(status));
+      return Void();
+    };
+
+    Return<void> hciEventReceived(
+        const ::android::hardware::hidl_vec<uint8_t>& event) override {
+      parent_.event_cb_count++;
+      parent_.event_queue.push(event);
+      NotifyFromCallback(kCallbackNameHciEventReceived);
+      ALOGV("Event received (length = %d)", static_cast<int>(event.size()));
+      return Void();
+    };
+
+    Return<void> aclDataReceived(
+        const ::android::hardware::hidl_vec<uint8_t>& data) override {
+      parent_.acl_cb_count++;
+      parent_.acl_queue.push(data);
+      NotifyFromCallback(kCallbackNameAclEventReceived);
+      return Void();
+    };
+
+    Return<void> scoDataReceived(
+        const ::android::hardware::hidl_vec<uint8_t>& data) override {
+      parent_.sco_cb_count++;
+      parent_.sco_queue.push(data);
+      NotifyFromCallback(kCallbackNameScoEventReceived);
+      return Void();
+    };
+
+    Return<void> isoDataReceived(
+        const ::android::hardware::hidl_vec<uint8_t>& data) override {
+      parent_.iso_cb_count++;
+      parent_.iso_queue.push(data);
+      NotifyFromCallback(kCallbackNameIsoEventReceived);
+      return Void();
+    };
+  };
+
+  sp<IBluetoothHci> bluetooth;
+  sp<BluetoothHciCallbacks> bluetooth_cb;
+  sp<BluetoothHciDeathRecipient> bluetooth_hci_death_recipient;
+  std::queue<hidl_vec<uint8_t>> event_queue;
+  std::queue<hidl_vec<uint8_t>> acl_queue;
+  std::queue<hidl_vec<uint8_t>> sco_queue;
+  std::queue<hidl_vec<uint8_t>> iso_queue;
+
+  bool initialized;
+
+  int event_cb_count;
+  int sco_cb_count;
+  int acl_cb_count;
+  int iso_cb_count;
+
+  int max_acl_data_packet_length;
+  int max_sco_data_packet_length;
+  int max_acl_data_packets;
+  int max_sco_data_packets;
+};
+
+// Discard NO-OPs from the event queue.
+void BluetoothHidlTest::handle_no_ops() {
+  while (event_queue.size() > 0) {
+    hidl_vec<uint8_t> event = event_queue.front();
+    EXPECT_GE(event.size(),
+              static_cast<size_t>(EVENT_COMMAND_COMPLETE_STATUS_BYTE));
+    bool event_is_no_op =
+        (event[EVENT_CODE_BYTE] == EVENT_COMMAND_COMPLETE) &&
+        (event[EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE] == 0x00) &&
+        (event[EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE + 1] == 0x00);
+    event_is_no_op |= (event[EVENT_CODE_BYTE] == EVENT_COMMAND_STATUS) &&
+                      (event[EVENT_COMMAND_STATUS_OPCODE_LSBYTE] == 0x00) &&
+                      (event[EVENT_COMMAND_STATUS_OPCODE_LSBYTE + 1] == 0x00);
+    if (event_is_no_op) {
+      event_queue.pop();
+    } else {
+      break;
+    }
+  }
+  // To be removed in VTS release builds
+  while (acl_queue.size() > 0) {
+    hidl_vec<uint8_t> acl_packet = acl_queue.front();
+    uint16_t connection_handle = acl_packet[1] & 0xF;
+    connection_handle <<= 8;
+    connection_handle |= acl_packet[0];
+    bool packet_is_no_op = connection_handle == ACL_HANDLE_QCA_DEBUG_MESSAGE;
+    if (packet_is_no_op) {
+      acl_queue.pop();
+    } else {
+      break;
+    }
+  }
+}
+
+// Receive an event, discarding NO-OPs.
+void BluetoothHidlTest::wait_for_event(bool timeout_is_error = true) {
+  hidl_vec<uint8_t> event;
+  do {
+    bool no_timeout =
+        bluetooth_cb->WaitForCallback(kCallbackNameHciEventReceived).no_timeout;
+    EXPECT_TRUE(no_timeout || !timeout_is_error);
+    if (no_timeout && timeout_is_error) {
+      EXPECT_LT(static_cast<size_t>(0), event_queue.size());
+    }
+    if (event_queue.size() == 0) {
+      // WaitForCallback timed out.
+      return;
+    }
+    handle_no_ops();
+  } while (event_queue.size() == 0);
+}
+
+// Wait until a COMMAND_COMPLETE is received.
+void BluetoothHidlTest::wait_for_command_complete_event(hidl_vec<uint8_t> cmd) {
+  wait_for_event();
+  hidl_vec<uint8_t> event = event_queue.front();
+  event_queue.pop();
+
+  EXPECT_GT(event.size(),
+            static_cast<size_t>(EVENT_COMMAND_COMPLETE_STATUS_BYTE));
+  EXPECT_EQ(EVENT_COMMAND_COMPLETE, event[EVENT_CODE_BYTE]);
+  EXPECT_EQ(cmd[0], event[EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE]);
+  EXPECT_EQ(cmd[1], event[EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE + 1]);
+  EXPECT_EQ(HCI_STATUS_SUCCESS, event[EVENT_COMMAND_COMPLETE_STATUS_BYTE]);
+}
+
+// Send the command to read the controller's buffer sizes.
+void BluetoothHidlTest::setBufferSizes() {
+  hidl_vec<uint8_t> cmd = COMMAND_HCI_READ_BUFFER_SIZE;
+  bluetooth->sendHciCommand(cmd);
+
+  wait_for_event();
+  if (event_queue.size() == 0) return;
+
+  hidl_vec<uint8_t> event = event_queue.front();
+  event_queue.pop();
+
+  EXPECT_EQ(EVENT_COMMAND_COMPLETE, event[EVENT_CODE_BYTE]);
+  EXPECT_EQ(cmd[0], event[EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE]);
+  EXPECT_EQ(cmd[1], event[EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE + 1]);
+  EXPECT_EQ(HCI_STATUS_SUCCESS, event[EVENT_COMMAND_COMPLETE_STATUS_BYTE]);
+
+  max_acl_data_packet_length =
+      event[EVENT_COMMAND_COMPLETE_STATUS_BYTE + 1] +
+      (event[EVENT_COMMAND_COMPLETE_STATUS_BYTE + 2] << 8);
+  max_sco_data_packet_length = event[EVENT_COMMAND_COMPLETE_STATUS_BYTE + 3];
+  max_acl_data_packets = event[EVENT_COMMAND_COMPLETE_STATUS_BYTE + 4] +
+                         (event[EVENT_COMMAND_COMPLETE_STATUS_BYTE + 5] << 8);
+  max_sco_data_packets = event[EVENT_COMMAND_COMPLETE_STATUS_BYTE + 6] +
+                         (event[EVENT_COMMAND_COMPLETE_STATUS_BYTE + 7] << 8);
+
+  ALOGD("%s: ACL max %d num %d SCO max %d num %d", __func__,
+        static_cast<int>(max_acl_data_packet_length),
+        static_cast<int>(max_acl_data_packets),
+        static_cast<int>(max_sco_data_packet_length),
+        static_cast<int>(max_sco_data_packets));
+}
+
+// Send an HCI command (in Loopback mode) and check the response.
+void BluetoothHidlTest::sendAndCheckHCI(int num_packets) {
+  ThroughputLogger logger = {__func__};
+  int command_size = 0;
+  for (int n = 0; n < num_packets; n++) {
+    // Send an HCI packet
+    std::vector<uint8_t> write_name = COMMAND_HCI_WRITE_LOCAL_NAME;
+    // With a name
+    char new_name[] = "John Jacob Jingleheimer Schmidt ___________________0";
+    size_t new_name_length = strlen(new_name);
+    for (size_t i = 0; i < new_name_length; i++)
+      write_name.push_back(static_cast<uint8_t>(new_name[i]));
+    // And the packet number
+    size_t i = new_name_length - 1;
+    for (int digits = n; digits > 0; digits = digits / 10, i--)
+      write_name[i] = static_cast<uint8_t>('0' + digits % 10);
+    // And padding
+    for (size_t i = 0; i < 248 - new_name_length; i++)
+      write_name.push_back(static_cast<uint8_t>(0));
+
+    hidl_vec<uint8_t> cmd = write_name;
+    bluetooth->sendHciCommand(cmd);
+
+    // Check the loopback of the HCI packet
+    wait_for_event();
+    if (event_queue.size() == 0) return;
+
+    hidl_vec<uint8_t> event = event_queue.front();
+    event_queue.pop();
+    size_t compare_length =
+        (cmd.size() > static_cast<size_t>(0xff) ? static_cast<size_t>(0xff)
+                                                : cmd.size());
+    EXPECT_GT(event.size(), compare_length + EVENT_FIRST_PAYLOAD_BYTE - 1);
+
+    EXPECT_EQ(EVENT_LOOPBACK_COMMAND, event[EVENT_CODE_BYTE]);
+    EXPECT_EQ(compare_length, event[EVENT_LENGTH_BYTE]);
+
+    // Don't compare past the end of the event.
+    if (compare_length + EVENT_FIRST_PAYLOAD_BYTE > event.size()) {
+      compare_length = event.size() - EVENT_FIRST_PAYLOAD_BYTE;
+      ALOGE("Only comparing %d bytes", static_cast<int>(compare_length));
+    }
+
+    if (n == num_packets - 1) {
+      command_size = cmd.size();
+    }
+
+    for (size_t i = 0; i < compare_length; i++)
+      EXPECT_EQ(cmd[i], event[EVENT_FIRST_PAYLOAD_BYTE + i]);
+  }
+  logger.setTotalBytes(command_size * num_packets * 2);
+}
+
+// Send a SCO data packet (in Loopback mode) and check the response.
+void BluetoothHidlTest::sendAndCheckSCO(int num_packets, size_t size,
+                                        uint16_t handle) {
+  ThroughputLogger logger = {__func__};
+  for (int n = 0; n < num_packets; n++) {
+    // Send a SCO packet
+    hidl_vec<uint8_t> sco_packet;
+    std::vector<uint8_t> sco_vector;
+    sco_vector.push_back(static_cast<uint8_t>(handle & 0xff));
+    sco_vector.push_back(static_cast<uint8_t>((handle & 0x0f00) >> 8));
+    sco_vector.push_back(static_cast<uint8_t>(size & 0xff));
+    sco_vector.push_back(static_cast<uint8_t>((size & 0xff00) >> 8));
+    for (size_t i = 0; i < size; i++) {
+      sco_vector.push_back(static_cast<uint8_t>(i + n));
+    }
+    sco_packet = sco_vector;
+    bluetooth->sendScoData(sco_vector);
+
+    // Check the loopback of the SCO packet
+    EXPECT_TRUE(bluetooth_cb->WaitForCallback(kCallbackNameScoEventReceived)
+                    .no_timeout);
+    hidl_vec<uint8_t> sco_loopback = sco_queue.front();
+    sco_queue.pop();
+
+    EXPECT_EQ(sco_packet.size(), sco_loopback.size());
+    size_t successful_bytes = 0;
+
+    for (size_t i = 0; i < sco_packet.size(); i++) {
+      if (sco_packet[i] == sco_loopback[i]) {
+        successful_bytes = i;
+      } else {
+        ALOGE("Miscompare at %d (expected %x, got %x)", static_cast<int>(i),
+              sco_packet[i], sco_loopback[i]);
+        ALOGE("At %d (expected %x, got %x)", static_cast<int>(i + 1),
+              sco_packet[i + 1], sco_loopback[i + 1]);
+        break;
+      }
+    }
+    EXPECT_EQ(sco_packet.size(), successful_bytes + 1);
+  }
+  logger.setTotalBytes(num_packets * size * 2);
+}
+
+// Send an ACL data packet (in Loopback mode) and check the response.
+void BluetoothHidlTest::sendAndCheckACL(int num_packets, size_t size,
+                                        uint16_t handle) {
+  ThroughputLogger logger = {__func__};
+  for (int n = 0; n < num_packets; n++) {
+    // Send an ACL packet
+    hidl_vec<uint8_t> acl_packet;
+    std::vector<uint8_t> acl_vector;
+    acl_vector.push_back(static_cast<uint8_t>(handle & 0xff));
+    acl_vector.push_back(static_cast<uint8_t>((handle & 0x0f00) >> 8) |
+                         ACL_BROADCAST_POINT_TO_POINT |
+                         ACL_PACKET_BOUNDARY_FIRST_AUTO_FLUSHABLE);
+    acl_vector.push_back(static_cast<uint8_t>(size & 0xff));
+    acl_vector.push_back(static_cast<uint8_t>((size & 0xff00) >> 8));
+    for (size_t i = 0; i < size; i++) {
+      acl_vector.push_back(static_cast<uint8_t>(i + n));
+    }
+    acl_packet = acl_vector;
+    bluetooth->sendAclData(acl_vector);
+
+    // Check the loopback of the ACL packet
+    EXPECT_TRUE(bluetooth_cb->WaitForCallback(kCallbackNameAclEventReceived)
+                    .no_timeout);
+    hidl_vec<uint8_t> acl_loopback = acl_queue.front();
+    acl_queue.pop();
+
+    EXPECT_EQ(acl_packet.size(), acl_loopback.size());
+    size_t successful_bytes = 0;
+
+    for (size_t i = 0; i < acl_packet.size(); i++) {
+      if (acl_packet[i] == acl_loopback[i]) {
+        successful_bytes = i;
+      } else {
+        ALOGE("Miscompare at %d (expected %x, got %x)", static_cast<int>(i),
+              acl_packet[i], acl_loopback[i]);
+        ALOGE("At %d (expected %x, got %x)", static_cast<int>(i + 1),
+              acl_packet[i + 1], acl_loopback[i + 1]);
+        break;
+      }
+    }
+    EXPECT_EQ(acl_packet.size(), successful_bytes + 1);
+  }
+  logger.setTotalBytes(num_packets * size * 2);
+}
+
+// Return the number of completed packets reported by the controller.
+int BluetoothHidlTest::wait_for_completed_packets_event(uint16_t handle) {
+  int packets_processed = 0;
+  wait_for_event(false);
+  if (event_queue.size() == 0) {
+    ALOGW("%s: WaitForCallback timed out.", __func__);
+    return packets_processed;
+  }
+  while (event_queue.size() > 0) {
+    hidl_vec<uint8_t> event = event_queue.front();
+    event_queue.pop();
+
+    EXPECT_EQ(EVENT_NUMBER_OF_COMPLETED_PACKETS, event[EVENT_CODE_BYTE]);
+    EXPECT_EQ(1, event[EVENT_NUMBER_OF_COMPLETED_PACKETS_NUM_HANDLES]);
+
+    uint16_t event_handle = event[3] + (event[4] << 8);
+    EXPECT_EQ(handle, event_handle);
+
+    packets_processed += event[5] + (event[6] << 8);
+  }
+  return packets_processed;
+}
+
+// Send local loopback command and initialize SCO and ACL handles.
+void BluetoothHidlTest::enterLoopbackMode(std::vector<uint16_t>* sco_handles,
+                                          std::vector<uint16_t>* acl_handles) {
+  hidl_vec<uint8_t> cmd = COMMAND_HCI_WRITE_LOOPBACK_MODE_LOCAL;
+  bluetooth->sendHciCommand(cmd);
+
+  // Receive connection complete events with data channels
+  int connection_event_count = 0;
+  bool command_complete_received = false;
+  while (true) {
+    wait_for_event(false);
+    if (event_queue.size() == 0) {
+      // Fail if there was no event received or no connections completed.
+      EXPECT_TRUE(command_complete_received);
+      EXPECT_LT(0, connection_event_count);
+      return;
+    }
+    hidl_vec<uint8_t> event = event_queue.front();
+    event_queue.pop();
+    EXPECT_GT(event.size(),
+              static_cast<size_t>(EVENT_COMMAND_COMPLETE_STATUS_BYTE));
+    if (event[EVENT_CODE_BYTE] == EVENT_CONNECTION_COMPLETE) {
+      EXPECT_GT(event.size(),
+                static_cast<size_t>(EVENT_CONNECTION_COMPLETE_TYPE));
+      EXPECT_EQ(event[EVENT_LENGTH_BYTE],
+                EVENT_CONNECTION_COMPLETE_PARAM_LENGTH);
+      uint8_t connection_type = event[EVENT_CONNECTION_COMPLETE_TYPE];
+
+      EXPECT_TRUE(connection_type == EVENT_CONNECTION_COMPLETE_TYPE_SCO ||
+                  connection_type == EVENT_CONNECTION_COMPLETE_TYPE_ACL);
+
+      // Save handles
+      uint16_t handle = event[EVENT_CONNECTION_COMPLETE_HANDLE_LSBYTE] |
+                        event[EVENT_CONNECTION_COMPLETE_HANDLE_LSBYTE + 1] << 8;
+      if (connection_type == EVENT_CONNECTION_COMPLETE_TYPE_SCO)
+        sco_handles->push_back(handle);
+      else
+        acl_handles->push_back(handle);
+
+      ALOGD("Connect complete type = %d handle = %d",
+            event[EVENT_CONNECTION_COMPLETE_TYPE], handle);
+      connection_event_count++;
+    } else {
+      EXPECT_EQ(EVENT_COMMAND_COMPLETE, event[EVENT_CODE_BYTE]);
+      EXPECT_EQ(cmd[0], event[EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE]);
+      EXPECT_EQ(cmd[1], event[EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE + 1]);
+      EXPECT_EQ(HCI_STATUS_SUCCESS, event[EVENT_COMMAND_COMPLETE_STATUS_BYTE]);
+      command_complete_received = true;
+    }
+  }
+}
+
+// Empty test: Initialize()/Close() are called in SetUp()/TearDown().
+TEST_P(BluetoothHidlTest, InitializeAndClose) {}
+
+// Send an HCI Reset with sendHciCommand and wait for a command complete event.
+TEST_P(BluetoothHidlTest, HciReset) {
+  hidl_vec<uint8_t> cmd = COMMAND_HCI_RESET;
+  bluetooth->sendHciCommand(cmd);
+
+  wait_for_command_complete_event(cmd);
+}
+
+// Read and check the HCI version of the controller.
+TEST_P(BluetoothHidlTest, HciVersionTest) {
+  hidl_vec<uint8_t> cmd = COMMAND_HCI_READ_LOCAL_VERSION_INFORMATION;
+  bluetooth->sendHciCommand(cmd);
+
+  wait_for_event();
+  if (event_queue.size() == 0) return;
+
+  hidl_vec<uint8_t> event = event_queue.front();
+  event_queue.pop();
+  EXPECT_GT(event.size(), static_cast<size_t>(EVENT_LOCAL_LMP_VERSION_BYTE));
+
+  EXPECT_EQ(EVENT_COMMAND_COMPLETE, event[EVENT_CODE_BYTE]);
+  EXPECT_EQ(cmd[0], event[EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE]);
+  EXPECT_EQ(cmd[1], event[EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE + 1]);
+  EXPECT_EQ(HCI_STATUS_SUCCESS, event[EVENT_COMMAND_COMPLETE_STATUS_BYTE]);
+
+  EXPECT_LE(HCI_MINIMUM_HCI_VERSION, event[EVENT_LOCAL_HCI_VERSION_BYTE]);
+  EXPECT_LE(HCI_MINIMUM_LMP_VERSION, event[EVENT_LOCAL_LMP_VERSION_BYTE]);
+}
+
+// Send an unknown HCI command and wait for the error message.
+TEST_P(BluetoothHidlTest, HciUnknownCommand) {
+  hidl_vec<uint8_t> cmd = COMMAND_HCI_SHOULD_BE_UNKNOWN;
+  bluetooth->sendHciCommand(cmd);
+
+  wait_for_event();
+  if (event_queue.size() == 0) return;
+
+  hidl_vec<uint8_t> event = event_queue.front();
+  event_queue.pop();
+
+  EXPECT_GT(event.size(),
+            static_cast<size_t>(EVENT_COMMAND_COMPLETE_STATUS_BYTE));
+  if (event[EVENT_CODE_BYTE] == EVENT_COMMAND_COMPLETE) {
+    EXPECT_EQ(cmd[0], event[EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE]);
+    EXPECT_EQ(cmd[1], event[EVENT_COMMAND_COMPLETE_OPCODE_LSBYTE + 1]);
+    EXPECT_EQ(HCI_STATUS_UNKNOWN_HCI_COMMAND,
+              event[EVENT_COMMAND_COMPLETE_STATUS_BYTE]);
+  } else {
+    EXPECT_EQ(EVENT_COMMAND_STATUS, event[EVENT_CODE_BYTE]);
+    EXPECT_EQ(cmd[0], event[EVENT_COMMAND_STATUS_OPCODE_LSBYTE]);
+    EXPECT_EQ(cmd[1], event[EVENT_COMMAND_STATUS_OPCODE_LSBYTE + 1]);
+    EXPECT_EQ(HCI_STATUS_UNKNOWN_HCI_COMMAND,
+              event[EVENT_COMMAND_STATUS_STATUS_BYTE]);
+  }
+}
+
+// Enter loopback mode, but don't send any packets.
+TEST_P(BluetoothHidlTest, WriteLoopbackMode) {
+  std::vector<uint16_t> sco_connection_handles;
+  std::vector<uint16_t> acl_connection_handles;
+  enterLoopbackMode(&sco_connection_handles, &acl_connection_handles);
+}
+
+// Enter loopback mode and send single packets.
+TEST_P(BluetoothHidlTest, LoopbackModeSinglePackets) {
+  setBufferSizes();
+
+  std::vector<uint16_t> sco_connection_handles;
+  std::vector<uint16_t> acl_connection_handles;
+  enterLoopbackMode(&sco_connection_handles, &acl_connection_handles);
+
+  sendAndCheckHCI(1);
+
+  // This should work, but breaks on some current platforms.  Figure out how to
+  // grandfather older devices but test new ones.
+  if (0 && sco_connection_handles.size() > 0) {
+    EXPECT_LT(0, max_sco_data_packet_length);
+    sendAndCheckSCO(1, max_sco_data_packet_length, sco_connection_handles[0]);
+    int sco_packets_sent = 1;
+    int completed_packets =
+        wait_for_completed_packets_event(sco_connection_handles[0]);
+    if (sco_packets_sent != completed_packets) {
+      ALOGW("%s: packets_sent (%d) != completed_packets (%d)", __func__,
+            sco_packets_sent, completed_packets);
+    }
+  }
+
+  if (acl_connection_handles.size() > 0) {
+    EXPECT_LT(0, max_acl_data_packet_length);
+    sendAndCheckACL(1, max_acl_data_packet_length, acl_connection_handles[0]);
+    int acl_packets_sent = 1;
+    int completed_packets =
+        wait_for_completed_packets_event(acl_connection_handles[0]);
+    if (acl_packets_sent != completed_packets) {
+      ALOGW("%s: packets_sent (%d) != completed_packets (%d)", __func__,
+            acl_packets_sent, completed_packets);
+    }
+  }
+}
+
+// Enter loopback mode and send packets for bandwidth measurements.
+TEST_P(BluetoothHidlTest, LoopbackModeBandwidth) {
+  setBufferSizes();
+
+  std::vector<uint16_t> sco_connection_handles;
+  std::vector<uint16_t> acl_connection_handles;
+  enterLoopbackMode(&sco_connection_handles, &acl_connection_handles);
+
+  sendAndCheckHCI(NUM_HCI_COMMANDS_BANDWIDTH);
+
+  // This should work, but breaks on some current platforms.  Figure out how to
+  // grandfather older devices but test new ones.
+  if (0 && sco_connection_handles.size() > 0) {
+    EXPECT_LT(0, max_sco_data_packet_length);
+    sendAndCheckSCO(NUM_SCO_PACKETS_BANDWIDTH, max_sco_data_packet_length,
+                    sco_connection_handles[0]);
+    int sco_packets_sent = NUM_SCO_PACKETS_BANDWIDTH;
+    int completed_packets =
+        wait_for_completed_packets_event(sco_connection_handles[0]);
+    if (sco_packets_sent != completed_packets) {
+      ALOGW("%s: packets_sent (%d) != completed_packets (%d)", __func__,
+            sco_packets_sent, completed_packets);
+    }
+  }
+
+  if (acl_connection_handles.size() > 0) {
+    EXPECT_LT(0, max_acl_data_packet_length);
+    sendAndCheckACL(NUM_ACL_PACKETS_BANDWIDTH, max_acl_data_packet_length,
+                    acl_connection_handles[0]);
+    int acl_packets_sent = NUM_ACL_PACKETS_BANDWIDTH;
+    int completed_packets =
+        wait_for_completed_packets_event(acl_connection_handles[0]);
+    if (acl_packets_sent != completed_packets) {
+      ALOGW("%s: packets_sent (%d) != completed_packets (%d)", __func__,
+            acl_packets_sent, completed_packets);
+    }
+  }
+}
+
+INSTANTIATE_TEST_SUITE_P(
+    PerInstance, BluetoothHidlTest,
+    testing::ValuesIn(
+        android::hardware::getAllHalInstanceNames(IBluetoothHci::descriptor)),
+    android::hardware::PrintInstanceNameToString);
diff --git a/compatibility_matrices/compatibility_matrix.current.xml b/compatibility_matrices/compatibility_matrix.current.xml
index aeb11c4..f0b5966 100644
--- a/compatibility_matrices/compatibility_matrix.current.xml
+++ b/compatibility_matrices/compatibility_matrix.current.xml
@@ -75,7 +75,7 @@
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.bluetooth</name>
-        <version>1.0</version>
+        <version>1.0-1</version>
         <interface>
             <name>IBluetoothHci</name>
             <instance>default</instance>
@@ -256,7 +256,7 @@
     <hal format="hidl" optional="false">
         <name>android.hardware.keymaster</name>
         <version>3.0</version>
-        <version>4.0</version>
+        <version>4.0-1</version>
         <interface>
             <name>IKeymasterDevice</name>
             <instance>default</instance>
@@ -264,7 +264,7 @@
     </hal>
     <hal format="hidl" optional="true">
         <name>android.hardware.keymaster</name>
-        <version>4.0</version>
+        <version>4.0-1</version>
         <interface>
             <name>IKeymasterDevice</name>
             <instance>strongbox</instance>
diff --git a/current.txt b/current.txt
index 98cb792..c1991c3 100644
--- a/current.txt
+++ b/current.txt
@@ -584,15 +584,20 @@
 fd65298e1e09e0e3c781ab18305920d757dbe55a3b459ce17814ec5cf6dfee99 android.hardware.wifi@1.0::IWifiP2pIface
 
 # HALs released in Android R
+79e115c8f8970b8b914bafc66df5425e065fda4dcda97222966ef12451d2a1cc android.hardware.bluetooth@1.1::IBluetoothHci
+40ab2c6866c18d32baf6e49e3053949e79601f56963a791e93e68b9ee18f718d android.hardware.bluetooth@1.1::IBluetoothHciCallbacks
 07d0a252b2d8fa35887908a996ba395cf392968395fc30afab791f46e0c22a52 android.hardware.boot@1.1::IBootControl
 74049a402be913963edfdd80828a53736570e9d8124a1bf18166b6ed46a6b0ab android.hardware.boot@1.1::types
 ce8dbe76eb9ee94b46ef98f725be992e760a5751073d4f4912484026541371f3 android.hardware.health@2.1::IHealth
 26f04510a0b57aba5167c5c0a7c2f077c2acbb98b81902a072517829fd9fd67f android.hardware.health@2.1::IHealthInfoCallback
 db47f4ceceb1f06c656f39caa70c557b0f8471ef59fd58611bea667ffca20101 android.hardware.health@2.1::types
+c228aaa27f66c48e147159a4f4996c5273191fece1b08de31bd171c61334855e android.hardware.keymaster@4.1::IKeymasterDevice
+adb0efdf1462e9b2e742c0dcadd598666aac551f178be06e755bfcdf5797abd0 android.hardware.keymaster@4.1::IOperation
+7a04ea5595ed418ca3e91c28b8bd7353dd988be9be7b0c8c9e64fb4b77bd4523 android.hardware.keymaster@4.1::types
 9e59fffceed0dd72a9799e04505db5f777bbbea1af0695ba4107ef6d967c6fda android.hardware.neuralnetworks@1.3::IDevice
 4a6c3b3556da951b4def21ba579a227c022980fe4465df6cdfbe20628fa75f5a android.hardware.neuralnetworks@1.3::IPreparedModel
 94e803236398bed1febb11cc21051bc42ec003700139b099d6c479e02a7ca3c3 android.hardware.neuralnetworks@1.3::IPreparedModelCallback
-b74fe72cfe438f50e772e6a307657ff449d5bde83c15dd1f140ff2edbe73499c android.hardware.neuralnetworks@1.3::types
+c511b1427b1c3f76af90967bbddaaf250db983a8d3abb9ff189fb5a807cf3d4d android.hardware.neuralnetworks@1.3::types
 274fb1254a6d1a97824ec5c880eeefc0e410dc6d3a2a4c34052201169d2b7de0 android.hardware.radio@1.5::types
 c8e81d912827a5d49b2ddcdc4eb4556c5d231a899a1dca879309e04210daa4a0 android.hardware.radio@1.5::IRadio
 a62a93faf173b14a6175b683ebf61ffa568dc61f81e369d2dce7b1265e86cf2f android.hardware.radio@1.5::IRadioIndication
diff --git a/health/2.0/default/healthd_common_adapter.cpp b/health/2.0/default/healthd_common_adapter.cpp
index 8fc689d..0b5d869 100644
--- a/health/2.0/default/healthd_common_adapter.cpp
+++ b/health/2.0/default/healthd_common_adapter.cpp
@@ -49,11 +49,14 @@
 static std::unique_ptr<HealthLoopAdapter> health_loop;
 
 int healthd_register_event(int fd, void (*handler)(uint32_t), EventWakeup wakeup) {
+    if (!health_loop) return -1;
+
     auto wrapped_handler = [handler](auto*, uint32_t epevents) { handler(epevents); };
     return health_loop->RegisterEvent(fd, wrapped_handler, wakeup);
 }
 
 void healthd_battery_update_internal(bool charger_online) {
+    if (!health_loop) return;
     health_loop->AdjustWakealarmPeriods(charger_online);
 }
 
diff --git a/keymaster/4.1/Android.bp b/keymaster/4.1/Android.bp
new file mode 100644
index 0000000..eaa7e41
--- /dev/null
+++ b/keymaster/4.1/Android.bp
@@ -0,0 +1,19 @@
+// This file is autogenerated by hidl-gen -Landroidbp.
+
+hidl_interface {
+    name: "android.hardware.keymaster@4.1",
+    root: "android.hardware",
+    vndk: {
+        enabled: true,
+    },
+    srcs: [
+        "types.hal",
+        "IKeymasterDevice.hal",
+        "IOperation.hal",
+    ],
+    interfaces: [
+        "android.hardware.keymaster@4.0",
+        "android.hidl.base@1.0",
+    ],
+    gen_java: false,
+}
diff --git a/keymaster/4.1/IKeymasterDevice.hal b/keymaster/4.1/IKeymasterDevice.hal
new file mode 100644
index 0000000..64d2c9f
--- /dev/null
+++ b/keymaster/4.1/IKeymasterDevice.hal
@@ -0,0 +1,70 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.keymaster@4.1;
+
+import @4.0::ErrorCode;
+import @4.0::HardwareAuthToken;
+import @4.0::IKeymasterDevice;
+import @4.0::KeyParameter;
+import @4.0::KeyPurpose;
+import @4.0::OperationHandle;
+import IOperation;
+
+/**
+ * @4.1::IKeymasterDevice is a minor extension to @4.0::IKeymasterDevice.  It adds support for
+ *
+ * - Partial hardware enforcment of UNLOCKED_DEVICE_REQUIRED keys;
+ * - Device-unique attestaion;
+ * - Early boot only keys;
+ * - Better cleanup of operations when clients die without completing or aborting them.
+ */
+interface IKeymasterDevice extends @4.0::IKeymasterDevice {
+    /**
+     * Called by client to notify the IKeymasterDevice that the device is now locked, and keys with
+     * the UNLOCKED_DEVICE_REQUIRED tag should no longer be usable.  When this function is called,
+     * the IKeymasterDevice should note the current timestamp, and attempts to use
+     * UNLOCKED_DEVICE_REQUIRED keys must be rejected with Error::DEVICE_LOCKED until an
+     * authentication token with a later timestamp is presented.  If the `passwordOnly' argument is
+     * set to true the sufficiently-recent authentication token must indicate that the user
+     * authenticated with a password, not a biometric.
+     *
+     * @param passwordOnly specifies whether the device must be unlocked with a password, rather
+     * than a biometric, before UNLOCKED_DEVICE_REQUIRED keys can be used.
+     */
+    deviceLocked(bool passwordOnly) generates (ErrorCode error);
+
+    /**
+     * Called by client to notify the IKeymasterDevice that the device has left the early boot
+     * state, and that keys with the EARLY_BOOT_ONLY tag may no longer be used.  All attempts to use
+     * an EARLY_BOOT_ONLY key after this method is called must fail with Error::INVALID_KEY_BLOB.
+     */
+    earlyBootEnded() generates (ErrorCode error);
+
+    /**
+     * Begins a cryptographic operation.  beginOp() is a variation on begin().  beginOp() has
+     * identical functionality to begin, but instead of an OperationHandle it returns an IOperation
+     * object.  An IKeymasterDevice HAL service must call linkToDeath() on the Operation before
+     * returning it, and the provided hidl_death_recipient, if called, must abort() the operation.
+     * This is to ensure that in the event a client crashes while an operation is in progress, the
+     * operation slot is freed and available for use by other clients.
+     *
+     * @4.1::IKeymasterDevices must implement both beginOp() and begin().
+     */
+    beginOp(KeyPurpose purpose, vec<uint8_t> keyBlob, vec<KeyParameter> inParams,
+        HardwareAuthToken authToken)
+        generates (ErrorCode error, vec<KeyParameter> outParam, IOperation operation);
+};
diff --git a/keymaster/4.1/IOperation.hal b/keymaster/4.1/IOperation.hal
new file mode 100644
index 0000000..7103e9e
--- /dev/null
+++ b/keymaster/4.1/IOperation.hal
@@ -0,0 +1,31 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.keymaster@4.1;
+
+import @4.0::ErrorCode;
+import @4.0::OperationHandle;
+
+/**
+ * IOperation represents an in-progress IKeymasterDevice operation.  It is returned by
+ * IKeymasterDevice.beginOp().
+ */
+interface IOperation {
+    /**
+     * Returns the operation handle to be used as an authentication challenge.
+     */
+    getOperationChallenge() generates (ErrorCode error, OperationHandle operation);
+};
diff --git a/keymaster/4.1/default/Android.bp b/keymaster/4.1/default/Android.bp
new file mode 100644
index 0000000..b06878b
--- /dev/null
+++ b/keymaster/4.1/default/Android.bp
@@ -0,0 +1,38 @@
+//
+// Copyright (C) 2019 The Android Open Source Project
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+cc_binary {
+    name: "android.hardware.keymaster@4.1-service",
+    defaults: ["hidl_defaults"],
+    relative_install_path: "hw",
+    vendor: true,
+    init_rc: ["android.hardware.keymaster@4.1-service.rc"],
+    srcs: ["service.cpp"],
+
+    shared_libs: [
+        "android.hardware.keymaster@4.0",
+        "android.hardware.keymaster@4.1",
+        "libbase",
+        "libcutils",
+        "libhardware",
+        "libhidlbase",
+        "libkeymaster4",
+        "libkeymaster41",
+        "liblog",
+        "libutils",
+    ],
+
+}
diff --git a/keymaster/4.1/default/OWNERS b/keymaster/4.1/default/OWNERS
new file mode 100644
index 0000000..335660d
--- /dev/null
+++ b/keymaster/4.1/default/OWNERS
@@ -0,0 +1,2 @@
+jdanis@google.com
+swillden@google.com
diff --git a/keymaster/4.1/default/android.hardware.keymaster@4.1-service.rc b/keymaster/4.1/default/android.hardware.keymaster@4.1-service.rc
new file mode 100644
index 0000000..740b3c2
--- /dev/null
+++ b/keymaster/4.1/default/android.hardware.keymaster@4.1-service.rc
@@ -0,0 +1,6 @@
+service vendor.keymaster-4-1 /vendor/bin/hw/android.hardware.keymaster@4.1-service
+    interface android.hardware.keymaster@4.0::IKeymasterDevice default
+    interface android.hardware.keymaster@4.1::IKeymasterDevice default
+    class early_hal
+    user system
+    group system drmrpc
diff --git a/keymaster/4.1/default/service.cpp b/keymaster/4.1/default/service.cpp
new file mode 100644
index 0000000..d79a291
--- /dev/null
+++ b/keymaster/4.1/default/service.cpp
@@ -0,0 +1,35 @@
+/*
+** Copyright 2019, The Android Open Source Project
+**
+** Licensed under the Apache License, Version 2.0 (the "License");
+** you may not use this file except in compliance with the License.
+** You may obtain a copy of the License at
+**
+**     http://www.apache.org/licenses/LICENSE-2.0
+**
+** Unless required by applicable law or agreed to in writing, software
+** distributed under the License is distributed on an "AS IS" BASIS,
+** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+** See the License for the specific language governing permissions and
+** limitations under the License.
+*/
+
+#include <android-base/logging.h>
+#include <android/hardware/keymaster/4.1/IKeymasterDevice.h>
+#include <hidl/HidlTransportSupport.h>
+
+#include <AndroidKeymaster41Device.h>
+
+using android::hardware::keymaster::V4_0::SecurityLevel;
+
+int main() {
+    ::android::hardware::configureRpcThreadpool(1, true /* willJoinThreadpool */);
+    auto keymaster = ::keymaster::V4_1::CreateKeymasterDevice(SecurityLevel::SOFTWARE);
+    auto status = keymaster->registerAsService();
+    if (status != android::OK) {
+        LOG(FATAL) << "Could not register service for Keymaster 4.1 (" << status << ")";
+    }
+
+    android::hardware::joinRpcThreadpool();
+    return -1;  // Should never get here.
+}
diff --git a/keymaster/4.1/support/Android.bp b/keymaster/4.1/support/Android.bp
new file mode 100644
index 0000000..34b6108
--- /dev/null
+++ b/keymaster/4.1/support/Android.bp
@@ -0,0 +1,32 @@
+//
+// Copyright (C) 2019 The Android Open Source Project
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+cc_library {
+    name: "libkeymaster4_1support",
+    vendor_available: true,
+    cflags: [
+        "-Wall",
+        "-Wextra",
+        "-Werror",
+    ],
+    export_include_dirs: ["include"],
+    shared_libs: [
+        "android.hardware.keymaster@3.0",
+        "android.hardware.keymaster@4.0",
+        "android.hardware.keymaster@4.1",
+        "libkeymaster4support",
+    ]
+}
diff --git a/keymaster/4.1/support/include/keymasterV4_1/authorization_set.h b/keymaster/4.1/support/include/keymasterV4_1/authorization_set.h
new file mode 100644
index 0000000..afc0eaf
--- /dev/null
+++ b/keymaster/4.1/support/include/keymasterV4_1/authorization_set.h
@@ -0,0 +1,32 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef HARDWARE_INTERFACES_KEYMASTER_V4_1_SUPPORT_INCLUDE_AUTHORIZATION_SET_H_
+#define HARDWARE_INTERFACES_KEYMASTER_V4_1_SUPPORT_INCLUDE_AUTHORIZATION_SET_H_
+
+#include <keymasterV4_0/authorization_set.h>
+
+#include <keymasterV4_1/keymaster_tags.h>
+
+namespace android::hardware::keymaster::V4_1 {
+
+using V4_0::AuthorizationSet;
+using V4_0::AuthorizationSetBuilder;
+using V4_0::KeyParameter;
+
+}  // namespace android::hardware::keymaster::V4_1
+
+#endif  // HARDWARE_INTERFACES_KEYMASTER_V4_1_SUPPORT_INCLUDE_AUTHORIZATION_SET_H_
diff --git a/keymaster/4.1/support/include/keymasterV4_1/keymaster_tags.h b/keymaster/4.1/support/include/keymasterV4_1/keymaster_tags.h
new file mode 100644
index 0000000..6ffe8e1
--- /dev/null
+++ b/keymaster/4.1/support/include/keymasterV4_1/keymaster_tags.h
@@ -0,0 +1,96 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef HARDWARE_INTERFACES_KEYMASTER_V4_1_SUPPORT_INCLUDE_KEYMASTER_TAGS_H_
+#define HARDWARE_INTERFACES_KEYMASTER_V4_1_SUPPORT_INCLUDE_KEYMASTER_TAGS_H_
+
+#include <android/hardware/keymaster/4.1/types.h>
+
+#include <keymasterV4_0/keymaster_tags.h>
+
+namespace android::hardware::keymaster::V4_1 {
+
+using V4_0::BlockMode;
+using V4_0::Digest;
+using V4_0::EcCurve;
+using V4_0::ErrorCode;
+using V4_0::HardwareAuthToken;
+using V4_0::KeyParameter;
+using V4_0::PaddingMode;
+using V4_0::TagType;
+using V4_0::VerificationToken;
+
+using V4_0::TypedTag;
+
+using V4_0::TAG_ACTIVE_DATETIME;
+using V4_0::TAG_ALGORITHM;
+using V4_0::TAG_ALLOW_WHILE_ON_BODY;
+using V4_0::TAG_APPLICATION_DATA;
+using V4_0::TAG_APPLICATION_ID;
+using V4_0::TAG_ASSOCIATED_DATA;
+using V4_0::TAG_ATTESTATION_APPLICATION_ID;
+using V4_0::TAG_ATTESTATION_CHALLENGE;
+using V4_0::TAG_AUTH_TIMEOUT;
+using V4_0::TAG_BLOB_USAGE_REQUIREMENTS;
+using V4_0::TAG_BLOCK_MODE;
+using V4_0::TAG_BOOT_PATCHLEVEL;
+using V4_0::TAG_BOOTLOADER_ONLY;
+using V4_0::TAG_CALLER_NONCE;
+using V4_0::TAG_CONFIRMATION_TOKEN;
+using V4_0::TAG_CREATION_DATETIME;
+using V4_0::TAG_DIGEST;
+using V4_0::TAG_EC_CURVE;
+using V4_0::TAG_HARDWARE_TYPE;
+using V4_0::TAG_INCLUDE_UNIQUE_ID;
+using V4_0::TAG_INVALID;
+using V4_0::TAG_KEY_SIZE;
+using V4_0::TAG_MAC_LENGTH;
+using V4_0::TAG_MAX_USES_PER_BOOT;
+using V4_0::TAG_MIN_MAC_LENGTH;
+using V4_0::TAG_MIN_SECONDS_BETWEEN_OPS;
+using V4_0::TAG_NO_AUTH_REQUIRED;
+using V4_0::TAG_NONCE;
+using V4_0::TAG_ORIGIN;
+using V4_0::TAG_ORIGINATION_EXPIRE_DATETIME;
+using V4_0::TAG_OS_PATCHLEVEL;
+using V4_0::TAG_OS_VERSION;
+using V4_0::TAG_PADDING;
+using V4_0::TAG_PURPOSE;
+using V4_0::TAG_RESET_SINCE_ID_ROTATION;
+using V4_0::TAG_ROLLBACK_RESISTANCE;
+using V4_0::TAG_ROOT_OF_TRUST;
+using V4_0::TAG_RSA_PUBLIC_EXPONENT;
+using V4_0::TAG_TRUSTED_CONFIRMATION_REQUIRED;
+using V4_0::TAG_TRUSTED_USER_PRESENCE_REQUIRED;
+using V4_0::TAG_UNIQUE_ID;
+using V4_0::TAG_UNLOCKED_DEVICE_REQUIRED;
+using V4_0::TAG_USAGE_EXPIRE_DATETIME;
+using V4_0::TAG_USER_AUTH_TYPE;
+using V4_0::TAG_USER_ID;
+using V4_0::TAG_USER_SECURE_ID;
+using V4_0::TAG_VENDOR_PATCHLEVEL;
+
+#define DECLARE_KM_4_1_TYPED_TAG(name)                                                   \
+    typedef typename V4_0::Tag2TypedTag<(static_cast<V4_0::Tag>(V4_1::Tag::name))>::type \
+            TAG_##name##_t;                                                              \
+    static TAG_##name##_t TAG_##name;
+
+DECLARE_KM_4_1_TYPED_TAG(EARLY_BOOT_ONLY);
+DECLARE_KM_4_1_TYPED_TAG(DEVICE_UNIQUE_ATTESTATION);
+
+}  // namespace android::hardware::keymaster::V4_1
+
+#endif  // HARDWARE_INTERFACES_KEYMASTER_V4_1_SUPPORT_INCLUDE_KEYMASTER_TAGS_H_
diff --git a/keymaster/4.1/types.hal b/keymaster/4.1/types.hal
new file mode 100644
index 0000000..bdf1731
--- /dev/null
+++ b/keymaster/4.1/types.hal
@@ -0,0 +1,42 @@
+/*
+ * Copyright (C) 2019 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.keymaster@4.1;
+
+import @4.0::ErrorCode;
+import @4.0::Tag;
+import @4.0::TagType;
+
+enum Tag : @4.0::Tag {
+    /**
+     * Keys tagged with EARLY_BOOT_ONLY may only be used, or created, during early boot, until
+     * IKeymasterDevice::earlyBootEnded() is called.
+     */
+    EARLY_BOOT_ONLY = TagType:BOOL | 305,
+    /**
+     * DEVICE_UNIQUE_ATTESTATION is an argument to IKeymasterDevice::attestKey().  It indicates that
+     * attestation using a device-unique key is requested, rather than a batch key.  Only
+     * SecurityLevel::STRONGBOX IKeymasterDevices may support device-unique attestations.
+     * SecurityLevel::TRUSTED_ENVIRONMENT IKeymasterDevices must return ErrorCode::INVALID_ARGUMENT
+     * if they receive DEVICE_UNIQUE_ATTESTATION.  SecurityLevel::STRONGBOX IKeymasterDevices need
+     * not support DEVICE_UNIQUE_ATTESTATION, and return ErrorCode::CANNOT_ATTEST_IDS if they do not
+     * support it.
+     *
+     * IKeymasterDevice implementations that support device-unique attestation MUST add the
+     * DEVICE_UNIQUE_ATTESTATION tag to device-unique attestations.
+     */
+    DEVICE_UNIQUE_ATTESTATION = TagType:BOOL | 720,
+};
diff --git a/keymaster/4.1/vts/OWNERS b/keymaster/4.1/vts/OWNERS
new file mode 100644
index 0000000..335660d
--- /dev/null
+++ b/keymaster/4.1/vts/OWNERS
@@ -0,0 +1,2 @@
+jdanis@google.com
+swillden@google.com
diff --git a/vibrator/1.4/vts/functional/Android.bp b/keymaster/4.1/vts/functional/Android.bp
similarity index 66%
rename from vibrator/1.4/vts/functional/Android.bp
rename to keymaster/4.1/vts/functional/Android.bp
index 202a824..f5a0c9c 100644
--- a/vibrator/1.4/vts/functional/Android.bp
+++ b/keymaster/4.1/vts/functional/Android.bp
@@ -15,19 +15,16 @@
 //
 
 cc_test {
-    name: "VtsHalVibratorV1_4TargetTest",
+    name: "VtsHalKeymasterV4_1TargetTest",
     defaults: ["VtsHalTargetTestDefaults"],
-    srcs: ["VtsHalVibratorV1_4TargetTest.cpp"],
+    srcs: [
+        "EarlyBootKeyTest.cpp",
+    ],
     static_libs: [
-        "android.hardware.vibrator@1.0",
-        "android.hardware.vibrator@1.1",
-        "android.hardware.vibrator@1.2",
-        "android.hardware.vibrator@1.3",
-        "android.hardware.vibrator@1.4",
+        "android.hardware.keymaster@4.0",
+        "android.hardware.keymaster@4.1",
+        "libkeymaster4support",
+        "libkeymaster4_1support",
     ],
-    test_suites: [
-        "general-tests",
-        "vts-core",
-    ],
+    test_suites: ["vts-core"],
 }
-
diff --git a/vibrator/1.4/IVibratorCallback.hal b/keymaster/4.1/vts/functional/EarlyBootKeyTest.cpp
similarity index 80%
rename from vibrator/1.4/IVibratorCallback.hal
rename to keymaster/4.1/vts/functional/EarlyBootKeyTest.cpp
index 76281bc..4a19010 100644
--- a/vibrator/1.4/IVibratorCallback.hal
+++ b/keymaster/4.1/vts/functional/EarlyBootKeyTest.cpp
@@ -14,8 +14,8 @@
  * limitations under the License.
  */
 
-package android.hardware.vibrator@1.4;
+namespace android::hardware::keymaster::V4_1::test {
 
-interface IVibratorCallback {
-    oneway onComplete();
-};
+// TODO(swillden): Put tests here.
+
+}  // namespace android::hardware::keymaster::V4_1::test
diff --git a/neuralnetworks/1.3/types.hal b/neuralnetworks/1.3/types.hal
index 86ab287..7df14b1 100644
--- a/neuralnetworks/1.3/types.hal
+++ b/neuralnetworks/1.3/types.hal
@@ -73,6 +73,4500 @@
     BASE_MAX        = 0xFFFF,
 };
 
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
+enum OperationType : int32_t {
+
+    /**
+     * Adds two tensors, element-wise.
+     *
+     * Takes two input tensors of identical {@link OperandType} and compatible
+     * dimensions. The output is the sum of both input tensors, optionally
+     * modified by an activation function.
+     *
+     * Two dimensions are compatible when:
+     *     1. they are equal, or
+     *     2. one of them is 1
+     *
+     * The size of the output is the maximum size along each dimension of the
+     * input operands. It starts with the trailing dimensions, and works its
+     * way forward.
+     *
+     * Example:
+     *
+     *     input1.dimension = {4, 1, 2}
+     *     input2.dimension = {5, 4, 3, 1}
+     *     output.dimension = {5, 4, 3, 2}
+     *
+     * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
+     * dimension is only compatible with 0 or 1. The size of the output
+     * dimension is zero if either of corresponding input dimension is zero.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: A tensor.
+     * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+     *      as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scales and zeroPoint can be different from input0 scale and zeroPoint.
+     * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     *
+     * Outputs:
+     * * 0: The sum, a tensor of the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+     */
+    ADD = @1.2::OperationType:ADD,
+
+    /**
+     * Performs a 2-D average pooling operation.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and
+     * padding.
+     *
+     * The values in the output tensor are computed as:
+     *
+     *     output[b, i, j, channel] =
+     *         sum_{di, dj}(
+     *             input[b, strides[1] * i + di, strides[2] * j + dj, channel]
+     *         ) / sum(1)
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
+     *
+     * Both explicit padding and implicit padding are supported.
+     *
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the left, in the ‘width’ dimension.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the right, in the ‘width’ dimension.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the top, in the ‘height’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the bottom, in the ‘height’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+     *      width.
+     * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+     *      height.
+     * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
+     *       Set to true to specify NCHW data layout for input0 and output0.
+     *       Available since HAL version 1.2.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
+     *      padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+     *      width.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+     *      height.
+     * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *      Available since HAL version 1.2.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, out_height, out_width, depth].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    AVERAGE_POOL_2D = @1.2::OperationType:AVERAGE_POOL_2D,
+
+    /**
+     * Concatenates the input tensors along the given dimension.
+     *
+     * The input tensors must have identical {@link OperandType} and the same
+     * dimensions except the dimension along the concatenation axis.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *   (full support since HAL version 1.2, see the input section)
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0 ~ n-1: The list of n input tensors, of shape
+     *            [D0, D1, ..., Daxis(i), ..., Dm].
+     *            Before HAL version 1.2, all input tensors of
+     *            {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *            must have the same scale and zeroPoint as the output tensor.
+     *            Since HAL version 1.2, zero-sized tensors are supported.
+     * * n: An {@link OperandType::INT32} scalar, specifying the
+     *      concatenation axis.
+     *
+     * Outputs:
+     * * 0: The output, a tensor of the same {@link OperandType} as the input
+     *      tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+     *      Since HAL version 1.2, for a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint values can be different from
+     *      input tensors. Before HAL version 1.2 they have to be the same as for the input tensors.
+     */
+    CONCATENATION = @1.2::OperationType:CONCATENATION,
+
+    /**
+     * Performs a 2-D convolution operation.
+     *
+     * The CONV_2D op sweeps a 2-D filter that can mix channels together over a
+     * batch of images, applying the filter to each window of each image of the
+     * appropriate size.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and
+     * padding.
+     *
+     * The values in the output tensor are computed as:
+     *
+     *     output[b, i, j, channel] =
+     *         sum_{di, dj, k} (
+     *             input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+     *             filter[channel, di, dj, k]
+     *         ) + bias[channel]
+     *
+     * Supported tensor {@link OperandType} configurations:
+     * * 32 bit floating point:
+     * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+     *
+     * * Quantized:
+     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+     * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+     * * * input.scale * filter.scale).
+     *
+     * Available since HAL version 1.2:
+     * * 16 bit floating point:
+     * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+     *
+     * * Quantized with symmetric per channel quantization for the filter:
+     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+     * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+     * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+     * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
+     *
+     * Both explicit padding and implicit padding are supported.
+     *
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+     *      specifying the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
+     * * 1: A 4-D tensor, of shape
+     *      [depth_out, filter_height, filter_width, depth_in], specifying the
+     *      filter.
+     *      For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+     *      the channel dimension (SymmPerChannelQuantParams::channelDim)
+     *      must be set to 0.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32}
+     *      or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
+     *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+     *      of 0 and bias_scale == input_scale * filter_scale.
+     *      For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+     *      and bias_scale of 0. The actual scale of each value 'i' is equal to
+     *      bias_scale[i] = input_scale * filter_scale[i].
+     * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the left, in the ‘width’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the right, in the ‘width’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the top, in the ‘height’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the bottom, in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *      Available since HAL version 1.2.
+     * * 11: An optional {@link OperandType::INT32} scalar, specifying the dilation
+     *      factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
+     *      cells between each filter element on width dimension. If this input is set,
+     *      input 12 (dilation factor for height) must be specified as well.
+     *      Available since HAL version 1.2.
+     * * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
+     *      factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
+     *      cells between each filter element on height dimension. If this input is set,
+     *      input 11 (dilation factor for width) must be specified as well.
+     *      Available since HAL version 1.2.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+     *      specifying the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
+     * * 1: A 4-D tensor, of shape
+     *      [depth_out, filter_height, filter_width, depth_in], specifying the
+     *      filter.
+     *      For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+     *      the channel dimension (SymmPerChannelQuantParams::channelDim)
+     *      must be set to 0.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32}
+     *      or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
+     *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+     *      of 0 and bias_scale == input_scale * filter_scale.
+     *      For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+     *      and bias_scale of 0. The actual scale of each value 'i' is equal to
+     *      bias_scale[i] = input_scale * filter_scale[i].
+     * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
+     *      padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *      Available since HAL version 1.2.
+     * * 8: An optional {@link OperandType::INT32} scalar, specifying the dilation
+     *      factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
+     *      cells between each filter element on width dimension. If this input is set,
+     *      input 9 (dilation factor for height) must be specified as well.
+     *      Available since HAL version 1.2.
+     * * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
+     *      factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
+     *      cells between each filter element on height dimension. If this input is set,
+     *      input 8 (dilation factor for width) must be specified as well.
+     *      Available since HAL version 1.2.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, out_height, out_width, depth_out].
+     *      Before HAL version 1.2, for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the following condition must be satisfied: output_scale > input_scale * filter_scale
+     */
+    CONV_2D = @1.2::OperationType:CONV_2D,
+
+    /**
+     * Performs a depthwise 2-D convolution operation.
+     *
+     * Given an input tensor of shape [batches, height, width, depth_in] and a
+     * filter tensor of shape [1, filter_height, filter_width, depth_out]
+     * containing depth_out convolutional filters of depth 1, DEPTHWISE_CONV
+     * applies a different filter to each input channel (expanding from 1
+     * channel to channel_multiplier channels for each), then concatenates the
+     * results together.
+     *
+     * The output has depth_out = depth_in * depth_multiplier channels.
+     * The output dimensions are functions of the filter dimensions, stride, and
+     * padding.
+     *
+     * The values in the output tensor are computed as:
+     *
+     *     output[b, i, j, k * channel_multiplier + q] =
+     *         sum_{di, dj} (
+     *             input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+     *             filter[1, di, dj, k * channel_multiplier + q]
+     *         ) + bias[k * channel_multiplier + q]
+     *
+     * Supported tensor {@link OperandType} configurations:
+     * * 32 bit floating point:
+     * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+     *
+     * * Quantized:
+     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+     * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+     * * * input.scale * filter.scale).
+     *
+     * Available since HAL version 1.2:
+     * * 16 bit floating point:
+     * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+     *
+     * * Quantized with symmetric per channel quantization for the filter:
+     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+     * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+     * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+     * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
+     *
+     * Both explicit padding and implicit padding are supported.
+     *
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+     *      specifying the input.
+     * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+     *      specifying the filter.
+     *      For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+     *      the channel dimension (SymmPerChannelQuantParams::channelDim)
+     *      must be set to 3.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32}
+     *      or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
+     *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+     *      of 0 and bias_scale == input_scale * filter_scale.
+     *      For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+     *      and bias_scale of 0. The actual scale of each value 'i' is equal to
+     *      bias_scale[i] = input_scale * filter_scale[i].
+     * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the left, in the ‘width’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the right, in the ‘width’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the top, in the ‘height’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the bottom, in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 9: An {@link OperandType::INT32} scalar, specifying the depthwise
+     *      multiplier.
+     * * 10: An {@link OperandType::INT32} scalar, and has to be one of the
+     *       {@link FusedActivationFunc} values. Specifies the activation to
+     *       invoke on the result.
+     * * 11: An optional {@link OperandType::BOOL} scalar, default to false.
+     *       Set to true to specify NCHW data layout for input0 and output0.
+     *       Available since HAL version 1.2.
+     * * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
+     *      factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
+     *      cells between each filter element on width dimension. If this input is set,
+     *      input 13 (dilation factor for height) must be specified as well.
+     *      Available since HAL version 1.2.
+     * * 13: An optional {@link OperandType::INT32} scalar, specifying the dilation
+     *      factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
+     *      cells between each filter element on height dimension. If this input is set,
+     *      input 12 (dilation factor for width) must be specified as well.
+     *      Available since HAL version 1.2.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+     *      specifying the input.
+     * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+     *      specifying the filter.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32}
+     *      or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
+     *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+     *      of 0 and bias_scale == input_scale * filter_scale.
+     *      For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+     *      and bias_scale of 0. The actual scale of each value 'i' is equal to
+     *      bias_scale[i] = input_scale * filter_scale[i].
+     * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
+     *      padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the depthwise
+     *      multiplier.
+     * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     * * 8: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *      Available since HAL version 1.2.
+     * * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
+     *      factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
+     *      cells between each filter element on width dimension. If this input is set,
+     *      input 10 (dilation factor for height) must be specified as well.
+     *      Available since HAL version 1.2.
+     * * 10: An optional {@link OperandType::INT32} scalar, specifying the dilation
+     *      factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
+     *      cells between each filter element on height dimension. If this input is set,
+     *      input 9 (dilation factor for width) must be specified as well.
+     *      Available since HAL version 1.2.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, out_height, out_width, depth_out]. Before HAL version 1.2, for
+     *      output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the following condition must be satisfied:
+     *      output_scale > input_scale * filter_scale
+     */
+    DEPTHWISE_CONV_2D = @1.2::OperationType:DEPTHWISE_CONV_2D,
+
+    /**
+     * Rearranges data from depth into blocks of spatial data.
+     *
+     * More specifically, this op outputs a copy of the input tensor where
+     * values from the depth dimension are moved in spatial blocks to the height
+     * and width dimensions. The value block_size indicates the input block size
+     * and how the data is moved.
+     *
+     * Chunks of data of size block_size * block_size from depth are rearranged
+     * into non-overlapping blocks of size block_size x block_size.
+     *
+     * The width of the output tensor is input_depth * block_size, whereas the
+     * height is input_height * block_size. The depth of the input tensor must
+     * be divisible by block_size * block_size
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
+     *
+     * Inputs:
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+     *      specifying the input.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the block_size.
+     *      block_size must be >=1 and block_size * block_size must be a divisor
+     *      of the input depth.
+     * * 2: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *      Available since HAL version 1.2.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batch, height*block_size,
+     *      width*block_size, depth/(block_size*block_size)].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    DEPTH_TO_SPACE = @1.2::OperationType:DEPTH_TO_SPACE,
+
+    /**
+     * Dequantizes the input tensor.
+     *
+     * The formula is:
+     *
+     *     output = (input - zeroPoint) * scale.
+     *
+     * Supported input tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * * {@link OperandType::TENSOR_QUANT8_SYMM} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since HAL version 1.2)
+     *
+     * Supported output tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}.
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: A tensor.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
+     *
+     * Outputs:
+     * * 0: A tensor with the same shape as input0.
+     */
+    DEQUANTIZE = @1.2::OperationType:DEQUANTIZE,
+
+    /**
+     * Looks up sub-tensors in the input tensor.
+     *
+     * This operator takes for input a tensor of values (Values) and
+     * a one-dimensional tensor of selection indices (Lookups).
+     * The output tensor is the concatenation of sub-tensors of Values as
+     * selected by Lookups.
+     *
+     * Think of Values as being sliced along its first dimension:
+     * The entries in Lookups select which slices are concatenated together
+     * to create the output tensor.
+     *
+     * For example, if Values has shape of [40, 200, 300] and
+     * Lookups has shape of [3], all three values found in Lookups are
+     * expected to be between 0 and 39. The resulting tensor must
+     * have shape of [3, 200, 300].
+     *
+     * If a value in Lookups is out of bounds, the operation must fail
+     * and an error must be reported.
+     *
+     * Supported value tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported value tensor rank: from 2
+     *
+     * Inputs:
+     * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32}.
+     *      The values are indices into the first dimension of Values.
+     * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are
+     *      extracted.
+     *
+     * Output:
+     * * 0: A n-D tensor with the same rank and shape as the Values
+     *      tensor, except for the first dimension which has the same size
+     *      as Lookups' only dimension.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input1.
+     */
+    EMBEDDING_LOOKUP = @1.2::OperationType:EMBEDDING_LOOKUP,
+
+    /**
+     * Computes element-wise floor() on the input tensor.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: A tensor.
+     *
+     * Outputs:
+     * * 0: The output tensor, of the same {@link OperandType} and dimensions as
+     *      the input tensor.
+     */
+    FLOOR = @1.2::OperationType:FLOOR,
+
+    /**
+     * Denotes a fully (densely) connected layer, which connects all elements
+     * in the input tensor with each element in the output tensor.
+     *
+     * This layer implements the operation:
+     *
+     *     outputs = activation(inputs * weights’ + bias)
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * * 0: A tensor of at least rank 2, specifying the input. If rank is
+     *      greater than 2, then it gets flattened to a 2-D Tensor. The
+     *      (flattened) 2-D Tensor is reshaped (if necessary) to
+     *      [batch_size, input_size], where "input_size" corresponds to the
+     *      number of inputs to the layer, matching the second dimension of
+     *      weights, and "batch_size" is calculated by dividing the number of
+     *      elements by "input_size".
+     *      Since HAL version 1.2, zero batch_size is supported for this tensor.
+     * * 1: A 2-D tensor, specifying the weights, of shape
+     *      [num_units, input_size], where "num_units" corresponds to the number
+     *      of output nodes.
+     * * 2: A 1-D tensor, of shape [num_units], specifying the bias. For input
+     *      tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
+     *      of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
+     *      of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     *
+     * Outputs:
+     * * 0: The output tensor, of shape [batch_size, num_units]. Before HAL version 1.2, for
+     *      output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
+     *      condition must be satisfied: output_scale > input_scale * filter_scale.
+     */
+    FULLY_CONNECTED = @1.2::OperationType:FULLY_CONNECTED,
+
+    /**
+     * Looks up sub-tensors in the input tensor using a key-value map.
+     *
+     * This operator takes for input a tensor of values (Values),
+     * a one-dimensional tensor of selection values (Lookups) and
+     * a one-dimensional tensor that maps these values to Values
+     * indexes. The output tensor is the concatenation of sub-tensors of
+     * Values as selected by Lookups via Keys.
+     *
+     * Think of Values as being sliced along its outer-most dimension.
+     * The output is a concatenation of selected slices, with one slice
+     * for each entry of Lookups. The slice selected is the one at the
+     * same index as the Maps entry that matches the value in Lookups.
+     *
+     * For a hit, the corresponding sub-tensor of Values is included
+     * in the Output tensor. For a miss, the corresponding sub-tensor in
+     * Output must have zero values.
+     *
+     * For example, if Values has shape of [40, 200, 300],
+     * Keys should have a shape of [40]. If Lookups tensor has shape
+     * of [3], three slices are being concatenated, so the resulting tensor
+     * must have the shape of [3, 200, 300]. If the first entry in Lookups
+     * has the value 123456, that value must be located in Keys tensor.
+     * If the sixth entry of Keys contains 123456, the sixth slice of Values
+     * must be selected. If no entry in Keys has 123456, a slice of zeroes
+     * must be concatenated.
+     *
+     * Supported value tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported value tensor rank: from 2
+     *
+     * Inputs:
+     * * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with
+     *      shape [ k ].
+     * * 1: Keys. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape
+     *      [ n ]; Keys and Values pair represent a map, i.e., the ith element
+     *      in Keys (Keys[i]) is the key to select the ith sub-tensor in Values
+     *      (Values[i]), where 0 <= i <= n-1. Keys tensor *MUST* be sorted in
+     *      ascending order.
+     * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension
+     *      must be n.
+     *
+     * Outputs:
+     * * 0: Output. A tensor with shape [ k …].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input2.
+     * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
+     *      hits (True) or not (False).
+     *      Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0
+     *      and scale 1.0f.
+     *      A non-zero byte represents True, a hit. A zero indicates otherwise.
+     */
+    HASHTABLE_LOOKUP = @1.2::OperationType:HASHTABLE_LOOKUP,
+
+    /**
+     * Applies L2 normalization along the depth dimension.
+     *
+     * The values in the output tensor are computed as:
+     *
+     *     output[batch, row, col, channel] =
+     *         input[batch, row, col, channel] /
+     *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
+     *
+     * For input tensor with rank less than 4, independently normalizes each
+     * 1-D slice along dimension dim.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+     *
+     * Supported tensor rank: up to 4
+     * Tensors with rank less than 4 are only supported since HAL version 1.2.
+     *
+     * Inputs:
+     * * 0: An n-D tensor, specifying the tensor to be normalized.
+     * * 1: An optional {@link OperandType::INT32} scalar, default to -1,
+     *      specifying the dimension normalization would be performed on.
+     *      Negative index is used to specify axis from the end (e.g. -1 for
+     *      the last axis). Must be in the range [-n, n).
+     *      Available since HAL version 1.2.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} and same shape as input0.
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the scale must be 1.f / 128 and the zeroPoint must be 128.
+     */
+    L2_NORMALIZATION = @1.2::OperationType:L2_NORMALIZATION,
+
+    /**
+     * Performs an 2-D L2 pooling operation.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and
+     * padding.
+     *
+     * The values in the output tensor are computed as:
+     *
+     *     output[b, i, j, c] =
+     *         sqrt(sum_{di, dj} pow(input[b, strides[1] * i + di, strides[2] * j + dj, c], 2) /
+     *              sum(1))
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
+     *
+     * Both explicit padding and implicit padding are supported.
+     *
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the left, in the ‘width’ dimension.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the right, in the ‘width’ dimension.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the top, in the ‘height’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the bottom, in the ‘height’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+     *      width.
+     * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+     *      height.
+     * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
+     *       Set to true to specify NCHW data layout for input0 and output0.
+     *       Available since HAL version 1.2.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
+     *      padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+     *      width.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+     *      height.
+     * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *      Available since HAL version 1.2.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, out_height, out_width, depth].
+     */
+    L2_POOL_2D = @1.2::OperationType:L2_POOL_2D,
+
+    /**
+     * Applies Local Response Normalization along the depth dimension.
+     *
+     * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the
+     * last dimension), and each vector is normalized independently. Within a
+     * given vector, each component is divided by the weighted, squared sum of
+     * inputs within depth_radius.
+     *
+     * The output is calculated using this formula:
+     *
+     *     sqr_sum[a, b, c, d] = sum(
+     *         pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2))
+     *     output = input / pow((bias + alpha * sqr_sum), beta)
+     *
+     * For input tensor with rank less than 4, independently normalizes each
+     * 1-D slice along specified dimension.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: up to 4
+     * Tensors with rank less than 4 are only supported since HAL version 1.2.
+     *
+     * Inputs:
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the radius of
+     *      the normalization window.
+     * * 2: A scalar, specifying the bias, must not be zero.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT16}, the bias
+     *      value must be of {@link OperandType::FLOAT16}.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
+     *      value must be of {@link OperandType::FLOAT32}.
+     * * 3: A scalar, specifying the scale factor, alpha.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT16}, the
+     *      alpha value must be of {@link OperandType::FLOAT16}.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
+     *      alpha value must be of {@link OperandType::FLOAT32}.
+     * * 4: A scalar, specifying the exponent, beta.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta
+     *      value must be of {@link OperandType::FLOAT16}.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta
+     *      value must be of {@link OperandType::FLOAT32}.
+     * * 5: An optional {@link OperandType::INT32} scalar, default to -1,
+     *      specifying the dimension normalization would be performed on.
+     *      Negative index is used to specify axis from the end (e.g. -1 for
+     *      the last axis). Must be in the range [-n, n).
+     *      Available since HAL version 1.2.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     */
+    LOCAL_RESPONSE_NORMALIZATION = @1.2::OperationType:LOCAL_RESPONSE_NORMALIZATION,
+
+    /**
+     * Computes sigmoid activation on the input tensor element-wise.
+     *
+     * The output is calculated using this formula:
+     *
+     *     output = 1 / (1 + exp(-input))
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * * 0: A tensor, specifying the input.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the scale must be 1.f / 256 and the zeroPoint must be 0.
+     */
+    LOGISTIC = @1.2::OperationType:LOGISTIC,
+
+    /**
+     * Projects an input to a bit vector via locality senstive hashing.
+     *
+     * Supported input tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported input tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: Hash functions. Dim.size == 2, DataType: Float.
+     *      Tensor[0].Dim[0]: Number of hash functions.
+     *      Tensor[0].Dim[1]: Number of projected output bits generated by each
+     *      hash function.
+     *      If the projection type is Sparse:
+     *      Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32
+     *
+     * * 1: Input. Dim.size >= 1, no restriction on DataType.
+     * * 2: Weight. Optional. Dim.size == 1, DataType: Float.
+     *      If not set, each input element is considered to have the same weight
+     *      of 1.0.
+     *      Tensor[1].Dim[0] == Tensor[2].Dim[0]
+     * * 3: Type:
+     *        Sparse:
+     *          Value LSHProjectionType_SPARSE(=3) (since HAL version 1.2).
+     *          Computed bit vector is considered to be sparse.
+     *          Each output element is an int32 made up of multiple bits
+     *          computed from hash functions.
+     *
+     *          NOTE: To avoid collisions across hash functions, an offset value
+     *          of k * (1 << Tensor[0].Dim[1]) will be added to each signature,
+     *          where k is the index of the hash function.
+     *
+     *          Value LSHProjectionType_SPARSE_DEPRECATED(=1).
+     *          Legacy behavior that does not include the offset value.
+     *
+     *        Dense:
+     *          Value LSHProjectionType_DENSE(=2).
+     *          Computed bit vector is considered to be dense. Each output
+     *          element represents a bit and can take the value of either
+     *          0 or 1.
+     *
+     * Outputs:
+     * * 0: If the projection type is Sparse:
+     *      Output.Dim == { Tensor[0].Dim[0] }
+     *      A tensor of int32 that represents hash signatures.
+     *
+     *      If the projection type is Dense:
+     *      Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
+     *      A flattened tensor that represents projected bit vectors.
+     * The offset value for sparse projections was added in HAL version 1.2.
+     */
+    LSH_PROJECTION = @1.2::OperationType:LSH_PROJECTION,
+
+    /**
+     * Performs a single time step in a Long Short-Term Memory (LSTM) layer
+     *
+     * The LSTM operation is described by the following equations.
+     *
+     * \f{eqnarray*}{
+     * i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\
+     * f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\
+     * C_t =& clip(f_t \odot C_{t-1} + i_t \odot
+     *        g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell}) & \\
+     * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o) & \\
+     *      & & \\
+     *      & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj})
+     *      & if\ there\ is\ a\ projection; \\
+     * h_t =& & \\
+     *      & o_t \odot g(C_t) & otherwise. \\
+     * \f}
+     * Where:
+     * * \f$x_t\f$ is the input,
+     * * \f$i_t\f$ is the input gate,
+     * * \f$f_t\f$ is the forget gate,
+     * * \f$C_t\f$ is the cell state,
+     * * \f$o_t\f$ is the output,
+     * * \f$h_t\f$ is the output state,
+     * * \f$\sigma\f$ is the logistic sigmoid function,
+     * * \f$g\f$ is the cell input and cell output activation function, usually
+     *   \f$tahn\f$,
+     * * \f$W_{xi}\f$ is the input-to-input weight matrix,
+     * * \f$W_{hi}\f$ is the recurrent to input weight matrix,
+     * * \f$W_{ci}\f$ is the cell-to-input weight matrix,
+     * * \f$b_i\f$ is the input gate bias,
+     * * \f$W_{xf}\f$ is the input-to-forget weight matrix,
+     * * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix,
+     * * \f$W_{cf}\f$ is the cell-to-forget weight matrix,
+     * * \f$b_f\f$ is the forget gate bias,
+     * * \f$W_{xc}\f$ is the input-to-cell weight matrix,
+     * * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix,
+     * * \f$b_c\f$ is the cell bias,
+     * * \f$W_{xo}\f$ is the input-to-output weight matrix,
+     * * \f$W_{ho}\f$ is the recurrent-to-output weight matrix,
+     * * \f$W_{co}\f$ is the cell-to-output weight matrix,
+     * * \f$b_o\f$ is the output gate bias,
+     * * \f$W_{proj}\f$ is the projection weight matrix,
+     * * \f$b_{proj}\f$ is the projection bias,
+     * * \f$t_{cell}\f$ is the threshold for clipping the cell state, and
+     * * \f$t_{proj}\f$ is the threshold for clipping the projected output.
+     * * \f$\odot\f$ is the
+     *   <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)">
+     *   Hadamard product</a> that takes two matrices and produces another
+     *   matrix, each element of which is the product of the corresponding
+     *   elements of the input matrices.
+     *
+     * Since HAL version 1.2 LSTM supports layer normalization.
+     * In case layer normalization is used, the inputs to internal activation
+     * functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered
+     * following an approach from section 3.1 from
+     * https://arxiv.org/pdf/1607.06450.pdf
+     *
+     * The operation has the following independently optional inputs:
+     * * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights
+     *   (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all
+     *   have values or neither of them have values (i.e., all set to null). If
+     *   they have values, the peephole optimization is used.
+     * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights
+     *   (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values,
+     *   or none of them have values. If they have no values, coupling of input
+     *   and forget gates (CIFG) is used, in which case the input gate
+     *   (\f$i_t\f$) is calculated using the following equation instead.
+     *   \f{eqnarray*}{
+     *   i_t = 1 - f_t
+     *   \f}
+     *   In case peephole optimization is used and CIFG is not used
+     *   cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the
+     *   cell-to-input weights must have no value.
+     * * The projection weights (\f$W_{proj}\f$) is required only for the
+     *   recurrent projection layer, and should otherwise have no value.
+     * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a
+     *   value if the recurrent projection layer exists, and should otherwise
+     *   have no value.
+     * * (HAL version 1.2 or later) The four layer normalization weights either all have
+     *   values or none of them have values. Additionally, if CIFG is used,
+     *   input layer normalization weights tensor is omitted and the other layer
+     *   normalization weights either all have values or none of them have
+     *   values. Layer normalization is used when the values of all the layer
+     *   normalization weights are present.
+     *
+     * References:
+     *
+     * The default non-peephole non-CIFG implementation is based on:
+     * http://www.bioinf.jku.at/publications/older/2604.pdf
+     * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
+     * Computation, 9(8):1735-1780, 1997.
+     *
+     * The peephole implementation and projection layer is based on:
+     * https://research.google.com/pubs/archive/43905.pdf
+     * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
+     * recurrent neural network architectures for large scale acoustic
+     * modeling." INTERSPEECH, 2014.
+     * (However, the concept of peephole optimization was introduced in work
+     * prior to this paper.)
+     *
+     * The coupling of input and forget gate (CIFG) is based on:
+     * http://arxiv.org/pdf/1503.04069.pdf
+     * Greff et al. "LSTM: A Search Space Odyssey"
+     *
+     * The layer normalization is based on:
+     * https://arxiv.org/pdf/1607.06450.pdf
+     * Jimmy Ba et al. "Layer Normalization"
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * All input and output tensors must be of the same type.
+     *
+     * Inputs:
+     * * 0: The input (\f$x_t\f$).
+     *      A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+     *      corresponds to the batching dimension, and “input_size” is the size
+     *      of the input.
+     * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
+     *      A 2-D tensor of shape [num_units, input_size], where “num_units”
+     *      corresponds to the number of cell units.
+     * * 2: The input-to-forget weights (\f$W_{xf}\f$).
+     *      A 2-D tensor of shape [num_units, input_size].
+     * * 3: The input-to-cell weights (\f$W_{xc}\f$).
+     *      A 2-D tensor of shape [num_units, input_size].
+     * * 4: The input-to-output weights (\f$W_{xo}\f$).
+     *      A 2-D tensor of shape [num_units, input_size].
+     * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
+     *      A 2-D tensor of shape [num_units, output_size], where “output_size”
+     *      corresponds to either the number of cell units (i.e., “num_units”),
+     *      or the second dimension of the “projection_weights”, if defined.
+     * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
+     *      A 2-D tensor of shape [num_units, output_size].
+     * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
+     *      A 2-D tensor of shape [num_units, output_size].
+     * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
+     *      A 2-D tensor of shape [num_units, output_size].
+     * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
+     *      A 1-D tensor of shape [num_units].
+     * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
+     *      A 1-D tensor of shape [num_units].
+     * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
+     *      A 1-D tensor of shape [num_units].
+     * * 12:The input gate bias (\f$b_i\f$). Optional.
+     *      A 1-D tensor of shape [num_units].
+     * * 13:The forget gate bias (\f$b_f\f$).
+     *      A 1-D tensor of shape [num_units].
+     * * 14:The cell bias (\f$b_c\f$).
+     *      A 1-D tensor of shape [num_units].
+     * * 15:The output gate bias (\f$b_o\f$).
+     *      A 1-D tensor of shape [num_units].
+     * * 16:The projection weights (\f$W_{proj}\f$). Optional.
+     *      A 2-D tensor of shape [output_size, num_units].
+     * * 17:The projection bias (\f$b_{proj}\f$). Optional.
+     *      A 1-D tensor of shape [output_size].
+     * * 18:The output state (in) (\f$h_{t-1}\f$).
+     *      A 2-D tensor of shape [batch_size, output_size].
+     * * 19:The cell state (in) (\f$C_{t-1}\f$).
+     *      A 2-D tensor of shape [batch_size, num_units].
+     * * 20:The activation function (\f$g\f$).
+     *      A value indicating the activation function:
+     *      <ul>
+     *      <li>0: None;
+     *      <li>1: Relu;
+     *      <li>3: Relu6;
+     *      <li>4: Tanh;
+     *      <li>6: Sigmoid.
+     *      </ul>
+     * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such
+     *      that values are bound within [-cell_clip, cell_clip]. If set to 0.0
+     *      then clipping is disabled.
+     *      Until HAL version 1.2 this scalar must be of type {@link
+     *      OperandType::FLOAT32}. Since HAL version 1.2, if all the input
+     *      tensors have type {@link OperandType::TENSOR_FLOAT32}, this
+     *      scalar must be of the type {@link OperandType::FLOAT32},
+     *      otherwise if all the input tensors have the type {@link
+     *      OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link
+     *      OperandType::FLOAT16}.
+     * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the
+     *      projection layer, such that values are bound within
+     *      [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+     *      Until HAL version 1.2 this scalar must be of type {@link
+     *      OperandType::FLOAT32}. Since HAL version 1.2, if all the input
+     *      tensors have type {@link OperandType::TENSOR_FLOAT32}, this
+     *      scalar must be of the type {@link OperandType::FLOAT32},
+     *      otherwise if all the input tensors have the type {@link
+     *      OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link
+     *      OperandType::FLOAT16}.
+     * Since HAL version 1.2 there are additional inputs to this op:
+     * * 23:The input layer normalization weights.
+     *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+     *      to activation at input gate.
+     * * 24:The forget layer normalization weights.
+     *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+     *      to activation at forget gate.
+     * * 25:The cell layer normalization weights.
+     *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+     *      to activation at cell gate.
+     * * 26:The output layer normalization weights.
+     *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+     *      to activation at output gate.
+     *
+     * Outputs:
+     * * 0: The scratch buffer.
+     *      A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or
+     *      [batch_size, num_units * 4] without CIFG.
+     * * 1: The output state (out) (\f$h_t\f$).
+     *      A 2-D tensor of shape [batch_size, output_size].
+     * * 2: The cell state (out) (\f$C_t\f$).
+     *      A 2-D tensor of shape [batch_size, num_units].
+     * * 3: The output (\f$o_t\f$).
+     *      A 2-D tensor of shape [batch_size, output_size]. This is effectively
+     *      the same as the current “output state (out)” value.
+     */
+    LSTM = @1.2::OperationType:LSTM,
+
+    /**
+     * Performs an 2-D max pooling operation.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and
+     * padding.
+     *
+     * The values in the output tensor are computed as:
+     *
+     *     output[b, i, j, channel] =
+     *         max_{di, dj} (
+     *             input[b, strides[1] * i + di, strides[2] * j + dj, channel]
+     *         )
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
+     *
+     * Both explicit padding and implicit padding are supported.
+     *
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the left, in the ‘width’ dimension.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the right, in the ‘width’ dimension.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the top, in the ‘height’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the bottom, in the ‘height’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+     *      width.
+     * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+     *      height.
+     * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
+     *       Set to true to specify NCHW data layout for input0 and output0.
+     *       Available since HAL version 1.2.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
+     *      padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+     *      width.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+     *      height.
+     * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *      Available since HAL version 1.2.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, out_height, out_width, depth].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    MAX_POOL_2D = @1.2::OperationType:MAX_POOL_2D,
+
+    /**
+     * Multiplies two tensors, element-wise.
+     *
+     * Takes two input tensors of identical {@link OperandType} and compatible
+     * dimensions. The output is the product of both input tensors, optionally
+     * modified by an activation function.
+     *
+     * Two dimensions are compatible when:
+     *     1. they are equal, or
+     *     2. one of them is 1
+     *
+     * The size of the resulting output is the maximum size along each dimension
+     * of the input operands. It starts with the trailing dimensions, and works
+     * its way forward.
+     *
+     * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
+     * dimension is only compatible with 0 or 1. The size of the output
+     * dimension is zero if either of corresponding input dimension is zero.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: A tensor.
+     * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+     *      as input0.
+     * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     *
+     * Outputs:
+     * * 0: The product, a tensor of the same {@link OperandType} as input0.
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the following condition must be satisfied:
+     *      output_scale > input1_scale * input2_scale.
+     */
+    MUL = @1.2::OperationType:MUL,
+
+    /**
+     * Computes rectified linear activation on the input tensor element-wise.
+     *
+     * The output is calculated using this formula:
+     *
+     *     output = max(0, input)
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * * 0: A tensor, specifying the input.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    RELU = @1.2::OperationType:RELU,
+
+    /**
+     * Computes rectified linear 1 activation on the input tensor element-wise.
+     *
+     * The output is calculated using this formula:
+     *
+     *     output = min(1.f, max(-1.f, input))
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * * 0: A tensor, specifying the input.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
+     *
+     * Outputs:
+     * * 0: The output tensor of the same shape as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    RELU1 = @1.2::OperationType:RELU1,
+
+    /**
+     * Computes rectified linear 6 activation on the input tensor element-wise.
+     *
+     * The output is calculated using this formula:
+     *
+     *     output = min(6, max(0, input))
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * * 0: A tensor, specifying the input.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    RELU6 = @1.2::OperationType:RELU6,
+
+    /**
+     * Reshapes a tensor.
+     *
+     * Given tensor, this operation returns a tensor that has the same values as
+     * tensor, but with a newly specified shape.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * * 0: A tensor, specifying the tensor to be reshaped.
+     * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}, defining the
+     *      shape of the output tensor. The number of elements implied by shape
+     *      must be the same as the number of elements in the input tensor.
+     *
+     *      If one component of shape is the special value -1, the size of that
+     *      dimension is computed so that the total size remains constant. In
+     *      particular, a shape of [-1] flattens into 1-D. At most one component
+     *      of shape can be -1.
+     *
+     * Outputs:
+     * * 0: The output tensor, of shape specified by the input shape.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    RESHAPE = @1.2::OperationType:RESHAPE,
+
+    /**
+     * Resizes images to given size using the bilinear interpretation.
+     *
+     * Resized images must be distorted if their output aspect ratio is not the
+     * same as input aspect ratio. The corner pixels of output may not be the
+     * same as corner pixels of input.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
+     *
+     * Both resizing by shape and resizing by scale are supported.
+     *
+     * Inputs (resizing by shape):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the output
+     *      width of the output tensor.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the output
+     *      height of the output tensor.
+     * * 3: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *      Available since HAL version 1.2.
+     *
+     * Inputs (resizing by scale, since HAL version 1.2):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input. Zero batches is supported for this tensor.
+     * * 1: A scalar, specifying width_scale, the scaling factor of the width
+     *      dimension from the input tensor to the output tensor. The output
+     *      width is calculated as new_width = floor(width * width_scale).
+     *      The scalar must be of {@link OperandType::FLOAT16} if input0 is
+     *      of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} otherwise.
+     * * 2: A scalar, specifying height_scale, the scaling factor of the height
+     *      dimension from the input tensor to the output tensor. The output
+     *      height is calculated as new_height = floor(height * height_scale).
+     *      The scalar must be of {@link OperandType::FLOAT16} if input0 is
+     *      of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} otherwise.
+     * * 3: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, new_height, new_width, depth].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    RESIZE_BILINEAR = @1.2::OperationType:RESIZE_BILINEAR,
+
+    /**
+     * A basic recurrent neural network layer.
+     *
+     * This layer implements the operation:
+     * outputs = state = activation(inputs * input_weights +
+     *                              state * recurrent_weights + bias)
+     *
+     * Where:
+     * * “input_weights” is a weight matrix that multiplies the inputs;
+     * * “recurrent_weights” is a weight matrix that multiplies the current
+     *    “state” which itself is the output from the previous time step
+     *    computation;
+     * * “bias” is a bias vector (added to each output vector in the batch);
+     * * “activation” is the function passed as the “fused_activation_function”
+     *   argument (if not “NONE”).
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * The input tensors must all be the same type.
+     *
+     * Inputs:
+     * * 0: input.
+     *      A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+     *      corresponds to the batching dimension, and “input_size” is the size
+     *      of the input.
+     * * 1: weights.
+     *      A 2-D tensor of shape [num_units, input_size], where “num_units”
+     *      corresponds to the number of units.
+     * * 2: recurrent_weights.
+     *      A 2-D tensor of shape [num_units, num_units], with columns
+     *      corresponding to the weights from each unit.
+     * * 3: bias.
+     *      A 1-D tensor of shape [num_units].
+     * * 4: hidden state (in).
+     *      A 2-D tensor of shape [batch_size, num_units].
+     * * 5: fused_activation_function.
+     *      An optional {@link FusedActivationFunc} value indicating the
+     *      activation function. If “NONE” is specified then it results in a
+     *      linear activation.
+     *
+     * Outputs:
+     * * 0: hidden state (out).
+     *      A 2-D tensor of shape [batch_size, num_units].
+     *
+     * * 1: output.
+     *      A 2-D tensor of shape [batch_size, num_units]. This is effectively
+     *      the same as the current state value.
+     */
+    RNN = @1.2::OperationType:RNN,
+
+    /**
+     * Computes the softmax activation on the input tensor element-wise, per
+     * batch, by normalizing the input vector so the maximum coefficient is
+     * zero.
+     *
+     * The output is calculated using this formula:
+     *
+     *     output[batch, i] =
+     *         exp((input[batch, i] - max(input[batch, :])) * beta) /
+     *         sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
+     *
+     * For input tensor with rank other than 2, the activation will be applied
+     * independently on each 1-D slice along specified dimension.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4.
+     * Tensors with rank other than 2 or 4 are only supported since HAL version 1.2.
+     *
+     * Inputs:
+     * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
+     * * 1: A scalar, specifying the positive scaling factor for the exponent,
+     *      beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of
+     *      {@link OperandType::FLOAT32}.
+     *      If input0 is of {@link OperandType::TENSOR_FLOAT16}, then the
+     *      scalar must be of {@link OperandType::FLOAT16}.
+     * * 2: An optional {@link OperandType::INT32} scalar, default to -1,
+     *      specifying the dimension the activation would be performed on.
+     *      Negative index is used to specify axis from the end (e.g. -1 for
+     *      the last axis). Must be in the range [-n, n).
+     *      Available since HAL version 1.2.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the scale must be 1.f / 256 and the zeroPoint must be 0.
+     */
+    SOFTMAX = @1.2::OperationType:SOFTMAX,
+
+    /**
+     * Rearranges blocks of spatial data, into depth.
+     *
+     * More specifically, this op outputs a copy of the input tensor where
+     * values from the height and width dimensions are moved to the depth
+     * dimension. The value block_size indicates the input block size and how
+     * the data is moved.
+     *
+     * Chunks of data of size block_size * block_size from depth are rearranged
+     * into non-overlapping blocks of size block_size x block_size.
+     *
+     * The depth of the output tensor is input_depth * block_size * block_size.
+     * The input tensor's height and width must be divisible by block_size.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
+     *
+     * Inputs:
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+     *      specifying the input.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the block_size.
+     *      block_size must be >=1 and block_size must be a divisor of both the
+     *      input height and width.
+     * * 2: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *      Available since HAL version 1.2.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, height/block_size,
+     *      width/block_size, depth_in*block_size*block_size].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    SPACE_TO_DEPTH = @1.2::OperationType:SPACE_TO_DEPTH,
+
+    /**
+     * SVDF op is a kind of stateful layer derived from the notion that a
+     * densely connected layer that's processing a sequence of input frames can
+     * be approximated by using a singular value decomposition of each of its
+     * nodes. The implementation is based on:
+     *
+     * https://research.google.com/pubs/archive/43813.pdf
+     *
+     * P. Nakkiran, R. Alvarez, R. Prabhavalkar, C. Parada.
+     * “Compressing Deep Neural Networks using a Rank-Constrained Topology”.
+     * INTERSPEECH, 2015.
+     *
+     * It processes the incoming input using a 2-stage filtering mechanism:
+     * * stage 1 performs filtering on the "features" dimension, whose outputs
+     *   get pushed into a memory of fixed-size memory_size.
+     * * stage 2 performs filtering on the "time" dimension of the memory_size
+     *   memoized outputs of stage 1.
+     *
+     * Specifically, for rank 1, this layer implements the operation:
+     *
+     *     memory = push(conv1d(inputs, weights_feature, feature_dim,
+     *                          "PADDING_VALID"));
+     *     outputs = activation(memory * weights_time + bias);
+     *
+     * Where:
+     * * “weights_feature” is a weights matrix that processes the inputs (by
+     *   convolving the input with every “feature filter”), and whose outputs
+     *   get pushed, stacked in order, into the fixed-size “memory” (the oldest
+     *   entry gets dropped);
+     * * “weights_time” is a weights matrix that processes the “memory” (by a
+     *   batched matrix multiplication on the num_units);
+     * * “bias” is an optional bias vector (added to each output vector in the
+     *   batch); and
+     * * “activation” is the function passed as the “fused_activation_function”
+     *   argument (if not “NONE”).
+     *
+     * Each rank adds a dimension to the weights matrices by means of stacking
+     * the filters.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * All input tensors must be the same type.
+     *
+     * Inputs:
+     * * 0: input.
+     *      A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+     *      corresponds to the batching dimension, and “input_size” is the size
+     *      of the input.
+     * * 1: weights_feature.
+     *      A 2-D tensor of shape [num_units, input_size], where “num_units”
+     *      corresponds to the number of units.
+     * * 2: weights_time.
+     *      A 2-D tensor of shape [num_units, memory_size], where “memory_size”
+     *      corresponds to the fixed-size of the memory.
+     * * 3: bias.
+     *      An optional 1-D tensor of shape [num_units].
+     * * 4: state (in).
+     *      A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank].
+     * * 5: rank.
+     *      The rank of the SVD approximation.
+     * * 6: fused_activation_function.
+     *      An optional {@link FusedActivationFunc} value indicating the
+     *      activation function. If “NONE” is specified then it results in a
+     *      linear activation.
+     *
+     * Outputs:
+     * * 0: state (out).
+     *      A 2-D tensor of the same {@link OperandType} as the inputs, with shape
+     *      [batch_size, (memory_size - 1) * num_units * rank].
+     * * 1: output.
+     *      A 2-D tensor of the same {@link OperandType} as the inputs, with shape
+     *      [batch_size, num_units].
+     */
+    SVDF = @1.2::OperationType:SVDF,
+
+    /**
+     * Computes hyperbolic tangent of input tensor element-wise.
+     *
+     * The output is calculated using this formula:
+     *
+     *     output = tanh(input)
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+     *
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * * 0: A tensor, specifying the input.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the scale must be 1.f / 128 and the zeroPoint must be 128.
+     */
+    TANH = @1.2::OperationType:TANH,
+
+    /**
+     * BatchToSpace for N-dimensional tensors.
+     *
+     * This operation reshapes the batch dimension (dimension 0) into M + 1
+     * dimensions of shape block_shape + [batch], interleaves these blocks back
+     * into the grid defined by the spatial dimensions [1, ..., M], to obtain a
+     * result with the same rank as the input.
+     *
+     * This is the reverse of SpaceToBatch.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
+     *
+     * Inputs:
+     * * 0: An n-D tensor, specifying the tensor to be reshaped
+     * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
+     *      sizes for each spatial dimension of the input tensor. All values
+     *      must be >= 1.
+     * * 2: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *      Available since API level 29.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    BATCH_TO_SPACE_ND = @1.2::OperationType:BATCH_TO_SPACE_ND,
+
+    /**
+     * Element-wise division of two tensors.
+     *
+     * Takes two input tensors of identical {@link OperandType} and compatible
+     * dimensions. The output is the result of dividing the first input tensor
+     * by the second, optionally modified by an activation function.
+     *
+     * Two dimensions are compatible when:
+     *     1. they are equal, or
+     *     2. one of them is 1
+     *
+     * The size of the output is the maximum size along each dimension of the
+     * input operands. It starts with the trailing dimensions, and works its way
+     * forward.
+     *
+     * Example:
+     *     input1.dimension =    {4, 1, 2}
+     *     input2.dimension = {5, 4, 3, 1}
+     *     output.dimension = {5, 4, 3, 2}
+     *
+     * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
+     * dimension is only compatible with 0 or 1. The size of the output
+     * dimension is zero if either of corresponding input dimension is zero.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor, specifying the first input.
+     * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+     *      as input0.
+     * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     */
+    DIV = @1.2::OperationType:DIV,
+
+    /**
+     * Computes the mean of elements across dimensions of a tensor.
+     *
+     * Reduces the input tensor along the given dimensions to reduce. Unless
+     * keep_dims is true, the rank of the tensor is reduced by 1 for each entry
+     * in axis. If keep_dims is true, the reduced dimensions are retained with
+     * length 1.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: A tensor, specifying the input.
+     * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+     *      to reduce. Must be in the range
+     *      [-rank(input_tensor), rank(input_tensor)).
+     *
+     *      NOTE: When the operation was introduced, the documentation
+     *      incorrectly stated that if dimensions were empty, the operation
+     *      would reduce across all dimensions. This behavior was never
+     *      implemented.
+     *
+     * * 2: An {@link OperandType::INT32} scalar, keep_dims. If positive,
+     *      retains reduced dimensions with length 1.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be same as input0.
+     */
+    MEAN = @1.2::OperationType:MEAN,
+
+    /**
+     * Pads a tensor.
+     *
+     * This operation pads a tensor according to the specified paddings.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *   (full support since HAL version 1.2, see the output section)
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor, specifying the tensor to be padded.
+     * * 1: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+     *      for each spatial dimension of the input tensor. The shape of the
+     *      tensor must be {rank(input0), 2}.
+     *      padding[i, 0] specifies the number of elements to be padded in the
+     *      front of dimension i.
+     *      padding[i, 1] specifies the number of elements to be padded after the
+     *      end of dimension i.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0. The
+     *      output tensor has the same rank as input0, and each
+     *      dimension of the output tensor has the same size as the
+     *      corresponding dimension of the input tensor plus the size
+     *      of the padding:
+     *          output0.dimension[i] =
+     *              padding[i, 0] + input0.dimension[i] + padding[i, 1]
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     *
+     *      NOTE: Before HAL version 1.2, the pad value for
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined.
+     *      Since HAL version 1.2, the pad value is always the logical zero.
+     */
+    PAD = @1.2::OperationType:PAD,
+
+    /**
+     * SpaceToBatch for N-Dimensional tensors.
+     *
+     * This operation divides "spatial" dimensions [1, ..., M] of the input into
+     * a grid of blocks of shape block_shape, and interleaves these blocks with
+     * the "batch" dimension (0) such that in the output, the spatial dimensions
+     * [1, ..., M] correspond to the position within the grid, and the batch
+     * dimension combines both the position within a spatial block and the
+     * original batch position. Prior to division into blocks, the spatial
+     * dimensions of the input are optionally zero padded according to paddings.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *   (full support since HAL version 1.2, see the output section)
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
+     *
+     * Inputs:
+     * * 0: An n-D tensor, specifying the input.
+     * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
+     *      sizes for each spatial dimension of the input tensor. All values
+     *      must be >= 1.
+     * * 2: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+     *      for each spatial dimension of the input tensor. All values must be
+     *      >= 0. The shape of the tensor must be {M, 2}, where M is the number
+     *      of spatial dimensions.
+     *      padding[i, 0] specifies the number of element to be padded in the
+     *      front of dimension i.
+     *      padding[i, 1] specifies the number of element to be padded after the
+     *      end of dimension i.
+     * * 3: An optional {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *      Available since HAL version 1.2.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     *
+     *      NOTE: Before HAL version 1.2, the pad value for
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined.
+     *      Since HAL version 1.2, the pad value is always the logical zero.
+     */
+    SPACE_TO_BATCH_ND = @1.2::OperationType:SPACE_TO_BATCH_ND,
+
+    /**
+     * Removes dimensions of size 1 from the shape of a tensor.
+     *
+     * Given a tensor input, this operation returns a tensor of the same
+     * {@link OperandType} with all dimensions of size 1 removed. If you don't
+     * want to remove all size 1 dimensions, you can remove specific size 1
+     * dimensions by specifying the axes (input1).
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor, the tensor to be squeezed.
+     * * 1: An optional 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+     *      dimensions to squeeze. If specified only squeezes the dimensions
+     *      listed. Otherwise, squeezes all dimensions. The dimension index
+     *      starts at 0. An error must be reported if squeezing a dimension that
+     *      is not 1.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0. Contains the
+     *      same data as input, but has one or more dimensions of size 1
+     *      removed.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    SQUEEZE = @1.2::OperationType:SQUEEZE,
+
+    /**
+     * Extracts a strided slice of a tensor.
+     *
+     * Roughly speaking, this op extracts a slice of size (end - begin) / stride
+     * from the given input tensor. Starting at the location specified by begin
+     * the slice continues by adding stride to the index until all dimensions
+     * are not less than end. Note that a stride can be negative, which causes a
+     * reverse slice.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor, specifying the tensor to be sliced.
+     * * 1: begin, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+     *      starts of the dimensions of the input tensor to be sliced. The
+     *      length must be of rank(input0).
+     * * 2: end, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+     *      ends of the dimensions of the input tensor to be sliced. The length
+     *      must be of rank(input0).
+     * * 3: strides, a 1-D tensor of {@link OperandType::TENSOR_INT32}. The
+     *      strides of the dimensions of the input tensor to be sliced. The
+     *      length must be of rank(input0). The entries must be non-zero.
+     * * 4: begin_mask, an {@link OperandType::INT32} scalar. If the ith bit
+     *      of begin_mask is set, begin[i] is ignored and the fullest possible
+     *      range in that dimension is used instead.
+     * * 5: end_mask, an {@link OperandType::INT32} scalar. If the ith bit of
+     *      end_mask is set, end[i] is ignored and the fullest possible range in
+     *      that dimension is used instead.
+     * * 6: shrink_axis_mask, an {@link OperandType::INT32} scalar. If the
+     *      ith bit of shrink_axis_mask is set, the ith dimension specification
+     *      shrinks the dimensionality by 1, taking on the value at index
+     *      begin[i]. In this case, the ith specification must define a
+     *      slice of size 1, e.g. begin[i] = x, end[i] = x + 1.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k),
+     *      where k is the number of bits set in shrink_axis_mask.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    STRIDED_SLICE = @1.2::OperationType:STRIDED_SLICE,
+
+    /**
+     * Element-wise subtraction of two tensors.
+     *
+     * Takes two input tensors of identical {@link OperandType} and compatible
+     * dimensions. The output is the result of subtracting the second input
+     * tensor from the first one, optionally modified by an activation function.
+     *
+     * Two dimensions are compatible when:
+     *     1. they are equal, or
+     *     2. one of them is 1
+     *
+     * The size of the output is the maximum size along each dimension of the
+     * input operands. It starts with the trailing dimensions, and works its way
+     * forward.
+     *
+     * Example:
+     *     input1.dimension =    {4, 1, 2}
+     *     input2.dimension = {5, 4, 3, 1}
+     *     output.dimension = {5, 4, 3, 2}
+     *
+     * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
+     * dimension is only compatible with 0 or 1. The size of the output
+     * dimension is zero if either of corresponding input dimension is zero.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor, specifying the first input.
+     * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+     *      as input0.
+     * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+     */
+    SUB = @1.2::OperationType:SUB,
+
+    /**
+     * Transposes the input tensor, permuting the dimensions according to the
+     * perm tensor.
+     *
+     * The returned tensor's dimension i corresponds to the input dimension
+     * perm[i]. If perm is not given, it is set to (n-1...0), where n is the
+     * rank of the input tensor. Hence by default, this operation performs a
+     * regular matrix transpose on 2-D input Tensors.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor, specifying the tensor to be transposed.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
+     * * 1: An optional 1-D Tensor of {@link OperandType::TENSOR_INT32},
+     *      the permutation of the dimensions of the input tensor.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    TRANSPOSE = @1.2::OperationType:TRANSPOSE,
+
+    /**
+     * Computes the absolute value of a tensor, element-wise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: from 1.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     */
+    ABS = @1.2::OperationType:ABS,
+
+    /**
+     * Returns the index of the largest element along an axis.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: An n-D tensor specifying the input. Must be non-empty.
+     * * 1: An {@link OperandType::INT32} scalar specifying the axis to
+     *      reduce across. Negative index is used to specify axis from the
+     *      end (e.g. -1 for the last axis). Must be in the range [-n, n).
+     *
+     * Outputs:
+     * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
+     */
+    // There is no underscore in ARG_MAX to avoid name conflict with
+    // the macro defined in libc/kernel/uapi/linux/limits.h.
+    ARGMAX = @1.2::OperationType:ARGMAX,
+
+    /**
+     * Returns the index of the smallest element along an axis.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: An n-D tensor specifying the input. Must be non-empty.
+     * * 1: An {@link OperandType::INT32} scalar specifying the axis to
+     *      reduce across. Negative index is used to specify axis from the
+     *      end (e.g. -1 for the last axis). Must be in the range [-n, n).
+     *
+     * Outputs:
+     * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
+     */
+    ARGMIN = @1.2::OperationType:ARGMIN,  // See ARGMAX for naming discussion.
+
+    /**
+     * Transform axis-aligned bounding box proposals using bounding box deltas.
+     *
+     * Given the positions of bounding box proposals and the corresponding
+     * bounding box deltas for each class, return the refined bounding box
+     * regions. The resulting bounding boxes are cliped against the edges of
+     * the image.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT16_ASYMM}
+     *
+     * Inputs:
+     * * 0: A 2-D Tensor of shape [num_rois, 4], specifying the locations of the
+     *      bounding box proposals, each line with format [x1, y1, x2, y2].
+     *      For tensor of type {@link OperandType::TENSOR_QUANT16_ASYMM},
+     *      the zeroPoint must be 0 and the scale must be 0.125. Zero num_rois
+     *      is supported for this tensor.
+     * * 1: A 2-D Tensor of shape [num_rois, num_classes * 4], specifying the
+     *      bounding box delta for each region of interest and each class. The
+     *      bounding box deltas are organized in the following order
+     *      [dx, dy, dw, dh], where dx and dy is the relative correction factor
+     *      for the center position of the bounding box with respect to the width
+     *      and height, dw and dh is the log-scale relative correction factor
+     *      for the width and height. For input0 of type
+     *      {@link OperandType::TENSOR_QUANT16_ASYMM}, this tensor should be
+     *      of {@link OperandType::TENSOR_QUANT8_ASYMM}. Zero num_rois is
+     *      supported for this tensor.
+     * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+     *      [num_rois], specifying the batch index of each box. Boxes with
+     *      the same batch index are grouped together. Zero num_rois is
+     *      supported for this tensor.
+     * * 3: A 2-D Tensor of shape [batches, 2], specifying the information of
+     *      each image in the batch, each line with format
+     *      [image_height, image_width].
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0, with shape
+     *      [num_rois, num_classes * 4], specifying the coordinates of each
+     *      output bounding box for each class, with format [x1, y1, x2, y2].
+     *      For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+     *      scale must be 0.125 and the zero point must be 0.
+     */
+    AXIS_ALIGNED_BBOX_TRANSFORM = @1.2::OperationType:AXIS_ALIGNED_BBOX_TRANSFORM,
+
+    /**
+     * Performs a forward LSTM on the input followed by a backward LSTM.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: 3, either time-major or batch-major.
+     *
+     * All input and output tensors must be of the same type.
+     *
+     *
+     * Inputs:
+     * * 0: The input.
+     *      A 3-D tensor of shape:
+     *        If time-major: [max_time, batch_size, input_size]
+     *        If batch-major: [batch_size, max_time, input_size]
+     *      where "max_time" is the number of timesteps (sequence length),
+     *      "batch_size" corresponds to the batching dimension, and
+     *      "input_size" is the size of the input.
+     * * 1: The forward input-to-input weights. Optional.
+     *      A 2-D tensor of shape [fw_num_units, input_size], where “fw_num_units”
+     *      corresponds to the number of forward cell units.
+     * * 2: The forward input-to-forget weights.
+     *      A 2-D tensor of shape [fw_num_units, input_size].
+     * * 3: The forward input-to-cell weights.
+     *      A 2-D tensor of shape [fw_num_units, input_size].
+     * * 4: The forward input-to-output weights.
+     *      A 2-D tensor of shape [fw_num_units, input_size].
+     * * 5: The forward recurrent-to-input weights. Optional.
+     *      A 2-D tensor of shape [fw_num_units, fw_output_size], where “fw_output_size”
+     *      corresponds to either the number of cell units (i.e., fw_num_units),
+     *      or the second dimension of the “fw_projection_weights”, if defined.
+     * * 6: The forward recurrent-to-forget weights.
+     *      A 2-D tensor of shape [fw_num_units, fw_output_size].
+     * * 7: The forward recurrent-to-cell weights.
+     *      A 2-D tensor of shape [fw_num_units, fw_output_size].
+     * * 8: The forward recurrent-to-output weights.
+     *      A 2-D tensor of shape [fw_num_units, fw_output_size].
+     * * 9: The forward cell-to-input weights. Optional.
+     *      A 1-D tensor of shape [fw_num_units].
+     * * 10: The forward cell-to-forget weights. Optional.
+     *       A 1-D tensor of shape [fw_num_units].
+     * * 11: The forward cell-to-output weights. Optional.
+     *       A 1-D tensor of shape [fw_num_units].
+     * * 12: The forward input gate bias. Optional.
+     *       A 1-D tensor of shape [fw_num_units].
+     * * 13: The forward forget gate bias.
+     *       A 1-D tensor of shape [fw_num_units].
+     * * 14: The forward cell gate bias.
+     *       A 1-D tensor of shape [fw_num_units].
+     * * 15: The forward output gate bias.
+     *       A 1-D tensor of shape [fw_num_units].
+     * * 16: The forward projection weights. Optional.
+     *       A 2-D tensor of shape [fw_output_size, fw_num_units].
+     * * 17: The forward projection bias. Optional.
+     *       A 1-D tensor of shape [fw_output_size].
+     * * 18: The backward input-to-input weights. Optional.
+     *       A 2-D tensor of shape [bw_num_units, input_size], where “bw_num_units”
+     *       corresponds to the number of backward cell units.
+     * * 19: The backward input-to-forget weights.
+     *       A 2-D tensor of shape [bw_num_units, input_size].
+     * * 20: The backward input-to-cell weights.
+     *       A 2-D tensor of shape [bw_num_units, input_size].
+     * * 21: The backward input-to-output weights.
+     *       A 2-D tensor of shape [bw_num_units, input_size].
+     * * 22: The backward recurrent-to-input weights. Optional.
+     *       A 2-D tensor of shape [bw_num_units, bw_output_size], where “bw_output_size”
+     *       corresponds to either the number of cell units (i.e., “bw_num_units”),
+     *       or the second dimension of the “bw_projection_weights”, if defined.
+     * * 23: The backward recurrent-to-forget weights.
+     *       A 2-D tensor of shape [bw_num_units, bw_output_size].
+     * * 24: The backward recurrent-to-cell weights.
+     *       A 2-D tensor of shape [bw_num_units, bw_output_size].
+     * * 25: The backward recurrent-to-output weights.
+     *       A 2-D tensor of shape [bw_num_units, bw_output_size].
+     * * 26: The backward cell-to-input weights. Optional.
+     *       A 1-D tensor of shape [bw_num_units].
+     * * 27: The backward cell-to-forget weights. Optional.
+     *       A 1-D tensor of shape [bw_num_units].
+     * * 28: The backward cell-to-output weights. Optional.
+     *       A 1-D tensor of shape [bw_num_units].
+     * * 29: The backward input gate bias. Optional.
+     *       A 1-D tensor of shape [bw_num_units].
+     * * 30: The backward forget gate bias.
+     *       A 1-D tensor of shape [bw_num_units].
+     * * 31: The backward cell gate bias.
+     *       A 1-D tensor of shape [bw_num_units].
+     * * 32: The backward output gate bias.
+     *       A 1-D tensor of shape [bw_num_units].
+     * * 33: The backward projection weights. Optional.
+     *       A 2-D tensor of shape [bw_output_size, bw_num_units].
+     * * 34: The backward projection bias. Optional.
+     *       A 1-D tensor of shape [bw_output_size].
+     * * 35: The forward input activation state.
+     *       A 2-D tensor of shape [batch_size, bw_output_size].
+     * * 36: The forward input cell state.
+     *       A 2-D tensor of shape [batch_size, bw_num_units].
+     * * 37: The backward input activation state.
+     *       A 2-D tensor of shape [batch_size, bw_output_size].
+     * * 38: The backward input cell state.
+     *       A 2-D tensor of shape [batch_size, bw_num_units].
+     * * 39: The auxiliary input. Optional.
+     *       A 3-D tensor of shape [max_time, batch_size, input_size], where “batch_size”
+     *       corresponds to the batching dimension, and “input_size” is the size
+     *       of the input.
+     * * 40: The forward auxiliary input-to-input weights. Optional.
+     *       A 2-D tensor of shape [fw_num_units, input_size].
+     * * 41: The forward auxiliary input-to-forget weights. Optional.
+     *       A 2-D tensor of shape [fw_num_units, input_size].
+     * * 42: The forward auxiliary input-to-cell weights. Optional.
+     *       A 2-D tensor of shape [fw_num_units, input_size].
+     * * 43: The forward auxiliary input-to-output weights. Optional.
+     *       A 2-D tensor of shape [fw_num_units, input_size].
+     * * 44: The backward auxiliary input-to-input weights. Optional.
+     *       A 2-D tensor of shape [bw_num_units, input_size].
+     * * 45: The backward auxiliary input-to-forget weights. Optional.
+     *       A 2-D tensor of shape [bw_num_units, input_size].
+     * * 46: The backward auxiliary input-to-cell weights. Optional.
+     *       A 2-D tensor of shape [bw_num_units, input_size].
+     * * 47: The backward auxiliary input-to-output weights. Optional.
+     *       A 2-D tensor of shape [bw_num_units, input_size].
+     * * 48: The activation function.
+     *       A value indicating the activation function:
+     *       <ul>
+     *       <li>0: None;
+     *       <li>1: Relu;
+     *       <li>3: Relu6;
+     *       <li>4: Tanh;
+     *       <li>6: Sigmoid.
+     *       </ul>
+     * * 49: The clipping threshold for the cell state, such
+     *       that values are bound within [-cell_clip, cell_clip]. If set to 0.0
+     *       then clipping is disabled.
+     *       If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
+     *       this scalar must be of the type {@link OperandType::FLOAT32},
+     *       otherwise if all the input tensors have the type {@link OperandType::TENSOR_FLOAT16},
+     *       this scalar must be of type {@link OperandType::FLOAT16}.
+     * * 50: The clipping threshold for the output from the
+     *       projection layer, such that values are bound within
+     *       [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+     *       If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
+     *       this scalar must be of the type {@link OperandType::FLOAT32},
+     *       otherwise if all the input tensors have the type {@link OperandType::TENSOR_FLOAT16},
+     *       this scalar must be of type {@link OperandType::FLOAT16}.
+     * * 51: merge_outputs
+     *       An {@link OperandType::BOOL} scalar specifying if the outputs
+     *       from forward and backward cells should be merged.
+     * * 52: time_major
+     *       An {@link OperandType::BOOL} scalar specifying the shape format
+     *       of input and output tensors.
+     * * 53: The forward input layer normalization weights. Optional.
+     *       A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+     *       to activation at input gate.
+     * * 54: The forward forget layer normalization weights. Optional.
+     *       A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+     *       to activation at forget gate.
+     * * 55: The forward cell layer normalization weights. Optional.
+     *       A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+     *       to activation at cell gate.
+     * * 56: The forward output layer normalization weights. Optional.
+     *       A 1-D tensor of shape [fw_num_units]. Used to rescale normalized inputs
+     *       to activation at output gate.
+     * * 57: The backward input layer normalization weights. Optional.
+     *       A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+     *       to activation at input gate.
+     * * 58: The backward forget layer normalization weights. Optional.
+     *       A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+     *       to activation at forget gate.
+     * * 59: The backward cell layer normalization weights. Optional.
+     *       A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+     *       to activation at cell gate.
+     * * 60: The backward output layer normalization weights. Optional.
+     *       A 1-D tensor of shape [bw_num_units]. Used to rescale normalized inputs
+     *       to activation at output gate.
+     *
+     * Outputs:
+     * * 0: The forward output.
+     *      A 3-D tensor of shape:
+     *        If time-major and not merge_outputs:
+     *          [max_time, batch_size, fw_output_size]
+     *        If time-major and merge_outputs:
+     *          [max_time, batch_size, fw_output_size + bw_output_size]
+     *        If batch-major and not merge_outputs:
+     *          [batch_size, max_time, fw_output_size]
+     *        If batch-major and merge_outputs:
+     *          [batch_size, max_time, fw_output_size + bw_output_size]
+     * * 1: The backward output.  Unused if merge_outputs is true.
+     *      A 3-D tensor of shape:
+     *        If time-major: [max_time, batch_size, bw_output_size]
+     *        If batch-major: [batch_size, max_time, bw_output_size]
+     */
+    BIDIRECTIONAL_SEQUENCE_LSTM = @1.2::OperationType:BIDIRECTIONAL_SEQUENCE_LSTM,
+
+    /**
+     * A recurrent neural network layer that applies a basic RNN cell to a
+     * sequence of inputs in forward and backward directions.
+     *
+     * This Op unrolls the input along the sequence dimension, and implements
+     * the following operation for each element in the sequence s =
+     * 1...sequence_length:
+     *   fw_outputs[s] = fw_state = activation(inputs[s] * fw_input_weights’ +
+     *          fw_state * fw_recurrent_weights’ + fw_bias)
+     *
+     * And for each element in sequence t = sequence_length : 1
+     *   bw_outputs[t] = bw_state = activation(inputs[t] * bw_input_weights’ +
+     *          bw_state * bw_recurrent_weights’ + bw_bias)
+     *
+     * Where:
+     * * “{fw,bw}_input_weights” is a weight matrix that multiplies the inputs;
+     * * “{fw,bw}_recurrent_weights” is a weight matrix that multiplies the
+     *    current “state” which itself is the output from the previous time step
+     *    computation;
+     * * “{fw,bw}_bias” is a bias vector (added to each output vector in the
+     *    batch);
+     * * “activation” is the function passed as the “fused_activation_function”
+     *   argument (if not “NONE”).
+     *
+     * The op also supports an auxiliary input. Regular cell feeds one input
+     * into the two RNN cells in the following way:
+     *
+     *       INPUT  (INPUT_REVERSED)
+     *         |         |
+     *    ---------------------
+     *    | FW_RNN     BW_RNN |
+     *    ---------------------
+     *         |         |
+     *      FW_OUT     BW_OUT
+     *
+     * An op with an auxiliary input takes two inputs and feeds them into the
+     * RNN cells in the following way:
+     *
+     *       AUX_INPUT   (AUX_INPUT_REVERSED)
+     *           |             |
+     *     INPUT | (INPUT_R'D.)|
+     *       |   |       |     |
+     *    -----------------------
+     *    |  \  /        \    / |
+     *    | FW_RNN       BW_RNN |
+     *    -----------------------
+     *         |           |
+     *      FW_OUT      BW_OUT
+     *
+     * While stacking this op on top of itself, this allows to connect both
+     * forward and backward outputs from previous cell to the next cell's
+     * inputs.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * The input tensors must all be the same type.
+     *
+     * Inputs:
+     * * 0: input.
+     *      A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+     *      it is set to true, then the input has a shape [maxTime, batchSize,
+     *      inputSize], otherwise the input has a shape [batchSize, maxTime,
+     *      inputSize].
+     * * 1: fwWeights.
+     *      A 2-D tensor of shape [fwNumUnits, inputSize].
+     * * 2: fwRecurrentWeights.
+     *      A 2-D tensor of shape [fwNumUnits, fwNumUnits].
+     * * 3: fwBias.
+     *      A 1-D tensor of shape [fwNumUnits].
+     * * 4: fwHiddenState.
+     *      A 2-D tensor of shape [batchSize, fwNumUnits]. Specifies a hidden
+     *      state input for the first time step of the computation.
+     * * 5: bwWeights.
+     *      A 2-D tensor of shape [bwNumUnits, inputSize].
+     * * 6: bwRecurrentWeights.
+     *      A 2-D tensor of shape [bwNumUnits, bwNumUnits].
+     * * 7: bwBias.
+     *      A 1-D tensor of shape [bwNumUnits].
+     * * 8: bwHiddenState
+     *      A 2-D tensor of shape [batchSize, bwNumUnits]. Specifies a hidden
+     *      state input for the first time step of the computation.
+     * * 9: auxInput.
+     *      A 3-D tensor. The shape is the same as of the input 0.
+     * * 10:fwAuxWeights.
+     *      A 2-D tensor of shape [fwNumUnits, inputSize].
+     * * 11:bwAuxWeights.
+     *      A 2-D tensor of shape [bwNumUnits, inputSize].
+     * * 12:fusedActivationFunction.
+     *      A {@link FusedActivationFunc} value indicating the activation function. If
+     *      “NONE” is specified then it results in a linear activation.
+     * * 13:timeMajor
+     *      An {@link OperandType::BOOL} scalar specifying the shape format
+     *      of input and output tensors.
+     * * 14:mergeOutputs
+     *      An {@link OperandType::BOOL} scalar specifying if the outputs
+     *      from forward and backward cells are separate (if set to false) or
+     *      concatenated (if set to true).
+     * Outputs:
+     * * 0: fwOutput.
+     *      A 3-D tensor. The first two dimensions of the shape are defined by
+     *      the input 6 (timeMajor) and the third dimension is defined by the
+     *      input 14 (mergeOutputs). If timeMajor is set to true, then the first
+     *      two dimensions are [maxTime, batchSize], otherwise they are set to
+     *      [batchSize, maxTime]. If mergeOutputs is set to true, then the third
+     *      dimension is equal to (fwNumUnits + bwNumUnits), otherwise it is set
+     *      to fwNumUnits.
+     * * 1: bwOutput.
+     *      A 3-D tensor. If the input 14 (mergeOutputs) is set to true, then
+     *      this tensor is not produced. The shape is defined by the input 6
+     *      (timeMajor). If it is set to true, then the shape is set to
+     *      [maxTime, batchSize, bwNumUnits], otherwise the shape is set to
+     *      [batchSize, maxTime, bwNumUnits].
+     */
+    BIDIRECTIONAL_SEQUENCE_RNN = @1.2::OperationType:BIDIRECTIONAL_SEQUENCE_RNN,
+
+    /**
+     * Greedily selects a subset of bounding boxes in descending order of score.
+     *
+     * This op applies NMS algorithm to each class. In each loop of execution,
+     * the box with maximum score gets selected and removed from the pending set.
+     * The scores of the rest of boxes are lowered according to the
+     * intersection-over-union (IOU) overlapping with the previously selected
+     * boxes and a specified NMS kernel method. Any boxes with score less
+     * than a threshold are removed from the pending set.
+     *
+     * Three NMS kernels are supported:
+     * * Hard:     score_new = score_old * (1 if IoU < threshold else 0)
+     * * Linear:   score_new = score_old * (1 if IoU < threshold else 1 - IoU)
+     * * Gaussian: score_new = score_old * exp(- IoU^2 / sigma)
+     *
+     * Axis-aligned bounding boxes are represented by its upper-left corner
+     * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
+     * bounding box should satisfy x1 <= x2 and y1 <= y2.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Inputs:
+     * * 0: A 2-D Tensor of shape [num_rois, num_classes], specifying the score
+     *      of each bounding box proposal. The boxes are grouped by batches in the
+     *      first dimension. Zero num_rois is supported for this tensor.
+     * * 1: A 2-D Tensor specifying the bounding boxes of shape
+     *      [num_rois, num_classes * 4], organized in the order [x1, y1, x2, y2].
+     *      The boxes are grouped by batches in the first dimension. The sequential
+     *      order of the boxes corresponds with input0. For input0 of type
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of
+     *      {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint of 0 and
+     *      scale of 0.125. Zero num_rois is supported for this tensor.
+     * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+     *      [num_rois], specifying the batch index of each box. Boxes with
+     *      the same batch index are grouped together.
+     * * 3: An {@link OperandType::FLOAT32} scalar, score_threshold. Boxes
+     *      with scores lower than the threshold are filtered before sending
+     *      to the NMS algorithm.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the maximum
+     *      number of selected bounding boxes for each image. Set to a negative
+     *      value for unlimited number of output bounding boxes.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the NMS
+     *      kernel method, options are 0:hard, 1:linear, 2:gaussian.
+     * * 6: An {@link OperandType::FLOAT32} scalar, specifying the IoU
+     *      threshold in hard and linear NMS kernel. This field is ignored if
+     *      gaussian kernel is selected.
+     * * 7: An {@link OperandType::FLOAT32} scalar, specifying the sigma in
+     *      gaussian NMS kernel. This field is ignored if gaussian kernel is
+     *      not selected.
+     * * 8: An {@link OperandType::FLOAT32} scalar, nms_score_threshold.
+     *      Boxes with scores lower than the threshold are dropped during the
+     *      score updating phase in soft NMS.
+     *
+     * Outputs:
+     * * 0: A 1-D Tensor of the same {@link OperandType} as input0, with shape
+     *      [num_output_rois], specifying the score of each output box. The boxes
+     *      are grouped by batches, but the sequential order in each batch is not
+     *      guaranteed. For type of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the scale and zero point must be the same as input0.
+     * * 1: A 2-D Tensor of the same {@link OperandType} as input1, with shape
+     *      [num_output_rois, 4], specifying the coordinates of each
+     *      output bounding box with the same format as input1. The sequential
+     *      order of the boxes corresponds with output0. For type of
+     *      {@link OperandType::TENSOR_QUANT16_ASYMM}, the scale must be
+     *      0.125 and the zero point must be 0.
+     * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+     *      [num_output_rois], specifying the class of each output box. The
+     *      sequential order of the boxes corresponds with output0.
+     * * 3: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+     *      [num_output_rois], specifying the batch index of each box. Boxes
+     *      with the same batch index are grouped together.
+     */
+    BOX_WITH_NMS_LIMIT = @1.2::OperationType:BOX_WITH_NMS_LIMIT,
+
+    /**
+     * Casts a tensor to a new type.
+     *
+     * This operation ignores the scale and zeroPoint of quanized tensors,
+     * e.g. it treats a {@link OperandType::TENSOR_QUANT8_ASYMM} input
+     * as a tensor of uint8 values.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: A tensor.
+     *
+     * Outputs:
+     * * 0: A tensor with the same shape as input0.
+     */
+    CAST = @1.2::OperationType:CAST,
+
+    /**
+     * Shuffle the channels of the input tensor.
+     *
+     * Given an input tensor and a integer value of num_groups, CHANNEL_SHUFFLE
+     * divide the channel dimension into num_groups groups, and reorganize the
+     * channels by grouping channels with the same index in each group.
+     *
+     * Along the channel dimension, the output is calculated using this formula:
+     *
+     *     output_channel[k * num_groups + g] = input_channel[g * group_size + k]
+     *
+     * where group_size = num_channels / num_groups
+     *
+     * The number of channels must be divisible by num_groups.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor, specifying the tensor to be shuffled.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the number of
+     *      groups.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the dimension
+     *      channel shuffle would be performed on. Negative index is used to
+     *      specify axis from the end (e.g. -1 for the last axis). Must be in
+     *      the range [-n, n).
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} and same shape as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    CHANNEL_SHUFFLE = @1.2::OperationType:CHANNEL_SHUFFLE,
+
+    /**
+     * Apply postprocessing steps to bounding box detections.
+     *
+     * Bounding box detections are generated by applying transformation on a set
+     * of predefined anchors with the bounding box deltas from bounding box
+     * regression. A final step of hard NMS is applied to limit the number of
+     * returned boxes.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Inputs:
+     * * 0: A 3-D Tensor of shape [batches, num_anchors, num_classes], specifying
+     *      the score of each anchor with each class. Class 0 for each
+     *      [batches, num_anchors, 0] is background and will be ignored.
+     * * 1: A 3-D Tensor of shape [batches, num_anchors, length_box_encoding], with
+     *      the first four values in length_box_encoding specifying the bounding
+     *      box deltas. The box deltas are encoded in the order of [dy, dx, dh, dw],
+     *      where dy and dx is the linear-scale relative correction factor for the
+     *      center position of the bounding box with respect to the width and height,
+     *      dh and dw is the log-scale relative correction factor for the width and
+     *      height. All the entries in length_box_encoding beyond the first four
+     *      values are ignored in this operation.
+     * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
+     *      predefined anchor, with format [ctr_y, ctr_x, h, w], where ctr_y and
+     *      ctr_x are the center position of the box, and h and w are the height
+     *      and the width.
+     * * 3: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+     *      factor for dy in bounding box deltas.
+     * * 4: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+     *      factor for dx in bounding box deltas.
+     * * 5: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+     *      factor for dh in bounding box deltas.
+     * * 6: An {@link OperandType::FLOAT32} scalar, specifying the scaling
+     *      factor for dw in bounding box deltas.
+     * * 7: An {@link OperandType::BOOL} scalar, set to true to use regular
+     *      multi-class NMS algorithm that do NMS separately for each class,
+     *      set to false for a faster algorithm that only do one single NMS
+     *      using the highest class score..
+     * * 8: An {@link OperandType::INT32} scalar, max_num_detections, specifying
+     *      the maximum number of boxes for the output. Boxes with the lowest
+     *      scores are discarded to meet the limit.
+     * * 9: An {@link OperandType::INT32} scalar, only used when input7 is
+     *      set to false, specifying the maximum number of classes per detection.
+     * * 10: An {@link OperandType::INT32} scalar, only used when input7 is
+     *       set to true, specifying the maximum number of detections when
+     *       applying NMS algorithm for each single class.
+     * * 11: A scalar, score_threshold. Boxes with scores lower than the
+     *       threshold are filtered before sending to the NMS algorithm. The
+     *       scalar must be of {@link OperandType::FLOAT16} if input0 is of
+     *       {@link OperandType::TENSOR_FLOAT16} and of
+     *       {@link OperandType::FLOAT32} if input0 is of
+     *       {@link OperandType::TENSOR_FLOAT32}.
+     * * 12: A scalar, specifying the IoU threshold for hard NMS. The scalar
+     *       must be of {@link OperandType::FLOAT16} if input0 is of
+     *       {@link OperandType::TENSOR_FLOAT16} and of
+     *       {@link OperandType::FLOAT32} if input0 is of
+     *       {@link OperandType::TENSOR_FLOAT32}.
+     * * 13: An {@link OperandType::BOOL} scalar, set to true to include
+     *       background class in the list of label map for the output, set
+     *       to false to not include the background. When the background
+     *       class is included, it has label 0 and the output classes start
+     *       at 1 in the label map, otherwise, the output classes start at 0.
+     *
+     * Outputs:
+     * * 0: A 2-D tensor of the same {@link OperandType} as input0, with shape
+     *      [batches, max_num_detections], specifying the score of each output
+     *      detections.
+     * * 1: A 3-D tensor of shape [batches, max_num_detections, 4], specifying the
+     *      coordinates of each output bounding box, with format
+     *      [y1, x1, y2, x2].
+     * * 2: A 2-D {@link OperandType::TENSOR_INT32} tensor, of shape
+     *      [batches, max_num_detections], specifying the class label for each
+     *      output detection.
+     * * 3: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape [batches],
+     *      specifying the number of valid output detections for each batch.
+     */
+    DETECTION_POSTPROCESSING = @1.2::OperationType:DETECTION_POSTPROCESSING,
+
+    /**
+     * For input tensors x and y, computes x == y elementwise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_BOOL8}
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * This operation supports broadcasting.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+     *      with input0.
+     *
+     * Outputs:
+     * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+     */
+    EQUAL = @1.2::OperationType:EQUAL,
+
+    /**
+     * Computes exponential of x element-wise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: from 1.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     */
+    EXP = @1.2::OperationType:EXP,
+
+    /**
+     * Inserts a dimension of 1 into a tensor's shape.
+     *
+     * Given a tensor input, this operation inserts a dimension of 1 at the
+     * given dimension index of input's shape. The dimension index starts at
+     * zero; if you specify a negative dimension index, it is counted backward
+     * from the end.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: An n-D tensor.
+     * * 1: An {@link OperandType::INT32} scalar specifying the dimension
+     *      index to expand. Must be in the range [-(n + 1), (n + 1)).
+     *
+     * Outputs:
+     * * 0: An (n + 1)-D tensor with the same {@link OperandType} and data as
+     *      input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    EXPAND_DIMS = @1.2::OperationType:EXPAND_DIMS,
+
+    /**
+     * Gathers values along an axis.
+     *
+     * Produces an output tensor with shape
+     *     input0.dimension[:axis] + indices.dimension + input0.dimension[axis + 1:]
+     * where:
+     *     # Vector indices (output is rank(input0)).
+     *     output[a_0, ..., a_n, i, b_0, ..., b_n] =
+     *       input0[a_0, ..., a_n, indices[i], b_0, ..., b_n]
+     *
+     *     # Higher rank indices (output is rank(input0) + rank(indices) - 1).
+     *     output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
+     *       input0[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: An n-D tensor from which to gather values.
+     * * 1: An {@link OperandType::INT32} scalar specifying the axis.
+     *      Negative index is used to specify axis from the end
+     *      (e.g. -1 for the last axis). Must be in the range [-n, n).
+     * * 2: A k-D tensor {@link OperandType::TENSOR_INT32} of indices.
+     *      The values must be in the bounds of the corresponding dimensions
+     *      of input0.
+     *
+     * Outputs:
+     * * 0: An (n + k - 1)-D tensor with the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    GATHER = @1.2::OperationType:GATHER,
+
+    /**
+     * Generate aixs-aligned bounding box proposals.
+     *
+     * Bounding box proposals are generated by applying transformation on a set
+     * of predefined anchors with the bounding box deltas from bounding box
+     * regression. A final step of hard NMS is applied to limit the number of
+     * returned boxes.
+     *
+     * Axis-aligned bounding boxes are represented by its upper-left corner
+     * coordinate (x1,y1) and lower-right corner coordinate (x2,y2). A valid
+     * bounding box should satisfy x1 <= x2 and y1 <= y2.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Inputs:
+     * * 0: A 4-D Tensor specifying the score of each anchor at each
+     *      location. With "NHWC" data layout, the tensor shape is
+     *      [batches, height, width, num_anchors]. With "NCHW" data layout,
+     *      the tensor shape is [batches, num_anchors, height, width].
+     * * 1: A 4-D Tensor specifying the bounding box deltas. With "NHWC" data
+     *      layout, the tensor shape is [batches, height, width, num_anchors * 4].
+     *      With "NCHW" data layout, the tensor shape is
+     *      [batches, num_anchors * 4, height, width]. The box deltas are encoded
+     *      in the order of [dx, dy, dw, dh], where dx and dy is the linear-scale
+     *      relative correction factor for the center position of the bounding box
+     *      with respect to the width and height, dw and dh is the log-scale
+     *      relative correction factor for the width and height. The last
+     *      dimensions is the channel dimension.
+     * * 2: A 2-D Tensor of shape [num_anchors, 4], specifying the shape of each
+     *      predefined anchor, with format [x1, y1, x2, y2]. For input0 of type
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should be of
+     *      {@link OperandType::TENSOR_QUANT16_SYMM}, with scale of 0.125.
+     * * 3: A 2-D Tensor of shape [batches, 2], specifying the size of
+     *      each image in the batch, with format [image_height, image_width].
+     *      For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM}, this
+     *      tensor should be of {@link OperandType::TENSOR_QUANT16_SYMM}, with
+     *      scale of 0.125.
+     * * 4: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+     *      from the height of original image to the height of feature map.
+     * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+     *      from the width of original image to the width of feature map.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the maximum
+     *      number of boxes before going into the hard NMS algorithm. Boxes
+     *      with the lowest scores are discarded to meet the limit. Set to
+     *      a non-positive value for unlimited number.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the maximum
+     *      number of boxes returning from the hard NMS algorithm. Boxes
+     *      with the lowest scores are discarded to meet the limit. Set to
+     *      a non-positive value for unlimited number.
+     * * 8: An {@link OperandType::FLOAT32} scalar, specifying the IoU
+     *      threshold for hard NMS.
+     * * 9: An {@link OperandType::FLOAT32} scalar, min_size. Boxes with
+     *      height or width lower than the absolute threshold are filtered out.
+     * * 10: An {@link OperandType::BOOL} scalar, set to true to specify
+     *       NCHW data layout for input0 and input1. Set to false for NHWC.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0, of shape
+     *      [num_output_rois], specifying the score of each output box.
+     *      The boxes are grouped by batches, but the sequential order in
+     *      each batch is not guaranteed. For type of
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the scale and zero
+     *      point must be the same as input0.
+     * * 1: A tensor of the same {@link OperandType} as input3, of shape
+     *      [num_output_rois, 4], specifying the coordinates of each output
+     *      bounding box for each class, with format [x1, y1, x2, y2].
+     *      The sequential order of the boxes corresponds with output0.
+     *      For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+     *      scale must be 0.125 and the zero point must be 0.
+     * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+     *      [num_output_rois], specifying the batch index of each box. Boxes
+     *      with the same batch index are grouped together.
+     */
+    GENERATE_PROPOSALS = @1.2::OperationType:GENERATE_PROPOSALS,
+
+    /**
+     * For input tensors x and y, computes x > y elementwise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_BOOL8}
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * This operation supports broadcasting.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+     *      with input0.
+     *
+     * Outputs:
+     * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+     */
+    GREATER = @1.2::OperationType:GREATER,
+    /**
+     * For input tensors x and y, computes x >= y elementwise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_BOOL8}
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * This operation supports broadcasting.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+     *      with input0.
+     *
+     * Outputs:
+     * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+     */
+    GREATER_EQUAL = @1.2::OperationType:GREATER_EQUAL,
+
+    /**
+     * Performs a grouped 2-D convolution operation.
+     *
+     * Given an input tensor of shape [batches, height, width, depth_in] and a
+     * filter tensor of shape [depth_out, filter_height, filter_width, depth_group]
+     * containing depth_out convolutional filters of depth depth_group, GROUPED_CONV
+     * applies a group of different filters to each input channel group, then
+     * concatenates the results together.
+     *
+     * Specifically, the input channels are divided into num_groups groups, each with
+     * depth depth_group, i.e. depth_in = num_groups * depth_group. The convolutional
+     * filters are also divided into num_groups groups, i.e. depth_out is divisible
+     * by num_groups. GROUPED_CONV applies each group of filters to the corresponding
+     * input channel group, and the result are concatenated together.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and
+     * padding.
+     *
+     * The values in the output tensor are computed as:
+     *
+     *     output[b, i, j, g * channel_multiplier + q] =
+     *         sum_{di, dj, dk} (
+     *             input[b, strides[1] * i + di, strides[2] * j + dj,
+     *                   g * depth_group + dk] *
+     *             filter[g * channel_multiplier + q, di, dj, dk]
+     *         ) + bias[channel]
+     *
+     * where channel_multiplier = depth_out / num_groups
+     *
+     * Supported tensor {@link OperandType} configurations:
+     * * 16 bit floating point:
+     * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+     *
+     * * 32 bit floating point:
+     * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+     *
+     * * Quantized:
+     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+     * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+     * * * input.scale * filter.scale).
+     *
+     * * Quantized with symmetric per channel quantization for the filter:
+     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+     * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+     * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+     * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     *
+     * Both explicit padding and implicit padding are supported.
+     *
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+     *      specifying the input, where depth_in = num_groups * depth_group.
+     * * 1: A 4-D tensor, of shape
+     *      [depth_out, filter_height, filter_width, depth_group], specifying
+     *      the filter, where depth_out must be divisible by num_groups.  For
+     *      tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+     *      the channel dimension (channelDim at
+     *      {@link SymmPerChannelQuantParams}) must be set to 0.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32} or
+     *      {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+     *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+     *      of 0 and bias_scale == input_scale * filter_scale. For filter tensor
+     *      of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+     *      0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+     *      bias_scale[i] = input_scale * filter_scale[i].
+     * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the left, in the ‘width’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the right, in the ‘width’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the top, in the ‘height’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the bottom, in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 9: An {@link OperandType::INT32} scalar, specifying the number of
+            groups.
+     * * 10: An {@link OperandType::INT32} scalar, and has to be one of the
+     *       {@link FusedActivationFunc} values. Specifies the activation to
+     *       invoke on the result.
+     * * 11: An {@link OperandType::BOOL} scalar, set to true to specify
+     *       NCHW data layout for input0 and output0. Set to false for NHWC.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+     *      specifying the input, where depth_in = num_groups * depth_group.
+     * * 1: A 4-D tensor, of shape
+     *      [depth_out, filter_height, filter_width, depth_group], specifying
+     *      the filter, where depth_out must be divisible by num_groups.  For
+     *      tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+     *      the channel dimension (SymmPerChannelQuantParams::channelDim)
+     *      must be set to 0.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32} or
+     *      {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+     *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+     *      of 0 and bias_scale == input_scale * filter_scale. For filter tensor
+     *      of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+     *      0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+     *      bias_scale[i] = input_scale * filter_scale[i].
+     * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
+     *      padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the number of
+     *      groups.
+     * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     * * 8: An {@link OperandType::BOOL} scalar, set to true to specify
+     *      NCHW data layout for input0 and output0. Set to false for NHWC.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, out_height, out_width, depth_out].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+     */
+    GROUPED_CONV_2D = @1.2::OperationType:GROUPED_CONV_2D,
+
+    /**
+     * Localize the maximum keypoints from heatmaps.
+     *
+     * This operation approximates the accurate maximum keypoint scores and
+     * indices after bicubic upscaling by using Taylor expansion up to the
+     * quadratic term.
+     *
+     * The bounding box is represented by its upper-left corner coordinate
+     * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
+     * A valid bounding box should satisfy x1 <= x2 and y1 <= y2.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     *
+     * Inputs:
+     * * 0: A 4-D Tensor of shape
+     *      [num_boxes, heatmap_size, heatmap_size, num_keypoints],
+     *      specifying the heatmaps, the height and width of heatmaps should
+     *      be the same, and must be greater than or equal to 2.
+     * * 1: A 2-D Tensor of shape [num_boxes, 4], specifying the bounding boxes,
+     *      each with format [x1, y1, x2, y2]. For input0 of type
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, this tensor should
+     *      be of {@link OperandType::TENSOR_QUANT16_ASYMM}, with zeroPoint
+     *      of 0 and scale of 0.125.
+     * * 2: An {@link OperandType::BOOL} scalar, set to true to specify
+     *      NCHW data layout for input0. Set to false for NHWC.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0, with shape
+     *      [num_boxes, num_keypoints], specifying score of the keypoints.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from input0 scale and zeroPoint.
+     * * 1: A tensor of the same {@link OperandType} as input1, with shape
+     *      [num_boxes, num_keypoints, 2], specifying the location of
+     *      the keypoints, the second dimension is organized as
+     *      [keypoint_x, keypoint_y].
+     *      For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+     *      scale must be 0.125 and the zero point must be 0.
+     */
+    HEATMAP_MAX_KEYPOINT = @1.2::OperationType:HEATMAP_MAX_KEYPOINT,
+
+    /**
+     * Applies instance normalization to the input tensor.
+     *
+     * The values in the output tensor are computed as:
+     *
+     *     output[b, h, w, c] =
+     *         (input[b, h, w, c] - mean[b, c]) * gamma /
+     *         sqrt(var[b, c] + epsilon) + beta
+     *
+     * Where the mean and variance are computed across the spatial dimensions:
+     *
+     *     mean[b, c] =
+     *         sum_{h, w}(input[b, h, w, c]) / sum(1)
+     *
+     *     var[b, c] =
+     *         sum_{h, w}(pow(input[b, h, w, c] - mean[b, c], 2)) / sum(1)
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     *
+     * Inputs:
+     * * 0: An n-D tensor, specifying the tensor to be normalized.
+     * * 1: A scalar, specifying gamma, the scale applied to the normalized
+     *      tensor. The scalar must be of {@link OperandType::FLOAT16} if
+     *      input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} if input0 is of
+     *      {@link OperandType::TENSOR_FLOAT32}.
+     * * 2: A scalar, specifying beta, the offset applied to the normalized
+     *      tensor. The scalar must be of {@link OperandType::FLOAT16} if
+     *      input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} if input0 is of
+     *      {@link OperandType::TENSOR_FLOAT32}.
+     * * 3: A scalar, specifying epsilon, the small value added to variance to
+     *      avoid dividing by zero. The scalar must be of {@link OperandType::FLOAT16} if
+     *      input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} if input0 is of
+     *      {@link OperandType::TENSOR_FLOAT32}.
+     * * 4: An {@link OperandType::BOOL} scalar, set to true to specify
+     *      NCHW data layout for input0 and output0. Set to false for NHWC.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} and same shape as input0.
+     */
+    INSTANCE_NORMALIZATION = @1.2::OperationType:INSTANCE_NORMALIZATION,
+
+    /**
+     * For input tensors x and y, computes x < y elementwise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_BOOL8}
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * This operation supports broadcasting.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+     *      with input0.
+     *
+     * Outputs:
+     * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+     */
+    LESS = @1.2::OperationType:LESS,
+
+    /**
+     * For input tensors x and y, computes x <= y elementwise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_BOOL8}
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * This operation supports broadcasting.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+     *      with input0.
+     *
+     * Outputs:
+     * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+     */
+    LESS_EQUAL = @1.2::OperationType:LESS_EQUAL,
+
+    /**
+     * Computes natural logarithm of x element-wise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: from 1.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     */
+    LOG = @1.2::OperationType:LOG,
+
+    /**
+     * Returns the truth value of x AND y element-wise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_BOOL8}
+     *
+     * Supported tensor rank: from 1
+     *
+     * This operation supports broadcasting.
+     *
+     * Inputs:
+     * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+     * * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions
+     *      compatible with input0.
+     *
+     * Outputs:
+     * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+     */
+    LOGICAL_AND = @1.2::OperationType:LOGICAL_AND,
+
+    /**
+     * Computes the truth value of NOT x element-wise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_BOOL8}
+     *
+     * Supported tensor rank: from 1.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     */
+    LOGICAL_NOT = @1.2::OperationType:LOGICAL_NOT,
+
+    /**
+     * Returns the truth value of x OR y element-wise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_BOOL8}
+     *
+     * Supported tensor rank: from 1
+     *
+     * This operation supports broadcasting.
+     *
+     * Inputs:
+     * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+     * * 1: A tensor of {@link OperandType::TENSOR_BOOL8} and dimensions
+     *      compatible with input0.
+     *
+     * Outputs:
+     * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+     */
+    LOGICAL_OR = @1.2::OperationType:LOGICAL_OR,
+
+    /**
+     * Computes the log softmax activations given logits.
+     *
+     * The output is calculated using this formula:
+     *
+     *     output = logits * beta - log(reduce_sum(exp(logits * beta), axis))
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: from 1.
+     *
+     * Inputs:
+     * * 0: A tensor specifying the input logits.
+     * * 1: A scalar, specifying the positive scaling factor for the exponent,
+     *      beta.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta
+     *      value must be of {@link OperandType::FLOAT16}.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta
+     *      value must be of {@link OperandType::FLOAT32}.
+     * * 2: An {@link OperandType::INT32} scalar specifying the axis to
+     *      reduce across. Negative index is used to specify axis from the
+     *      end (e.g. -1 for the last axis). Must be in the range [-n, n).
+     *
+     * Outputs:
+     * * 0: The output tensor of the same {@link OperandType} and shape as
+     *      input0.
+     */
+    LOG_SOFTMAX = @1.2::OperationType:LOG_SOFTMAX,
+
+    /**
+     * Returns the element-wise maximum of two tensors.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     * * 1: A tensor of the same {@link OperandType} and compatible dimensions
+     *      with input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scales and zeroPoint can be different from input0 scale and zeroPoint.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+     */
+    MAXIMUM = @1.2::OperationType:MAXIMUM,
+
+    /**
+     * Returns the element-wise minimum of two tensors.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     * * 1: A tensor of the same {@link OperandType} and compatible dimensions
+     *      with input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scales and zeroPoint can be different from input0 scale and zeroPoint.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+     */
+    MINIMUM = @1.2::OperationType:MINIMUM,
+
+    /**
+     * Computes numerical negative value element-wise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     *
+     * Supported tensor rank: from 1.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     */
+    NEG = @1.2::OperationType:NEG,
+
+    /**
+     * For input tensors x and y, computes x != y elementwise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_BOOL8}
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * This operation supports broadcasting.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     * * 1: A tensor of the same {@link OperandType} and dimensions compatible
+     *      with input0.
+     *
+     * Outputs:
+     * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
+     */
+    NOT_EQUAL = @1.2::OperationType:NOT_EQUAL,
+
+    /**
+     * Pads a tensor with the given constant value according to the specified
+     * paddings.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor, specifying the tensor to be padded.
+     * * 1: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+     *      for each spatial dimension of the input tensor. The shape of the
+     *      tensor must be {rank(input0), 2}.
+     *      padding[i, 0] specifies the number of elements to be padded in the
+     *      front of dimension i.
+     *      padding[i, 1] specifies the number of elements to be padded after
+     *      the end of dimension i.
+     * * 2: An scalar specifying the value to use for padding input0.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT16}, the
+     *      pad value must be of {@link OperandType::FLOAT16}.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
+     *      pad value must be of {@link OperandType::FLOAT32}.
+     *      For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the pad value must be of {@link OperandType::INT32}. The
+     *      scale and zeroPoint are assumed to be the same as in input0.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0. The
+     *      output tensor has the same rank as input0, and each
+     *      dimension of the output tensor has the same size as the
+     *      corresponding dimension of the input tensor plus the size
+     *      of the padding:
+     *          output0.dimension[i] =
+     *              padding[i, 0] + input0.dimension[i] + padding[i, 1]
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    PAD_V2 = @1.2::OperationType:PAD_V2,
+
+    /**
+     * Computes the power of one value to another.
+     *
+     * Given a tensor base and a tensor exponent, this operation computes
+     * base^exponent elementwise.
+     *
+     * This operations supports broadcasting. The size of the output is the
+     * maximum size along each dimension of the input operands. It starts with
+     * the trailing dimensions, and works its way forward.
+     *
+     * For example:
+     *     base.dimension     =    {4, 1, 2}
+     *     exponent.dimension = {5, 4, 3, 1}
+     *     output.dimension   = {5, 4, 3, 2}
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: A tensor specifying the base.
+     * * 1: A tensor specifying the exponent.
+     *
+     * Outputs:
+     * * 0: An output tensor.
+     */
+    POW = @1.2::OperationType:POW,
+
+    /**
+     * Parametric Rectified Linear Unit.
+     *
+     * It follows: f(x) = alpha * x for x < 0, f(x) = x for x >= 0, where alpha
+     * is a learned array with the same {@link OperandType} and compatible
+     * dimensions as input x.
+     *
+     * Two dimensions are compatible when:
+     *     1. they are equal, or
+     *     2. one of them is 1
+     *
+     * The size of the output is the maximum size along each dimension of the
+     * input operands. It starts with the trailing dimensions, and works its way
+     * forward.
+     *
+     * Example:
+     *     input.dimension  =    {4, 1, 2}
+     *     alpha.dimension  = {5, 4, 3, 1}
+     *     output.dimension = {5, 4, 3, 2}
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: A tensor, specifying the input.
+     * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+     *      as input0, specifying the alpha.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be diffent from the input0 scale and zeroPoint.
+     */
+    PRELU = @1.2::OperationType:PRELU,
+
+    /**
+     * Quantizes the input tensor.
+     *
+     * The formula is:
+     *
+     *     output = max(0, min(255, round(input / scale) + zeroPoint)
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: A tensor, may be zero-sized.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0, but with
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM}.
+     */
+    QUANTIZE = @1.2::OperationType:QUANTIZE,
+
+    /**
+     * A version of quantized LSTM, using 16 bit quantization for internal
+     * state.
+     *
+     * There is no projection layer, so cell state size is equal to the output
+     * size.
+     *
+     * Inputs:
+     * * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *      and shape [numBatches, inputSize] specifying the input to the LSTM
+     *      cell. Tensor is quantized with a fixed quantization range of
+     *      [-1, 127/128] (scale = 1/128, zeroPoint = 128).
+     * * 1: The input-to-input weights.
+     *      A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *      and shape [outputSize, inputSize] specifying input-to-input part of
+     *      weights for fully-connected layer inside the LSTM cell.
+     *      Quantization zero point and scale must be the same across all the
+     *      weights.
+     * * 2: The input-to-forget weights.
+     *      A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *      and shape [outputSize, inputSize] specifying input-to-forget part of
+     *      weights for fully-connected layer inside the LSTM cell.
+     *      Quantization zero point and scale must be the same across all the
+     *      weights.
+     * * 3: The input-to-cell weights.
+     *      A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *      and shape [outputSize, inputSize] specifying input-to-cell part of
+     *      weights for fully-connected layer inside the LSTM cell.
+     *      Quantization zero point and scale must be the same across all the
+     *      weights.
+     * * 4: The input-to-output weights.
+     *      A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *      and shape [outputSize, inputSize] specifying input-to-output part of
+     *      weights for fully-connected layer inside the LSTM cell.
+     *      Quantization zero point and scale must be the same across all the
+     *      weights.
+     * * 5: The recurrent-to-input weights.
+     *      A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *      and shape [outputSize, outputSize] specifying recurrent-to-input part
+     *      of weights for fully-connected layer inside the LSTM cell.
+     *      Quantization zero point and scale must be the same across all the
+     *      weights.
+     * * 6: The recurrent-to-forget weights.
+     *      A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *      and shape [outputSize, outputSize] specifying recurrent-to-forget
+     *      part of weights for fully-connected layer inside the LSTM cell.
+     *      Quantization zero point and scale must be the same across all the
+     *      weights.
+     * * 7: The recurrent-to-cell weights.
+     *      A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *      and shape [outputSize, outputSize] specifying recurrent-to-cell part
+     *      of weights for fully-connected layer inside the LSTM cell.
+     *      Quantization zero point and scale must be the same across all the
+     *      weights.
+     * * 8: The recurrent-to-output weights.
+     *      A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *      and shape [outputSize, outputSize] specifying recurrent-to-output
+     *      part of weights for fully-connected layer inside the LSTM cell.
+     *      Quantization zero point and scale must be the same across all the
+     *      weights.
+     * * 9: The input gate bias.
+     *      A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+     *      [outputSize] specifying the bias for the fully-connected layer
+     *      inside the LSTM cell. Bias is quantized with scale being a product
+     *      of input and weights scales and zeroPoint equal to 0.
+     * * 10:The forget gate bias.
+     *      A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+     *      [outputSize] specifying the bias for the fully-connected layer
+     *      inside the LSTM cell. Bias is quantized with scale being a product
+     *      of input and weights scales and zeroPoint equal to 0.
+     * * 11:The cell bias.
+     *      A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+     *      [outputSize] specifying the bias for the fully-connected layer
+     *      inside the LSTM cell. Bias is quantized with scale being a product
+     *      of input and weights scales and zeroPoint equal to 0.
+     * * 12:The output gate bias.
+     *      A 1-D tensor of type {@link OperandType::TENSOR_INT32} and shape
+     *      [outputSize] specifying the bias for the fully-connected layer
+     *      inside the LSTM cell. Bias is quantized with scale being a product
+     *      of input and weights scales and zeroPoint equal to 0.
+     * * 13: A 2-D tensor of type {@link OperandType::TENSOR_QUANT16_SYMM}
+     *       and shape [numBatches, outputSize] specifying the cell state from the
+     *       previous time step of the LSTM cell. It is quantized using a
+     *       quantization range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 /
+     *       32768, zeroPoint = 0).
+     * * 14: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *       and shape [numBathes, outputSize] specifying the output of the LSTM
+     *       cell from previous time-step. Tensor is quantized with a fixed
+     *       quantization range of [-1, 127/128] (scale = 1/128, zeroPoint =
+     *       128).
+     *
+     *
+     * Outputs:
+     * * 0: A 2-D tensor of type {@link OperandType::TENSOR_QUANT16_SYMM}
+     *      and shape [numBatches, outputSize] which contains a cell state from
+     *      the current time step. Tensor is quantized using a quantization
+     *      range of [-2^4, 2^4 * 32767/32768] (scale = 2^4 / 32768, zeroPoint =
+     *      0).
+     * * 1: A 2-D tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *      and shape [numBathes, outputSize] which contains the output value.
+     *      Tensor is quantized with a fixed quantization range of [-1, 127/128]
+     *      (scale = 1/128, zeroPoint = 128).
+     */
+    QUANTIZED_16BIT_LSTM = @1.2::OperationType:QUANTIZED_16BIT_LSTM,
+
+    /**
+     * Draws samples from a multinomial distribution.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Inputs:
+     * * 0: A 2-D tensor with shape [batches, classes], specifying the
+     *      unnormalized log-probabilities for all classes.
+     * * 1: A scalar {@link OperandType::INT32}, specifying the number of
+     *      independent samples to draw for each row slice.
+     * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [2],
+     *      specifying seeds used to initialize the random distribution.
+     * Outputs:
+     * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape
+     *      [batches, samples], containing the drawn samples.
+     */
+    RANDOM_MULTINOMIAL = @1.2::OperationType:RANDOM_MULTINOMIAL,
+
+    /**
+     * Reduces a tensor by computing the "logical and" of elements along given
+     * dimensions.
+     *
+     * If keep_dims is true, the reduced dimensions are
+     * retained with length 1. Otherwise, the rank of the tensor is reduced by
+     * 1 for each entry in dimensions.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_BOOL8}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor.
+     * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+     *      to reduce. Dimension values must be in the range [-n, n).
+     * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+     *      retains reduced dimensions with length 1.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     */
+    REDUCE_ALL = @1.2::OperationType:REDUCE_ALL,
+
+    /**
+     * Reduces a tensor by computing the "logical or" of elements along given
+     * dimensions.
+     *
+     * If keep_dims is true, the reduced dimensions are
+     * retained with length 1. Otherwise, the rank of the tensor is reduced by
+     * 1 for each entry in dimensions.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_BOOL8}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor.
+     * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+     *      to reduce. Dimension values must be in the range [-n, n).
+     * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+     *      retains reduced dimensions with length 1.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     */
+    REDUCE_ANY = @1.2::OperationType:REDUCE_ANY,
+
+    /**
+     * Reduces a tensor by computing the maximum of elements along given
+     * dimensions.
+     *
+     * If keep_dims is true, the reduced dimensions are
+     * retained with length 1. Otherwise, the rank of the tensor is reduced by
+     * 1 for each entry in dimensions.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor.
+     * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+     *      to reduce. Dimension values must be in the range [-n, n).
+     * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+     *      retains reduced dimensions with length 1.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    REDUCE_MAX = @1.2::OperationType:REDUCE_MAX,
+
+    /**
+     * Reduces a tensor by computing the minimum of elements along given
+     * dimensions.
+     *
+     * If keep_dims is true, the reduced dimensions are
+     * retained with length 1. Otherwise, the rank of the tensor is reduced by
+     * 1 for each entry in dimensions.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor.
+     * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+     *      to reduce. Dimension values must be in the range [-n, n).
+     * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+     *      retains reduced dimensions with length 1.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    REDUCE_MIN = @1.2::OperationType:REDUCE_MIN,
+
+    /**
+     * Reduces a tensor by multiplying elements along given dimensions.
+     *
+     * If keep_dims is true, the reduced dimensions are
+     * retained with length 1. Otherwise, the rank of the tensor is reduced by
+     * 1 for each entry in dimensions.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor.
+     * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+     *      to reduce. Dimension values must be in the range [-n, n).
+     * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+     *      retains reduced dimensions with length 1.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     */
+    REDUCE_PROD = @1.2::OperationType:REDUCE_PROD,
+
+    /**
+     * Reduces a tensor by summing elements along given dimensions.
+     *
+     * If keep_dims is true, the reduced dimensions are
+     * retained with length 1. Otherwise, the rank of the tensor is reduced by
+     * 1 for each entry in dimensions.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * * 0: An n-D tensor.
+     * * 1: A 1-D tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+     *      to reduce. Dimension values must be in the range [-n, n).
+     * * 2: An {@link OperandType::BOOL} scalar, keep_dims. If true,
+     *      retains reduced dimensions with length 1.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0.
+     */
+    REDUCE_SUM = @1.2::OperationType:REDUCE_SUM,
+
+    /**
+     * Select and scale the feature map of each region of interest to a unified
+     * output size by average pooling sampling points from bilinear interpolation.
+     *
+     * The region of interest is represented by its upper-left corner coordinate
+     * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
+     * A spatial scaling factor is applied to map into feature map coordinate.
+     * A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
+     *
+     * No rounding is applied in this operation. The sampling points are unified
+     * distributed in the pooling bin and their values are calculated by bilinear
+     * interpolation.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     *
+     * Inputs:
+     * * 0: A 4-D tensor, specifying the feature map.
+     * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
+     *      the regions of interest, each line with format [x1, y1, x2, y2].
+     *      For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
+     *      with zeroPoint of 0 and scale of 0.125. Zero num_rois is
+     *      supported for this tensor.
+     * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+     *      [num_rois], specifying the batch index of each box. Boxes with
+     *      the same batch index are grouped together. Zero num_rois is
+     *      supported for this tensor.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the output
+     *      height of the output tensor.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the output
+     *      width of the output tensor.
+     * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+     *      from the height of original image to the height of feature map.
+     * * 6: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+     *      from the width of original image to the width of feature map.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the number of
+     *      sampling points in height dimension used to compute the output.
+     *      Set to 0 for adaptive value of ceil(roi_height/out_height).
+     * * 8: An {@link OperandType::INT32} scalar, specifying the number of
+     *      sampling points in width dimension used to compute the output.
+     *      Set to 0 for adaptive value of ceil(roi_width/out_width).
+     * * 9: An {@link OperandType::BOOL} scalar, set to true to specify
+     *      NCHW data layout for input0 and output0. Set to false for NHWC.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0. The output
+     *      shape is [num_rois, out_height, out_width, depth].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from the input0 scale and zeroPoint.
+     */
+    ROI_ALIGN = @1.2::OperationType:ROI_ALIGN,
+
+    /**
+     * Select and scale the feature map of each region of interest to a unified
+     * output size by max-pooling.
+     *
+     * The region of interest is represented by its upper-left corner coordinate
+     * (x1,y1) and lower-right corner coordinate (x2,y2) in the original image.
+     * A spatial scaling factor is applied to map into feature map coordinate.
+     * A valid region of interest should satisfy x1 <= x2 and y1 <= y2.
+     *
+     * Rounding is applied in this operation to ensure integer boundary for
+     * regions of interest and pooling bins.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     *
+     * Inputs:
+     * * 0: A 4-D tensor, specifying the feature map.
+     * * 1: A 2-D Tensor of shape [num_rois, 4], specifying the locations of
+     *      the regions of interest, each line with format [x1, y1, x2, y2].
+     *      For input0 of type {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      this tensor should be of {@link OperandType::TENSOR_QUANT16_ASYMM},
+     *      with zeroPoint of 0 and scale of 0.125.
+     * * 2: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
+     *      [num_rois], specifying the batch index of each box. Boxes with
+     *      the same batch index are grouped together.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the output
+     *      height of the output tensor.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the output
+     *      width of the output tensor.
+     * * 5: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+     *      from the height of original image to the height of feature map.
+     * * 6: An {@link OperandType::FLOAT32} scalar, specifying the ratio
+     *      from the width of original image to the width of feature map.
+     * * 7: An {@link OperandType::BOOL} scalar, set to true to specify
+     *      NCHW data layout for input0 and output0. Set to false for NHWC.
+     *
+     * Outputs:
+     * * 0: A tensor of the same {@link OperandType} as input0. The output
+     *      shape is [num_rois, out_height, out_width, depth].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    ROI_POOLING = @1.2::OperationType:ROI_POOLING,
+
+    /**
+     * Computes reciprocal of square root of x element-wise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: from 1.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     */
+    RSQRT = @1.2::OperationType:RSQRT,
+
+    /**
+     * Using a tensor of booleans c and input tensors x and y select values
+     * elementwise from both input tensors:
+     *
+     * O[i] = C[i] ? x[i] : y[i].
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: A tensor of type {@link OperandType::TENSOR_BOOL8} acting as a
+     *      mask that chooses, based on the value at each element, whether the
+     *      corresponding element in the output should be taken from input1 (if
+     *      true) or input2 (if false).
+     * * 1: An input tensor of the same shape as input0.
+     * * 2: An input tensor of the same shape and type as input1.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scales and zeroPoint can be different from input1 scale and zeroPoint.
+     *
+     * Outputs:
+     * * 0: A tensor of the same type and shape as input1 and input2.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+     *
+     */
+    SELECT = @1.2::OperationType:SELECT,
+
+    /**
+     * Computes sin of x element-wise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: from 1.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     */
+    SIN = @1.2::OperationType:SIN,
+
+    /**
+     * Extracts a slice of specified size from the input tensor starting at a
+     * specified location.
+     *
+     * The starting location is specified as a 1-D tensor containing offsets
+     * for each dimension. The size is specified as a 1-D tensor containing
+     * either size of a slice along corresponding dimension or -1. In the latter
+     * case, all the remaining elements in dimension are included in the slice.
+     *
+     * A sum of begin offset and a size of a slice must not exceed size of a
+     * corresponding dimension.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: An n-D tensor to take slice from, may be zero-sized.
+     * * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying
+     *      the beginning indices of the slice in each dimension.
+     * * 2: A 1-D tensor of type {@link OperandType::TENSOR_INT32} specifying
+     *      the size of the slice in each dimension.
+     *
+     * Outputs:
+     * * 0: An n-D tensor of the same type as the input containing the slice.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      its scale and zeroPoint has to be same as the input0 scale and zeroPoint.
+     */
+    SLICE = @1.2::OperationType:SLICE,
+
+    /**
+     * Splits a tensor along a given axis into num_splits subtensors.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: An n-D tensor to split.
+     * * 1: An {@link OperandType::INT32} scalar specifying the axis along
+     *      which to split.
+     * * 2: An {@link OperandType::INT32} scalar indicating the number of
+     *      splits along given axis. Must evenly divide axis size.
+     *
+     * Outputs:
+     * * 0 ~ (num_splits - 1): Resulting subtensors.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    SPLIT = @1.2::OperationType:SPLIT,
+
+    /**
+     * Computes square root of x element-wise.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: from 1.
+     *
+     * Inputs:
+     * * 0: A tensor.
+     *
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     */
+    SQRT = @1.2::OperationType:SQRT,
+
+    /**
+     * Constructs a tensor by tiling a given tensor.
+     *
+     * This operation creates a new tensor by replicating `input` `multiples`
+     * times. The output tensor's i-th dimension has `input.dims(i) * multiples[i]`
+     * elements, and the values of `input` are replicated `multiples[i]` times
+     * along the i-th dimension.
+     * For example, tiling `[a b c d]` by `[2]` produces `[a b c d a b c d]`.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: input, an n-D tensor specifying the input.
+     * * 1: multiples, a 1-D tensor of {@link OperandType::TENSOR_INT32}.
+     *      The length of multiples must be n.
+     *
+     * Outputs:
+     * * 0: A tiled tensor of the same {@link OperandType} and rank as `input`.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    TILE = @1.2::OperationType:TILE,
+
+    /**
+     * Finds values and indices of the k largest entries for the last dimension.
+     *
+     * Resulting values in each dimensions are sorted in descending order. If
+     * two values are equal, the one with larger index appears first.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: from 1
+     *
+     * Inputs:
+     * * 0: input, an n-D tensor specifying the input.
+     * * 1: k, an {@link OperandType::INT32} scalar, specifying the number of
+     *      top elements to look for along the last dimension.
+     *
+     * Outputs:
+     * * 0: An n-D tensor of the same type as the input, containing the k
+     *      largest elements along each last dimensional slice.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     * * 1: An n-D tensor of type {@link OperandType::TENSOR_INT32}
+     *      containing the indices of values within the last dimension of input.
+     */
+    TOPK_V2 = @1.2::OperationType:TOPK_V2,
+
+    /**
+     * Performs the transpose of 2-D convolution operation.
+     *
+     * This operation is sometimes called "deconvolution" after Deconvolutional
+     * Networks, but is actually the transpose (gradient) of
+     * {@link OperandType::CONV_2D} rather than an actual deconvolution.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and
+     * padding.
+     *
+     * Supported tensor {@link OperandType} configurations:
+     * * 16 bit floating point:
+     * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
+     *
+     * * 32 bit floating point:
+     * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
+     *
+     * * Quantized:
+     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+     * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+     * * * input.scale * filter.scale).
+     *
+     * * Quantized with symmetric per channel quantization for the filter:
+     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, and output.
+     * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
+     * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
+     * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     *
+     * Both explicit padding and implicit padding are supported.
+     *
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+     *      specifying the input.
+     * * 1: A 4-D tensor, of shape
+     *      [depth_out, filter_height, filter_width, depth_in], specifying the
+     *      filter. For tensor of type
+     *      {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
+     *      dimension (SymmPerChannelQuantParams::channelDim) must be set to 0.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32} or
+     *      {@link OperandType::TENSOR_FLOAT16}, the bias should be of the
+     *      same type. For input tensor of type
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
+     *      of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale. For filter tensor of
+     *      {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
+     *      must be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+     *      0 and bias_scale of 0. The actual scale of each value 'i' is equal
+     *      to bias_scale[i] = input_scale * filter_scale[i].
+     * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the left, in the ‘width’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the right, in the ‘width’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the top, in the ‘height’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the bottom, in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     * * 10: An {@link OperandType::BOOL} scalar, set to true to specify
+     *       NCHW data layout for input0 and output0. Set to false for NHWC.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
+     *      specifying the input.
+     * * 1: A 4-D tensor, of shape
+     *      [depth_out, filter_height, filter_width, depth_in], specifying the
+     *      filter. For tensor of type
+     *      {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
+     *      dimension (SymmPerChannelQuantParams::channelDim) must be set to 0.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32} or
+     *      {@link OperandType::TENSOR_FLOAT16}, the bias should be of the
+     *      same type. For input tensor of type
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
+     *      of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale. For filter tensor of
+     *      {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
+     *      must be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+     *      0 and bias_scale of 0. The actual scale of each value 'i' is equal
+     *      to bias_scale[i] = input_scale * filter_scale[i].
+     * * 3: An {@link OperandType::TENSOR_INT32} tensor, specifying the output
+     *      tensor shape.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the implicit
+     *      padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
+     * * 8: An {@link OperandType::BOOL} scalar, set to true to specify
+     *      NCHW data layout for input0 and output0. Set to false for NHWC.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, out_height, out_width, depth_out].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
+     */
+    TRANSPOSE_CONV_2D = @1.2::OperationType:TRANSPOSE_CONV_2D,
+
+    /**
+     * A recurrent neural network specified by an LSTM cell.
+     *
+     * Performs (fully) dynamic unrolling of input.
+     *
+     * This Op unrolls the input along the time dimension, and implements the
+     * following operation for each element in the sequence
+     * s = 1...sequence_length:
+     *   outputs[s] = projection(state = activation(LSTMOp(inputs[s])))
+     *
+     * Where LSTMOp is the LSTM op as in {@link OperandType::LSTM},
+     * the "projection" is an optional projection layer from state and output
+     * and the “activation” is the function passed as the
+     * “fused_activation_function” argument (if not “NONE”).
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: 3, either time-major or batch-major.
+     *
+     * All input and output tensors must be of the same type.
+     *
+     * Inputs:
+     * * 0: The input (\f$x_t\f$).
+     *      A 3-D tensor of shape:
+     *        If time-major: [max_time, batch_size, input_size]
+     *        If batch-major: [batch_size, max_time, input_size]
+     *      where “max_time” is the number of timesteps (sequence length),
+     *      “batch_size” corresponds to the batching dimension, and
+     *      “input_size” is the size of the input.
+     * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
+     *      A 2-D tensor of shape [num_units, input_size], where “num_units”
+     *      corresponds to the number of cell units.
+     * * 2: The input-to-forget weights (\f$W_{xf}\f$).
+     *      A 2-D tensor of shape [num_units, input_size].
+     * * 3: The input-to-cell weights (\f$W_{xc}\f$).
+     *      A 2-D tensor of shape [num_units, input_size].
+     * * 4: The input-to-output weights (\f$W_{xo}\f$).
+     *      A 2-D tensor of shape [num_units, input_size].
+     * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
+     *      A 2-D tensor of shape [num_units, output_size], where “output_size”
+     *      corresponds to either the number of cell units (i.e., “num_units”),
+     *      or the second dimension of the “projection_weights”, if defined.
+     * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
+     *      A 2-D tensor of shape [num_units, output_size].
+     * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
+     *      A 2-D tensor of shape [num_units, output_size].
+     * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
+     *      A 2-D tensor of shape [num_units, output_size].
+     * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
+     *      A 1-D tensor of shape [num_units].
+     * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
+     *      A 1-D tensor of shape [num_units].
+     * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
+     *      A 1-D tensor of shape [num_units].
+     * * 12:The input gate bias (\f$b_i\f$). Optional.
+     *      A 1-D tensor of shape [num_units].
+     * * 13:The forget gate bias (\f$b_f\f$).
+     *      A 1-D tensor of shape [num_units].
+     * * 14:The cell bias (\f$b_c\f$).
+     *      A 1-D tensor of shape [num_units].
+     * * 15:The output gate bias (\f$b_o\f$).
+     *      A 1-D tensor of shape [num_units].
+     * * 16:The projection weights (\f$W_{proj}\f$). Optional.
+     *      A 2-D tensor of shape [output_size, num_units].
+     * * 17:The projection bias (\f$b_{proj}\f$). Optional.
+     *      A 1-D tensor of shape [output_size].
+     * * 18:The output state (in) (\f$h_{t-1}\f$).
+     *      A 2-D tensor of shape [batch_size, output_size].
+     * * 19:The cell state (in) (\f$C_{t-1}\f$).
+     *      A 2-D tensor of shape [batch_size, num_units].
+     * * 20:The activation function (\f$g\f$).
+     *      A value indicating the activation function:
+     *      <ul>
+     *      <li>0: None;
+     *      <li>1: Relu;
+     *      <li>3: Relu6;
+     *      <li>4: Tanh;
+     *      <li>6: Sigmoid.
+     *      </ul>
+     * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such
+     *      that values are bound within [-cell_clip, cell_clip]. If set to 0.0
+     *      then clipping is disabled.
+     * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the
+     *      projection layer, such that values are bound within
+     *      [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
+     * * 23:Time-major if true, batch-major if false.
+     * * 24:The input layer normalization weights. Optional.
+     *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+     *      to activation at input gate.
+     * * 25:The forget layer normalization weights. Optional.
+     *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+     *      to activation at forget gate.
+     * * 26:The cell layer normalization weights. Optional.
+     *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+     *      to activation at cell gate.
+     * * 27:The output layer normalization weights. Optional.
+     *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
+     *      to activation at output gate.
+     *
+     * Outputs:
+     * * 0: The output (\f$o_t\f$).
+     *      A 3-D tensor of shape:
+     *        If time-major: [max_time, batch_size, output_size]
+     *        If batch-major: [batch_size, max_time, output_size]
+     */
+    UNIDIRECTIONAL_SEQUENCE_LSTM = @1.2::OperationType:UNIDIRECTIONAL_SEQUENCE_LSTM,
+
+    /**
+     * A recurrent neural network layer that applies a basic RNN cell to a
+     * sequence of inputs.
+     *
+     * This layer unrolls the input along the sequence dimension, and implements
+     * the following operation
+     * for each element in the sequence s = 1...sequence_length:
+     *   outputs[s] = state = activation(inputs[s] * input_weights’ + state *
+     *   recurrent_weights’ + bias)
+     *
+     * Where:
+     * * “input_weights” is a weight matrix that multiplies the inputs;
+     * * “recurrent_weights” is a weight matrix that multiplies the current
+     *    “state” which itself is the output from the previous time step
+     *    computation;
+     * * “bias” is a bias vector (added to each output vector in the batch);
+     * * “activation” is the function passed as the “fused_activation_function”
+     *   argument (if not “NONE”).
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * The input tensors must all be the same type.
+     *
+     * Inputs:
+     * * 0: input.
+     *      A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+     *      it is set to 1, then the input has a shape [maxTime, batchSize,
+     *      inputSize], otherwise the input has a shape [batchSize, maxTime,
+     *      inputSize].
+     * * 1: weights.
+     *      A 2-D tensor of shape [numUnits, inputSize].
+     * * 2: recurrent_weights.
+     *      A 2-D tensor of shape [numUnits, numUnits].
+     * * 3: bias.
+     *      A 1-D tensor of shape [numUnits].
+     * * 4: hidden state
+     *      A 2-D tensor of shape [batchSize, numUnits]. Specifies a hidden
+     *      state input for the first time step of the computation.
+     * * 5: fusedActivationFunction.
+     *      A {@link FusedActivationFunc} value indicating the activation function. If
+     *      “NONE” is specified then it results in a linear activation.
+     * * 6: timeMajor
+     *      An {@link OperandType::INT32} scalar specifying the shape format
+     *      of input and output tensors. Must be set to either 0 or 1.
+     * Outputs:
+     * * 0: output.
+     *      A 3-D tensor. The shape is defined by the input 6 (timeMajor). If
+     *      it is set to 1, then the output has a shape [maxTime, batchSize,
+     *      numUnits], otherwise the output has a shape [batchSize, maxTime,
+     *      numUnits].
+     */
+    UNIDIRECTIONAL_SEQUENCE_RNN = @1.2::OperationType:UNIDIRECTIONAL_SEQUENCE_RNN,
+
+    /**
+     * Resizes images to given size using the nearest neighbor interpretation.
+     *
+     * Resized images must be distorted if their output aspect ratio is not the
+     * same as input aspect ratio. The corner pixels of output may not be the
+     * same as corner pixels of input.
+     *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16}
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
+     * With the default data layout NHWC, the data is stored in the order of:
+     * [batch, height, width, channels]. Alternatively, the data layout could
+     * be NCHW, the data storage order of: [batch, channels, height, width].
+     *
+     * Both resizing by shape and resizing by scale are supported.
+     *
+     * Inputs (resizing by shape):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input. Zero batches is supported for this tensor.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the output
+     *      width of the output tensor.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the output
+     *      height of the output tensor.
+     * * 3: An {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *
+     * Inputs (resizing by scale):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input. Zero batches is supported for this tensor.
+     * * 1: A scalar, specifying width_scale, the scaling factor of the width
+     *      dimension from the input tensor to the output tensor. The output
+     *      width is calculated as new_width = floor(width * width_scale).
+     *      The scalar must be of {@link OperandType::FLOAT16} if input0 is
+     *      of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} otherwise.
+     * * 2: A scalar, specifying height_scale, the scaling factor of the height
+     *      dimension from the input tensor to the output tensor. The output
+     *      height is calculated as new_height = floor(height * height_scale).
+     *      The scalar must be of {@link OperandType::FLOAT16} if input0 is
+     *      of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} otherwise.
+     * * 3: An {@link OperandType::BOOL} scalar, default to false.
+     *      Set to true to specify NCHW data layout for input0 and output0.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, new_height, new_width, depth].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
+     */
+    RESIZE_NEAREST_NEIGHBOR = @1.2::OperationType:RESIZE_NEAREST_NEIGHBOR,
+
+    /**
+     * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
+     * OEM operation and data types.
+     *
+     * This operation is OEM specific. It should only be used for OEM
+     * applications.
+     */
+    OEM_OPERATION = @1.2::OperationType:OEM_OPERATION,
+    /* ADDING A NEW FUNDAMENTAL OPERATION REQUIRES UPDATING THE VALUE OF
+     * OperationTypeRange::FUNDAMENTAL_MAX.
+     */
+    /* ADDING A NEW OEM OPERATION REQUIRES UPDATING THE VALUE OF
+     * OperationTypeRange::OEM_MAX.
+     */
+};
+
+/**
+ * The range of values in the OperationType enum.
+ */
+enum OperationTypeRange : uint32_t {
+    BASE_MIN        = 0,
+    FUNDAMENTAL_MIN = 0,
+    FUNDAMENTAL_MAX = 94,
+    OEM_MIN         = 10000,
+    OEM_MAX         = 10000,
+    BASE_MAX        = 0xFFFF,
+};
+
 
 /**
  * The capabilities of a driver.
@@ -109,6 +4603,32 @@
 };
 
 /**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+    /**
+     * The operation type.
+     *
+     * Besides the values listed in {@link OperationType}, any value above
+     * {@link OperationTypeRange::BASE_MAX} is possible and should be interpreted
+     * as an extension type according to {@link Model::extensionNameToPrefix}.
+     */
+    OperationType type;
+
+    /**
+     * Describes the table that contains the indexes of the inputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
+    vec<uint32_t> inputs;
+
+    /**
+     * Describes the table that contains the indexes of the outputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
+    vec<uint32_t> outputs;
+};
+
+/**
  * Describes one operand of the model's graph.
  */
 struct Operand {
@@ -233,28 +4753,6 @@
 };
 
 /**
- * Describes one operation of the model's graph.
- */
-struct Operation {
-    /**
-     * The operation type.
-     */
-    OperationType type;
-
-    /**
-     * Describes the table that contains the indexes of the inputs of the
-     * operation. The offset is the index in the operandIndexes table.
-     */
-    vec<uint32_t> inputs;
-
-    /**
-     * Describes the table that contains the indexes of the outputs of the
-     * operation. The offset is the index in the operandIndexes table.
-     */
-    vec<uint32_t> outputs;
-};
-
-/**
  * A Neural Network Model.
  *
  * This includes not only the execution graph, but also constant data such as
diff --git a/neuralnetworks/1.3/types.t b/neuralnetworks/1.3/types.t
index d41cfd2..e06f5d6 100644
--- a/neuralnetworks/1.3/types.t
+++ b/neuralnetworks/1.3/types.t
@@ -44,6 +44,47 @@
     BASE_MAX        = 0xFFFF,
 };
 
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
+enum OperationType : int32_t {
+
+%insert Operation_1.0
+
+%insert Operation_1.1
+
+%insert Operation_1.2
+
+    /**
+     * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
+     * OEM operation and data types.
+     *
+     * This operation is OEM specific. It should only be used for OEM
+     * applications.
+     */
+    OEM_OPERATION = @1.2::OperationType:OEM_OPERATION,
+    /* ADDING A NEW FUNDAMENTAL OPERATION REQUIRES UPDATING THE VALUE OF
+     * OperationTypeRange::FUNDAMENTAL_MAX.
+     */
+    /* ADDING A NEW OEM OPERATION REQUIRES UPDATING THE VALUE OF
+     * OperationTypeRange::OEM_MAX.
+     */
+};
+
+/**
+ * The range of values in the OperationType enum.
+ */
+enum OperationTypeRange : uint32_t {
+    BASE_MIN        = 0,
+    FUNDAMENTAL_MIN = 0,
+%insert Operation_1.3_MAX
+    OEM_MIN         = 10000,
+    OEM_MAX         = 10000,
+    BASE_MAX        = 0xFFFF,
+};
+
 
 /**
  * The capabilities of a driver.
@@ -80,6 +121,32 @@
 };
 
 /**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+    /**
+     * The operation type.
+     *
+     * Besides the values listed in {@link OperationType}, any value above
+     * {@link OperationTypeRange::BASE_MAX} is possible and should be interpreted
+     * as an extension type according to {@link Model::extensionNameToPrefix}.
+     */
+    OperationType type;
+
+    /**
+     * Describes the table that contains the indexes of the inputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
+    vec<uint32_t> inputs;
+
+    /**
+     * Describes the table that contains the indexes of the outputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
+    vec<uint32_t> outputs;
+};
+
+/**
  * Describes one operand of the model's graph.
  */
 struct Operand {
@@ -204,28 +271,6 @@
 };
 
 /**
- * Describes one operation of the model's graph.
- */
-struct Operation {
-    /**
-     * The operation type.
-     */
-    OperationType type;
-
-    /**
-     * Describes the table that contains the indexes of the inputs of the
-     * operation. The offset is the index in the operandIndexes table.
-     */
-    vec<uint32_t> inputs;
-
-    /**
-     * Describes the table that contains the indexes of the outputs of the
-     * operation. The offset is the index in the operandIndexes table.
-     */
-    vec<uint32_t> outputs;
-};
-
-/**
  * A Neural Network Model.
  *
  * This includes not only the execution graph, but also constant data such as
diff --git a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
index 325d641..a1e04c5 100644
--- a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
@@ -63,7 +63,6 @@
 using V1_1::ExecutionPreference;
 using V1_2::Constant;
 using V1_2::MeasureTiming;
-using V1_2::OperationType;
 using V1_2::OutputShape;
 using V1_2::SymmPerChannelQuantParams;
 using V1_2::Timing;
diff --git a/neuralnetworks/1.3/vts/functional/TestAssertions.cpp b/neuralnetworks/1.3/vts/functional/TestAssertions.cpp
index 7361078..a7569e6 100644
--- a/neuralnetworks/1.3/vts/functional/TestAssertions.cpp
+++ b/neuralnetworks/1.3/vts/functional/TestAssertions.cpp
@@ -25,8 +25,6 @@
 #define CHECK_TEST_ENUM(EnumType, enumValue) \
     static_assert(static_cast<EnumType>(Test##EnumType::enumValue) == EnumType::enumValue)
 
-using V1_2::OperationType;
-
 CHECK_TEST_ENUM(OperandType, FLOAT32);
 CHECK_TEST_ENUM(OperandType, INT32);
 CHECK_TEST_ENUM(OperandType, UINT32);
diff --git a/neuralnetworks/1.3/vts/functional/ValidateModel.cpp b/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
index 1ff02dc..46bbd3f 100644
--- a/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
+++ b/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
@@ -27,7 +27,6 @@
 using V1_0::ErrorStatus;
 using V1_0::OperandLifeTime;
 using V1_1::ExecutionPreference;
-using V1_2::OperationType;
 using V1_2::OperationTypeRange;
 using V1_2::SymmPerChannelQuantParams;
 using HidlToken =
diff --git a/radio/config/1.0/vts/functional/radio_config_hidl_hal_utils.h b/radio/config/1.0/vts/functional/radio_config_hidl_hal_utils.h
index 6bc1b65..2722afe 100644
--- a/radio/config/1.0/vts/functional/radio_config_hidl_hal_utils.h
+++ b/radio/config/1.0/vts/functional/radio_config_hidl_hal_utils.h
@@ -27,6 +27,7 @@
 #include <gtest/gtest.h>
 #include <hidl/GtestPrinter.h>
 #include <hidl/ServiceManagement.h>
+#include <log/log.h>
 
 #include "vts_test_util.h"
 
diff --git a/radio/config/1.1/vts/functional/radio_config_hidl_hal_utils.h b/radio/config/1.1/vts/functional/radio_config_hidl_hal_utils.h
index e9951dc..4cdeb06 100644
--- a/radio/config/1.1/vts/functional/radio_config_hidl_hal_utils.h
+++ b/radio/config/1.1/vts/functional/radio_config_hidl_hal_utils.h
@@ -26,6 +26,7 @@
 #include <gtest/gtest.h>
 #include <hidl/GtestPrinter.h>
 #include <hidl/ServiceManagement.h>
+#include <log/log.h>
 
 #include "vts_test_util.h"
 
diff --git a/radio/config/1.2/vts/functional/radio_config_hidl_hal_utils.h b/radio/config/1.2/vts/functional/radio_config_hidl_hal_utils.h
index e9cbcbd..ba3f02e 100644
--- a/radio/config/1.2/vts/functional/radio_config_hidl_hal_utils.h
+++ b/radio/config/1.2/vts/functional/radio_config_hidl_hal_utils.h
@@ -28,6 +28,7 @@
 #include <gtest/gtest.h>
 #include <hidl/GtestPrinter.h>
 #include <hidl/ServiceManagement.h>
+#include <log/log.h>
 
 #include "vts_test_util.h"
 
@@ -98,20 +99,6 @@
             const ::android::hardware::hidl_vec<SimSlotStatus>& slotStatus);
 };
 
-// Test environment for Radio HIDL HAL.
-class RadioConfigHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
-  public:
-    // get the test environment singleton
-    static RadioConfigHidlEnvironment* Instance() {
-        static RadioConfigHidlEnvironment* instance = new RadioConfigHidlEnvironment;
-        return instance;
-    }
-    virtual void registerTestServices() override { registerTestService<IRadioConfig>(); }
-
-  private:
-    RadioConfigHidlEnvironment() {}
-};
-
 // The main test class for Radio config HIDL.
 class RadioConfigHidlTest : public ::testing::TestWithParam<std::string> {
   protected:
diff --git a/sensors/2.0/multihal/Android.bp b/sensors/2.0/multihal/Android.bp
index c13eaf2..811c455 100644
--- a/sensors/2.0/multihal/Android.bp
+++ b/sensors/2.0/multihal/Android.bp
@@ -43,10 +43,10 @@
     srcs: [
         "service.cpp",
         "HalProxy.cpp",
-        "ScopedWakelock.cpp",
     ],
     init_rc: ["android.hardware.sensors@2.0-service-multihal.rc"],
     vintf_fragments: ["android.hardware.sensors@2.0-multihal.xml"],
+    shared_libs: ["android.hardware.sensors@2.0-ScopedWakelock"]
 }
 
 cc_library_headers {
@@ -55,19 +55,40 @@
     export_include_dirs: ["include"],
 }
 
+cc_library_shared {
+    name: "android.hardware.sensors@2.0-ScopedWakelock",
+    defaults: [
+        "hidl_defaults",
+        "android.hardware.sensors@2.0-multihal-defaults",
+    ],
+    srcs: [
+        "ScopedWakelock.cpp",
+    ],
+    vendor_available: true,
+    export_header_lib_headers: [
+        "android.hardware.sensors@2.0-multihal.header"
+    ]
+}
+
 // The below targets should only be used for testing.
 cc_test_library {
     name: "android.hardware.sensors@2.0-HalProxy",
-    defaults: ["android.hardware.sensors@2.0-multihal-defaults"],
+    defaults: [
+        "hidl_defaults",
+        "android.hardware.sensors@2.0-multihal-defaults",
+    ],
     vendor_available: true,
     srcs: [
         "HalProxy.cpp",
-        "ScopedWakelock.cpp",
     ],
     export_header_lib_headers: [
         "android.hardware.sensors@2.0-multihal.header",
     ],
+    export_shared_lib_headers: [
+	"android.hardware.sensors@2.0-ScopedWakelock",
+    ],
     shared_libs: [
         "libutils",
+        "android.hardware.sensors@2.0-ScopedWakelock",
     ],
 }
diff --git a/sensors/2.0/multihal/tests/Android.bp b/sensors/2.0/multihal/tests/Android.bp
index e7f9499..1637312 100644
--- a/sensors/2.0/multihal/tests/Android.bp
+++ b/sensors/2.0/multihal/tests/Android.bp
@@ -25,6 +25,7 @@
     shared_libs: [
         "android.hardware.sensors@1.0",
         "android.hardware.sensors@2.0",
+	"android.hardware.sensors@2.0-ScopedWakelock",
         "libcutils",
         "libfmq",
         "libhardware",
@@ -83,6 +84,7 @@
     shared_libs: [
         "android.hardware.sensors@1.0",
         "android.hardware.sensors@2.0",
+        "android.hardware.sensors@2.0-ScopedWakelock",
         "libbase",
         "libcutils",
         "libfmq",
diff --git a/tests/extension/vibrator/aidl/client/test-cpp-client.cpp b/tests/extension/vibrator/aidl/client/test-cpp-client.cpp
index f6f5537..015a345 100644
--- a/tests/extension/vibrator/aidl/client/test-cpp-client.cpp
+++ b/tests/extension/vibrator/aidl/client/test-cpp-client.cpp
@@ -20,9 +20,9 @@
 #include <binder/IServiceManager.h>
 #include <gtest/gtest.h>
 
+using android::checked_interface_cast;
 using android::IBinder;
 using android::IInterface;
-using android::interface_cast;
 using android::OK;
 using android::sp;
 using android::waitForVintfService;
@@ -44,7 +44,7 @@
     // getting the extension
     sp<IBinder> ext;
     ASSERT_EQ(OK, IInterface::asBinder(vib)->getExtension(&ext));
-    sp<ICustomVibrator> cvib = interface_cast<ICustomVibrator>(ext);
+    sp<ICustomVibrator> cvib = checked_interface_cast<ICustomVibrator>(ext);
     ASSERT_NE(nullptr, cvib.get());
 
     // calling extension method
diff --git a/tests/memory/2.0/Android.bp b/tests/memory/2.0/Android.bp
new file mode 100644
index 0000000..5166652
--- /dev/null
+++ b/tests/memory/2.0/Android.bp
@@ -0,0 +1,12 @@
+hidl_interface {
+    name: "android.hardware.tests.memory@2.0",
+    root: "android.hardware",
+    srcs: [
+        "IMemoryInterface.hal",
+        "types.hal",
+    ],
+    interfaces: [
+        "android.hidl.base@1.0",
+    ],
+    gen_java: true,
+}
diff --git a/tests/memory/2.0/IMemoryInterface.hal b/tests/memory/2.0/IMemoryInterface.hal
new file mode 100644
index 0000000..2c824bf
--- /dev/null
+++ b/tests/memory/2.0/IMemoryInterface.hal
@@ -0,0 +1,12 @@
+package android.hardware.tests.memory@2.0;
+
+interface IMemoryInterface {
+    // Flips all the bits in the given memory buffer.
+    bitwiseNot(memory mem);
+    // Returns a read-only buffer of size 8, containing the bytes 0..7.
+    getTestMem() generates(memory mem);
+    // Given two memory regions of the same size, returns two memory fields of
+    // equal size, the first contains the byte-wise sum and the other the byte-
+    // wise difference.
+    getSumDiff(TwoMemory in) generates(TwoMemory out);
+};
diff --git a/tests/memory/2.0/types.hal b/tests/memory/2.0/types.hal
new file mode 100644
index 0000000..9ec357b
--- /dev/null
+++ b/tests/memory/2.0/types.hal
@@ -0,0 +1,6 @@
+package android.hardware.tests.memory@2.0;
+
+struct TwoMemory {
+    memory mem1;
+    memory mem2;
+};
diff --git a/vibrator/1.4/Android.bp b/vibrator/1.4/Android.bp
deleted file mode 100644
index acfc795..0000000
--- a/vibrator/1.4/Android.bp
+++ /dev/null
@@ -1,19 +0,0 @@
-// This file is autogenerated by hidl-gen -Landroidbp.
-
-hidl_interface {
-    name: "android.hardware.vibrator@1.4",
-    root: "android.hardware",
-    srcs: [
-        "types.hal",
-        "IVibrator.hal",
-        "IVibratorCallback.hal",
-    ],
-    interfaces: [
-        "android.hardware.vibrator@1.0",
-        "android.hardware.vibrator@1.1",
-        "android.hardware.vibrator@1.2",
-        "android.hardware.vibrator@1.3",
-        "android.hidl.base@1.0",
-    ],
-    gen_java: true,
-}
diff --git a/vibrator/1.4/IVibrator.hal b/vibrator/1.4/IVibrator.hal
deleted file mode 100644
index 913abe3..0000000
--- a/vibrator/1.4/IVibrator.hal
+++ /dev/null
@@ -1,57 +0,0 @@
-/*
- * Copyright (C) 2019 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package android.hardware.vibrator@1.4;
-
-import @1.0::EffectStrength;
-import @1.3::Effect;
-import @1.0::Status;
-import @1.3::IVibrator;
-import IVibratorCallback;
-
-interface IVibrator extends @1.3::IVibrator {
-    /**
-     * Determine capabilities of the vibrator HAL.
-     */
-    getCapabilities() generates (bitfield<Capabilities> capabilities);
-
-    /**
-     * Turn on vibrator
-     *
-     * This function must only be called after the previous timeout has expired or
-     * was canceled (through off()).
-     * @param timeoutMs number of milliseconds to vibrate.
-     * @param callback A callback used to inform Frameworks of state change, if supported.
-     * @return vibratorOnRet whether vibrator command was successful or not.
-     */
-    on_1_4(uint32_t timeoutMs, IVibratorCallback callback) generates (Status vibratorOnRet);
-
-    /**
-     * Fire off a predefined haptic event.
-     *
-     * @param effect The type of haptic event to trigger.
-     * @param strength The intensity of haptic event to trigger.
-     * @param callback A callback used to inform Frameworks of state change, if supported.
-     * @return status Whether the effect was successfully performed or not. Must
-     *     return Status::UNSUPPORTED_OPERATION if the effect is not supported.
-     * @return lengthMs The length of time the event is expected to take in
-     *     milliseconds. This doesn't need to be perfectly accurate, but should be a reasonable
-     *     approximation. Should be a positive, non-zero value if the returned status is Status::OK,
-     *     and set to 0 otherwise.
-     */
-    perform_1_4(Effect effect, EffectStrength strength, IVibratorCallback callback)
-        generates (Status status, uint32_t lengthMs);
-};
diff --git a/vibrator/1.4/types.hal b/vibrator/1.4/types.hal
deleted file mode 100644
index acc49b1..0000000
--- a/vibrator/1.4/types.hal
+++ /dev/null
@@ -1,22 +0,0 @@
-/*
- * Copyright (C) 2019 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package android.hardware.vibrator@1.4;
-
-enum Capabilities : uint32_t {
-    ON_COMPLETION_CALLBACK = 1 << 0,
-    PERFORM_COMPLETION_CALLBACK = 1 << 1,
-};
diff --git a/vibrator/1.4/vts/functional/VtsHalVibratorV1_4TargetTest.cpp b/vibrator/1.4/vts/functional/VtsHalVibratorV1_4TargetTest.cpp
deleted file mode 100644
index 1b6abe9..0000000
--- a/vibrator/1.4/vts/functional/VtsHalVibratorV1_4TargetTest.cpp
+++ /dev/null
@@ -1,203 +0,0 @@
-/*
- * Copyright (C) 2019 The Android Open Source Project
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#define LOG_TAG "vibrator_hidl_hal_test"
-
-#include <android-base/logging.h>
-#include <android/hardware/vibrator/1.0/types.h>
-#include <android/hardware/vibrator/1.4/IVibrator.h>
-#include <gtest/gtest.h>
-#include <hidl/GtestPrinter.h>
-#include <hidl/ServiceManagement.h>
-
-#include <getopt.h>
-#include <unistd.h>
-
-#include <future>
-
-using ::android::sp;
-using ::android::hardware::hidl_bitfield;
-using ::android::hardware::hidl_enum_range;
-using ::android::hardware::Return;
-using ::android::hardware::Void;
-using ::android::hardware::vibrator::V1_0::EffectStrength;
-using ::android::hardware::vibrator::V1_0::Status;
-using ::android::hardware::vibrator::V1_3::Effect;
-using ::android::hardware::vibrator::V1_4::Capabilities;
-using ::android::hardware::vibrator::V1_4::IVibrator;
-using ::android::hardware::vibrator::V1_4::IVibratorCallback;
-
-static uint32_t sCompletionLimitMs = UINT32_MAX;
-
-#define EXPECT_OK(ret) ASSERT_TRUE((ret).isOk())
-
-class CompletionCallback : public IVibratorCallback {
-  public:
-    CompletionCallback(std::function<void()> callback) : mCallback(callback) {}
-    Return<void> onComplete() override {
-        mCallback();
-        return Void();
-    }
-
-  private:
-    std::function<void()> mCallback;
-};
-
-class VibratorHidlTest_1_4 : public testing::TestWithParam<std::string> {
-  public:
-    virtual void SetUp() override {
-        vibrator = IVibrator::getService(GetParam());
-        ASSERT_NE(vibrator, nullptr);
-        capabilities = vibrator->getCapabilities();
-    }
-
-    virtual void TearDown() override {}
-
-    sp<IVibrator> vibrator;
-    hidl_bitfield<Capabilities> capabilities;
-};
-
-TEST_P(VibratorHidlTest_1_4, OnWithCallback) {
-    if (capabilities & Capabilities::ON_COMPLETION_CALLBACK) {
-        std::promise<void> completionPromise;
-        std::future<void> completionFuture{completionPromise.get_future()};
-        sp<CompletionCallback> callback =
-                new CompletionCallback([&completionPromise] { completionPromise.set_value(); });
-        uint32_t duration = 250;
-        std::chrono::milliseconds timeout{duration * 2};
-        EXPECT_EQ(Status::OK, vibrator->on_1_4(duration, callback));
-        EXPECT_EQ(completionFuture.wait_for(timeout), std::future_status::ready);
-        vibrator->off();
-    }
-}
-
-static void validatePerformEffectUnsupportedOperation(Status status, uint32_t lengthMs) {
-    ASSERT_EQ(Status::UNSUPPORTED_OPERATION, status);
-    ASSERT_EQ(static_cast<uint32_t>(0), lengthMs)
-            << "Effects that return UNSUPPORTED_OPERATION must have a duration of zero";
-}
-
-static void validatePerformEffect(Status status, uint32_t lengthMs) {
-    ASSERT_TRUE(status == Status::OK || status == Status::UNSUPPORTED_OPERATION);
-    if (status == Status::OK) {
-        ASSERT_LT(static_cast<uint32_t>(0), lengthMs)
-                << "Effects that return OK must return a positive duration";
-    } else {
-        validatePerformEffectUnsupportedOperation(status, lengthMs);
-    }
-}
-
-/*
- * Test to make sure effects within the valid range return are either supported and return OK with
- * a valid duration, or are unsupported and return UNSUPPORTED_OPERATION with a duration of 0.
- */
-TEST_P(VibratorHidlTest_1_4, PerformEffect_1_4) {
-    Status performStatus;
-    uint32_t performLength;
-    auto validateWrapper = [&](Status status, uint32_t lengthMs) {
-        performStatus = status;
-        performLength = lengthMs;
-        validatePerformEffect(status, lengthMs);
-    };
-    for (const auto& effect : hidl_enum_range<Effect>()) {
-        for (const auto& strength : hidl_enum_range<EffectStrength>()) {
-            std::promise<void> completionPromise;
-            std::future<void> completionFuture{completionPromise.get_future()};
-            sp<CompletionCallback> callback =
-                    new CompletionCallback([&completionPromise] { completionPromise.set_value(); });
-            EXPECT_OK(vibrator->perform_1_4(effect, strength, callback, validateWrapper));
-            if (performStatus == Status::OK && performLength < sCompletionLimitMs &&
-                (capabilities & Capabilities::PERFORM_COMPLETION_CALLBACK)) {
-                std::chrono::milliseconds timeout{performLength * 2};
-                EXPECT_EQ(completionFuture.wait_for(timeout), std::future_status::ready);
-            }
-        }
-    }
-}
-
-/*
- * Test to make sure effect values above the valid range are rejected.
- */
-TEST_P(VibratorHidlTest_1_4, PerformEffect_1_4_BadEffects_AboveValidRange) {
-    Effect effect = *std::prev(hidl_enum_range<Effect>().end());
-    Effect badEffect = static_cast<Effect>(static_cast<int32_t>(effect) + 1);
-    EXPECT_OK(vibrator->perform_1_4(badEffect, EffectStrength::LIGHT, nullptr,
-                                    validatePerformEffectUnsupportedOperation));
-}
-
-/*
- * Test to make sure effect values below the valid range are rejected.
- */
-TEST_P(VibratorHidlTest_1_4, PerformEffect_1_4_BadEffects_BelowValidRange) {
-    Effect effect = *hidl_enum_range<Effect>().begin();
-    Effect badEffect = static_cast<Effect>(static_cast<int32_t>(effect) - 1);
-    EXPECT_OK(vibrator->perform_1_4(badEffect, EffectStrength::LIGHT, nullptr,
-                                    validatePerformEffectUnsupportedOperation));
-}
-
-/*
- * Test to make sure strength values above the valid range are rejected.
- */
-TEST_P(VibratorHidlTest_1_4, PerformEffect_1_4_BadStrength_AboveValidRange) {
-    EffectStrength strength = *std::prev(hidl_enum_range<EffectStrength>().end());
-    EffectStrength badStrength = static_cast<EffectStrength>(static_cast<int32_t>(strength) + 1);
-    EXPECT_OK(vibrator->perform_1_4(Effect::THUD, badStrength, nullptr,
-                                    validatePerformEffectUnsupportedOperation));
-}
-
-/*
- * Test to make sure strength values below the valid range are rejected.
- */
-TEST_P(VibratorHidlTest_1_4, PerformEffect_1_4_BadStrength_BelowValidRange) {
-    EffectStrength strength = *hidl_enum_range<EffectStrength>().begin();
-    EffectStrength badStrength = static_cast<EffectStrength>(static_cast<int32_t>(strength) - 1);
-    EXPECT_OK(vibrator->perform_1_4(Effect::THUD, badStrength, nullptr,
-                                    validatePerformEffectUnsupportedOperation));
-}
-
-INSTANTIATE_TEST_SUITE_P(
-        PerInstance, VibratorHidlTest_1_4,
-        testing::ValuesIn(android::hardware::getAllHalInstanceNames(IVibrator::descriptor)),
-        android::hardware::PrintInstanceNameToString);
-
-enum {
-    OPTION_COMPLETION_LIMIT_MS,
-};
-
-int main(int argc, char** argv) {
-    struct option options[] = {
-            {"completion-limit-ms", required_argument, 0, OPTION_COMPLETION_LIMIT_MS}, {}};
-
-    printf("Running main() from %s\n", __FILE__);
-    testing::InitGoogleTest(&argc, argv);
-
-    while (true) {
-        int opt = getopt_long(argc, argv, "", options, nullptr);
-        if (opt == -1) {
-            break;
-        }
-        switch (opt) {
-            case OPTION_COMPLETION_LIMIT_MS:
-                std::istringstream(optarg) >> sCompletionLimitMs;
-                break;
-            default:
-                printf("Unrecognized option\n");
-                return -EINVAL;
-        }
-    }
-
-    return RUN_ALL_TESTS();
-}
diff --git a/vibrator/aidl/Android.bp b/vibrator/aidl/Android.bp
index cd5439f..e26041a 100644
--- a/vibrator/aidl/Android.bp
+++ b/vibrator/aidl/Android.bp
@@ -5,5 +5,11 @@
         "android/hardware/vibrator/*.aidl",
     ],
     stability: "vintf",
+    backend: {
+        ndk: {
+            vndk: {
+                enabled: true,
+            },
+        },
+    },
 }
-