diff --git a/automotive/can/1.0/default/Android.bp b/automotive/can/1.0/default/Android.bp
index 8aa1d6b..ee2e92b 100644
--- a/automotive/can/1.0/default/Android.bp
+++ b/automotive/can/1.0/default/Android.bp
@@ -39,6 +39,7 @@
         "CanBus.cpp",
         "CanBusNative.cpp",
         "CanBusVirtual.cpp",
+        "CanBusSlcan.cpp",
         "CanController.cpp",
         "CanSocket.cpp",
         "CloseHandle.cpp",
diff --git a/automotive/can/1.0/default/CanBus.cpp b/automotive/can/1.0/default/CanBus.cpp
index 38a9974..42d2e3c 100644
--- a/automotive/can/1.0/default/CanBus.cpp
+++ b/automotive/can/1.0/default/CanBus.cpp
@@ -30,9 +30,7 @@
 namespace V1_0 {
 namespace implementation {
 
-/**
- * Whether to log sent/received packets.
- */
+/** Whether to log sent/received packets. */
 static constexpr bool kSuperVerbose = false;
 
 Return<Result> CanBus::send(const CanMessage& message) {
@@ -85,6 +83,8 @@
     return {};
 }
 
+CanBus::CanBus() {}
+
 CanBus::CanBus(const std::string& ifname) : mIfname(ifname) {}
 
 CanBus::~CanBus() {
diff --git a/automotive/can/1.0/default/CanBus.h b/automotive/can/1.0/default/CanBus.h
index 30a2924..365e90c 100644
--- a/automotive/can/1.0/default/CanBus.h
+++ b/automotive/can/1.0/default/CanBus.h
@@ -48,12 +48,22 @@
     bool down();
 
   protected:
+    /**
+     * Blank constructor, since some interface types (such as SLCAN) don't get a name until after
+     * being initialized.
+     *
+     * If using this constructor, you MUST initialize mIfname prior to the completion of preUp().
+     */
+    CanBus();
+
     CanBus(const std::string& ifname);
 
     /**
      * Prepare the SocketCAN interface.
      *
      * After calling this method, mIfname network interface is available and ready to be brought up.
+     *
+     * \return OK on success, or an error state on failure. See ICanController::Result
      */
     virtual ICanController::Result preUp();
 
@@ -61,11 +71,13 @@
      * Cleanup after bringing the interface down.
      *
      * This is a counterpart to preUp().
+     *
+     * \return true upon success and false upon failure
      */
     virtual bool postDown();
 
     /** Network interface name. */
-    const std::string mIfname;
+    std::string mIfname;
 
   private:
     struct CanMessageListener {
diff --git a/automotive/can/1.0/default/CanBusSlcan.cpp b/automotive/can/1.0/default/CanBusSlcan.cpp
new file mode 100644
index 0000000..7dce838
--- /dev/null
+++ b/automotive/can/1.0/default/CanBusSlcan.cpp
@@ -0,0 +1,166 @@
+/*
+ * 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.
+ */
+
+#include "CanBusSlcan.h"
+
+#include <android-base/logging.h>
+#include <libnetdevice/can.h>
+#include <libnetdevice/libnetdevice.h>
+
+#include <net/if.h>
+#include <sys/ioctl.h>
+#include <sys/stat.h>
+#include <termios.h>
+
+namespace android {
+namespace hardware {
+namespace automotive {
+namespace can {
+namespace V1_0 {
+namespace implementation {
+
+namespace slcanprotocol {
+static const std::string kOpenCommand = "O\r";
+static const std::string kCloseCommand = "C\r";
+static constexpr int kSlcanDiscipline = N_SLCAN;
+static constexpr int kDefaultDiscipline = N_TTY;
+
+static const std::map<uint32_t, std::string> kBitrateCommands = {
+        {10000, "C\rS0\r"},  {20000, "C\rS1\r"},  {50000, "C\rS2\r"},
+        {100000, "C\rS3\r"}, {125000, "C\rS4\r"}, {250000, "C\rS5\r"},
+        {500000, "C\rS6\r"}, {800000, "C\rS7\r"}, {1000000, "C\rS8\r"}};
+}  // namespace slcanprotocol
+
+/**
+ * Serial Line CAN constructor
+ * \param string uartName - name of slcan device (e.x. /dev/ttyUSB0)
+ * \param uint32_t bitrate - speed of the CAN bus (125k = MSCAN, 500k = HSCAN)
+ */
+CanBusSlcan::CanBusSlcan(const std::string& uartName, uint32_t bitrate)
+    : CanBus(), mUartName(uartName), kBitrate(bitrate) {}
+
+ICanController::Result CanBusSlcan::preUp() {
+    // verify valid bitrate and translate to serial command format
+    const auto lookupIt = slcanprotocol::kBitrateCommands.find(kBitrate);
+    if (lookupIt == slcanprotocol::kBitrateCommands.end()) {
+        return ICanController::Result::BAD_BAUDRATE;
+    }
+    const auto canBitrateCommand = lookupIt->second;
+
+    /* Attempt to open the uart in r/w without blocking or becoming the
+     * controlling terminal */
+    mFd = base::unique_fd(open(mUartName.c_str(), O_RDWR | O_NONBLOCK | O_NOCTTY));
+    if (!mFd.ok()) {
+        LOG(ERROR) << "SLCAN Failed to open " << mUartName << ": " << strerror(errno);
+        return ICanController::Result::BAD_ADDRESS;
+    }
+
+    // blank terminal settings and pull them from the device
+    struct termios terminalSettings = {};
+    if (tcgetattr(mFd.get(), &terminalSettings) < 0) {
+        LOG(ERROR) << "Failed to read attrs of" << mUartName << ": " << strerror(errno);
+        return ICanController::Result::UNKNOWN_ERROR;
+    }
+
+    // change settings to raw mode
+    cfmakeraw(&terminalSettings);
+
+    // disable software flow control
+    terminalSettings.c_iflag &= ~IXOFF;
+    // enable hardware flow control
+    terminalSettings.c_cflag |= CRTSCTS;
+
+    struct serial_struct serialSettings;
+    // get serial settings
+    if (ioctl(mFd.get(), TIOCGSERIAL, &serialSettings) < 0) {
+        LOG(ERROR) << "Failed to read serial settings from " << mUartName << ": "
+                   << strerror(errno);
+        return ICanController::Result::UNKNOWN_ERROR;
+    }
+    // set low latency mode
+    serialSettings.flags |= ASYNC_LOW_LATENCY;
+    // apply serial settings
+    if (ioctl(mFd.get(), TIOCSSERIAL, &serialSettings) < 0) {
+        LOG(ERROR) << "Failed to set low latency mode on " << mUartName << ": " << strerror(errno);
+        return ICanController::Result::UNKNOWN_ERROR;
+    }
+
+    /* TCSADRAIN applies settings after we finish writing the rest of our
+     * changes (as opposed to TCSANOW, which changes immediately) */
+    if (tcsetattr(mFd.get(), TCSADRAIN, &terminalSettings) < 0) {
+        LOG(ERROR) << "Failed to apply terminal settings to " << mUartName << ": "
+                   << strerror(errno);
+        return ICanController::Result::UNKNOWN_ERROR;
+    }
+
+    // apply speed setting for CAN
+    if (write(mFd.get(), canBitrateCommand.c_str(), canBitrateCommand.length()) <= 0) {
+        LOG(ERROR) << "Failed to apply CAN bitrate: " << strerror(errno);
+        return ICanController::Result::UNKNOWN_ERROR;
+    }
+
+    // set open flag TODO: also support listen only
+    if (write(mFd.get(), slcanprotocol::kOpenCommand.c_str(),
+              slcanprotocol::kOpenCommand.length()) <= 0) {
+        LOG(ERROR) << "Failed to set open flag: " << strerror(errno);
+        return ICanController::Result::UNKNOWN_ERROR;
+    }
+
+    // set line discipline to slcan
+    if (ioctl(mFd.get(), TIOCSETD, &slcanprotocol::kSlcanDiscipline) < 0) {
+        LOG(ERROR) << "Failed to set line discipline to slcan: " << strerror(errno);
+        return ICanController::Result::UNKNOWN_ERROR;
+    }
+
+    // get the name of the device we created
+    struct ifreq ifrequest = {};
+    if (ioctl(mFd.get(), SIOCGIFNAME, ifrequest.ifr_name) < 0) {
+        LOG(ERROR) << "Failed to get the name of the created device: " << strerror(errno);
+        return ICanController::Result::UNKNOWN_ERROR;
+    }
+
+    // Update the CanBus object with name that was assigned to it
+    mIfname = ifrequest.ifr_name;
+
+    return ICanController::Result::OK;
+}
+
+bool CanBusSlcan::postDown() {
+    // reset the line discipline to TTY mode
+    if (ioctl(mFd.get(), TIOCSETD, &slcanprotocol::kDefaultDiscipline) < 0) {
+        LOG(ERROR) << "Failed to reset line discipline!";
+        return false;
+    }
+
+    // issue the close command
+    if (write(mFd.get(), slcanprotocol::kCloseCommand.c_str(),
+              slcanprotocol::kCloseCommand.length()) <= 0) {
+        LOG(ERROR) << "Failed to close tty!";
+        return false;
+    }
+
+    // close our unique_fd
+    mFd.reset();
+
+    return true;
+}
+
+}  // namespace implementation
+}  // namespace V1_0
+}  // namespace can
+}  // namespace automotive
+}  // namespace hardware
+}  // namespace android
diff --git a/automotive/can/1.0/default/CanBusSlcan.h b/automotive/can/1.0/default/CanBusSlcan.h
new file mode 100644
index 0000000..2713da8
--- /dev/null
+++ b/automotive/can/1.0/default/CanBusSlcan.h
@@ -0,0 +1,50 @@
+/*
+ * 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.
+ */
+
+#pragma once
+
+#include <linux/serial.h>
+#include <linux/tty.h>
+#include <net/if.h>
+#include <termios.h>
+#include "CanBus.h"
+
+namespace android {
+namespace hardware {
+namespace automotive {
+namespace can {
+namespace V1_0 {
+namespace implementation {
+
+struct CanBusSlcan : public CanBus {
+    CanBusSlcan(const std::string& uartName, uint32_t bitrate);
+
+  protected:
+    virtual ICanController::Result preUp() override;
+    virtual bool postDown() override;
+
+  private:
+    const std::string mUartName;
+    const uint32_t kBitrate;
+    base::unique_fd mFd;
+};
+
+}  // namespace implementation
+}  // namespace V1_0
+}  // namespace can
+}  // namespace automotive
+}  // namespace hardware
+}  // namespace android
diff --git a/automotive/can/1.0/default/CanController.cpp b/automotive/can/1.0/default/CanController.cpp
index 3b63fe4..ffdc912 100644
--- a/automotive/can/1.0/default/CanController.cpp
+++ b/automotive/can/1.0/default/CanController.cpp
@@ -17,6 +17,7 @@
 #include "CanController.h"
 
 #include "CanBusNative.h"
+#include "CanBusSlcan.h"
 #include "CanBusVirtual.h"
 
 #include <android-base/logging.h>
@@ -34,10 +35,8 @@
 using IfaceIdDisc = ICanController::BusConfiguration::InterfaceIdentifier::hidl_discriminator;
 
 Return<void> CanController::getSupportedInterfaceTypes(getSupportedInterfaceTypes_cb _hidl_cb) {
-    _hidl_cb({
-            ICanController::InterfaceType::VIRTUAL,
-            ICanController::InterfaceType::SOCKETCAN,
-    });
+    _hidl_cb({ICanController::InterfaceType::VIRTUAL, ICanController::InterfaceType::SOCKETCAN,
+              ICanController::InterfaceType::SLCAN});
     return {};
 }
 
@@ -77,6 +76,12 @@
         } else {
             return ICanController::Result::BAD_ADDRESS;
         }
+    } else if (config.iftype == ICanController::InterfaceType::SLCAN) {
+        if (config.interfaceId.getDiscriminator() == IfaceIdDisc::address) {
+            busService = new CanBusSlcan(config.interfaceId.address(), config.baudrate);
+        } else {
+            return ICanController::Result::BAD_ADDRESS;
+        }
     } else {
         return ICanController::Result::NOT_SUPPORTED;
     }
diff --git a/automotive/can/1.0/default/CanSocket.cpp b/automotive/can/1.0/default/CanSocket.cpp
index ecf4044..86e12d1 100644
--- a/automotive/can/1.0/default/CanSocket.cpp
+++ b/automotive/can/1.0/default/CanSocket.cpp
@@ -33,12 +33,10 @@
 
 using namespace std::chrono_literals;
 
-/**
- * How frequently the read thread checks whether the interface was asked to be down.
+/* How frequently the read thread checks whether the interface was asked to be down.
  *
  * Note: This does *not* affect read timing or bandwidth, just CPU load vs time to
- *       down the interface.
- */
+ *       down the interface. */
 static constexpr auto kReadPooling = 100ms;
 
 std::unique_ptr<CanSocket> CanSocket::open(const std::string& ifname, ReadCallback rdcb,
@@ -105,8 +103,7 @@
 
     while (!mStopReaderThread) {
         /* The ideal would be to have a blocking read(3) call and interrupt it with shutdown(3).
-         * This is unfortunately not supported for SocketCAN, so we need to rely on select(3).
-         */
+         * This is unfortunately not supported for SocketCAN, so we need to rely on select(3). */
         const auto sel = selectRead(mSocket, kReadPooling);
         if (sel == 0) continue;  // timeout
         if (sel == -1) {
@@ -127,8 +124,7 @@
          * Apart from the added complexity, it's possible the added calculations and system calls
          * would add so much time to the processing pipeline so the precision of the reported time
          * was buried under the subsystem latency. Let's just use a local time since boot here and
-         * leave precise hardware timestamps for custom proprietary implementations (if needed).
-         */
+         * leave precise hardware timestamps for custom proprietary implementations (if needed). */
         const std::chrono::nanoseconds ts(elapsedRealtimeNano());
 
         if (nbytes != CAN_MTU) {
diff --git a/automotive/can/1.0/default/CanSocket.h b/automotive/can/1.0/default/CanSocket.h
index 284e1ea..c98330b 100644
--- a/automotive/can/1.0/default/CanSocket.h
+++ b/automotive/can/1.0/default/CanSocket.h
@@ -31,9 +31,7 @@
 namespace V1_0 {
 namespace implementation {
 
-/**
- * Wrapper around SocketCAN socket.
- */
+/** Wrapper around SocketCAN socket. */
 struct CanSocket {
     using ReadCallback = std::function<void(const struct canfd_frame&, std::chrono::nanoseconds)>;
     using ErrorCallback = std::function<void(int errnoVal)>;
diff --git a/automotive/can/1.0/default/CloseHandle.h b/automotive/can/1.0/default/CloseHandle.h
index 5191739..eade109 100644
--- a/automotive/can/1.0/default/CloseHandle.h
+++ b/automotive/can/1.0/default/CloseHandle.h
@@ -26,9 +26,7 @@
 namespace V1_0 {
 namespace implementation {
 
-/**
- * Generic ICloseHandle implementation ignoring double-close events.
- */
+/** Generic ICloseHandle implementation ignoring double-close events. */
 struct CloseHandle : public ICloseHandle {
     using Callback = std::function<void()>;
 
diff --git a/automotive/can/1.0/default/libnetdevice/NetlinkRequest.h b/automotive/can/1.0/default/libnetdevice/NetlinkRequest.h
index 21202e3..ba9b65b 100644
--- a/automotive/can/1.0/default/libnetdevice/NetlinkRequest.h
+++ b/automotive/can/1.0/default/libnetdevice/NetlinkRequest.h
@@ -27,9 +27,7 @@
 typedef unsigned short rtattrtype_t;  // as in rtnetlink.h
 typedef __u16 nlmsgtype_t;            // as in netlink.h
 
-/**
- * Implementation details, do not use outside NetlinkRequest template.
- */
+/** Implementation details, do not use outside NetlinkRequest template. */
 namespace impl {
 
 struct rtattr* addattr_l(struct nlmsghdr* n, size_t maxLen, rtattrtype_t type, const void* data,
@@ -59,7 +57,7 @@
         mRequest.nlmsg.nlmsg_flags = flags;
     }
 
-    /** Returns pointer to raw netlink message header. */
+    /** \return pointer to raw netlink message header. */
     struct nlmsghdr* header() {
         return &mRequest.nlmsg;
     }
@@ -89,9 +87,7 @@
         if (ap == nullptr) mIsGood = false;
     }
 
-    /**
-     * Guard class to frame nested attributes. See nest(int).
-     */
+    /** Guard class to frame nested attributes. See nest(int). */
     struct Nest {
         Nest(NetlinkRequest& req, rtattrtype_t type) : mReq(req), mAttr(req.nestStart(type)) {}
         ~Nest() { mReq.nestEnd(mAttr); }
diff --git a/automotive/can/1.0/default/libnetdevice/NetlinkSocket.h b/automotive/can/1.0/default/libnetdevice/NetlinkSocket.h
index 81d6224..90e1f3f 100644
--- a/automotive/can/1.0/default/libnetdevice/NetlinkSocket.h
+++ b/automotive/can/1.0/default/libnetdevice/NetlinkSocket.h
@@ -38,8 +38,8 @@
     /**
      * Send Netlink message to Kernel.
      *
-     * @param msg Message to send, nlmsg_seq will be set to next sequence number
-     * @return true, if succeeded
+     * \param msg Message to send, nlmsg_seq will be set to next sequence number
+     * \return true, if succeeded
      */
     template <class T, unsigned int BUFSIZE>
     bool send(NetlinkRequest<T, BUFSIZE>& req) {
@@ -50,7 +50,7 @@
     /**
      * Receive Netlink ACK message from Kernel.
      *
-     * @return true if received ACK message, false in case of error
+     * \return true if received ACK message, false in case of error
      */
     bool receiveAck();
 
diff --git a/automotive/can/1.0/default/libnetdevice/include/libnetdevice/can.h b/automotive/can/1.0/default/libnetdevice/include/libnetdevice/can.h
index ec3f962..d75361e 100644
--- a/automotive/can/1.0/default/libnetdevice/include/libnetdevice/can.h
+++ b/automotive/can/1.0/default/libnetdevice/include/libnetdevice/can.h
@@ -27,13 +27,16 @@
 /**
  * Opens and binds SocketCAN socket.
  *
- * @param ifname Interface to open a socket against
- * @return Socket's FD or -1 in case of failure
+ * \param ifname Interface to open a socket against
+ * \return Socket's FD or -1 in case of failure
  */
 base::unique_fd socket(const std::string& ifname);
 
 /**
  * Sets CAN interface bitrate.
+ *
+ * \param ifname Interface for which the bitrate is to be set
+ * \return true on success, false on failure
  */
 bool setBitrate(std::string ifname, uint32_t bitrate);
 
diff --git a/automotive/can/1.0/default/libnetdevice/include/libnetdevice/libnetdevice.h b/automotive/can/1.0/default/libnetdevice/include/libnetdevice/libnetdevice.h
index 33d5de5..e22eafb 100644
--- a/automotive/can/1.0/default/libnetdevice/include/libnetdevice/libnetdevice.h
+++ b/automotive/can/1.0/default/libnetdevice/include/libnetdevice/libnetdevice.h
@@ -25,49 +25,49 @@
 /**
  * Checks, if the network interface exists.
  *
- * @param ifname Interface to check
- * @return true if it exists, false otherwise
+ * \param ifname Interface to check
+ * \return true if it exists, false otherwise
  */
 bool exists(std::string ifname);
 
 /**
  * Checks if network interface is up.
  *
- * @param ifname Interface to check
- * @return true/false if the check succeeded, nullopt otherwise
+ * \param ifname Interface to check
+ * \return true/false if the check succeeded, nullopt otherwise
  */
 std::optional<bool> isUp(std::string ifname);
 
 /**
  * Brings network interface up.
  *
- * @param ifname Interface to bring up
- * @return true in case of success, false otherwise
+ * \param ifname Interface to bring up
+ * \return true in case of success, false otherwise
  */
 bool up(std::string ifname);
 
 /**
  * Brings network interface down.
  *
- * @param ifname Interface to bring down
- * @return true in case of success, false otherwise
+ * \param ifname Interface to bring down
+ * \return true in case of success, false otherwise
  */
 bool down(std::string ifname);
 
 /**
  * Adds virtual link.
  *
- * @param dev the name of the new virtual device
- * @param type the type of the new device
- * @return true in case of success, false otherwise
+ * \param dev the name of the new virtual device
+ * \param type the type of the new device
+ * \return true in case of success, false otherwise
  */
 bool add(std::string dev, std::string type);
 
 /**
  * Deletes virtual link.
  *
- * @param dev the name of the device to remove
- * @return true in case of success, false otherwise
+ * \param dev the name of the device to remove
+ * \return true in case of success, false otherwise
  */
 bool del(std::string dev);
 
diff --git a/automotive/can/1.0/tools/canhaldump.cpp b/automotive/can/1.0/tools/canhaldump.cpp
index 5713d17..99fd14a 100644
--- a/automotive/can/1.0/tools/canhaldump.cpp
+++ b/automotive/can/1.0/tools/canhaldump.cpp
@@ -49,7 +49,7 @@
         } else {
             std::cout << "   [" << message.payload.size() << "] ";
             for (const auto byte : message.payload) {
-                std::cout << " " << unsigned(byte);
+                std::cout << " " << std::setfill('0') << std::setw(2) << unsigned(byte);
             }
         }
         std::cout << std::nouppercase << std::dec << std::endl;
diff --git a/automotive/can/1.0/vts/functional/VtsHalCanBusVirtualV1_0TargetTest.cpp b/automotive/can/1.0/vts/functional/VtsHalCanBusVirtualV1_0TargetTest.cpp
index ba29c29..225984d 100644
--- a/automotive/can/1.0/vts/functional/VtsHalCanBusVirtualV1_0TargetTest.cpp
+++ b/automotive/can/1.0/vts/functional/VtsHalCanBusVirtualV1_0TargetTest.cpp
@@ -91,8 +91,7 @@
         EXPECT_EQ(ICanController::Result::OK, result);
 
         /* Not using ICanBus::getService here, since it ignores interfaces not in the manifest
-         * file -- this is a test, so we don't want to add dummy services to a device manifest.
-         */
+         * file -- this is a test, so we don't want to add dummy services to a device manifest. */
         auto manager = hidl::manager::V1_2::IServiceManager::getService();
         auto service = manager->get(ICanBus::descriptor, config.name);
         mBus = ICanBus::castFrom(service);
diff --git a/automotive/can/1.0/vts/functional/VtsHalCanControllerV1_0TargetTest.cpp b/automotive/can/1.0/vts/functional/VtsHalCanControllerV1_0TargetTest.cpp
index 70f9fe4..64e7a96 100644
--- a/automotive/can/1.0/vts/functional/VtsHalCanControllerV1_0TargetTest.cpp
+++ b/automotive/can/1.0/vts/functional/VtsHalCanControllerV1_0TargetTest.cpp
@@ -95,8 +95,7 @@
 
 void CanControllerHalTest::assertRegistered(std::string srvname, bool expectRegistered) {
     /* Not using ICanBus::tryGetService here, since it ignores interfaces not in the manifest
-     * file -- this is a test, so we don't want to add dummy services to a device manifest.
-     */
+     * file -- this is a test, so we don't want to add dummy services to a device manifest. */
     auto manager = hidl::manager::V1_2::IServiceManager::getService();
     auto busService = manager->get(ICanBus::descriptor, srvname);
     ASSERT_EQ(expectRegistered, busService.withDefault(nullptr) != nullptr)
diff --git a/automotive/vehicle/2.0/default/common/include/vhal_v2_0/VehicleConnector.h b/automotive/vehicle/2.0/default/common/include/vhal_v2_0/VehicleConnector.h
new file mode 100644
index 0000000..56ecd67
--- /dev/null
+++ b/automotive/vehicle/2.0/default/common/include/vhal_v2_0/VehicleConnector.h
@@ -0,0 +1,141 @@
+/*
+ * 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 android_hardware_automotive_vehicle_V2_0_VehicleConnector_H_
+#define android_hardware_automotive_vehicle_V2_0_VehicleConnector_H_
+
+#include <vector>
+
+#include <android/hardware/automotive/vehicle/2.0/types.h>
+
+namespace android {
+namespace hardware {
+namespace automotive {
+namespace vehicle {
+namespace V2_0 {
+
+/**
+ *  This file defines the interface of client/server pair for HAL-vehicle
+ *  communication. Vehicle HAL may use this interface to talk to the vehicle
+ *  regardless of the underlying communication channels.
+ */
+
+/**
+ *  Vehicle HAL talks to the vehicle through a client, instead of accessing
+ *  the car bus directly, to give us more flexibility on the implementation.
+ *  Android OS do not need direct access to the vehicle, and the communication
+ *  channel is also customizable.
+ *
+ *  Client lives on the Android (HAL) side to talk to the vehicle
+ */
+class IVehicleClient {
+  public:
+    IVehicleClient() = default;
+
+    IVehicleClient(const IVehicleClient&) = delete;
+
+    IVehicleClient& operator=(const IVehicleClient&) = delete;
+
+    IVehicleClient(IVehicleClient&&) = default;
+
+    virtual ~IVehicleClient() = default;
+
+    // Get configuration of all properties from server
+    virtual std::vector<VehiclePropConfig> getAllPropertyConfig() const = 0;
+
+    // Send the set property request to server
+    virtual StatusCode setProperty(const VehiclePropValue& value) = 0;
+
+    // Receive a new property value from server
+    virtual void onPropertyValue(const VehiclePropValue& value) = 0;
+};
+
+/**
+ *  Server lives on the vehicle side to talk to Android HAL
+ */
+class IVehicleServer {
+  public:
+    IVehicleServer() = default;
+
+    IVehicleServer(const IVehicleServer&) = delete;
+
+    IVehicleServer& operator=(const IVehicleServer&) = delete;
+
+    IVehicleServer(IVehicleServer&&) = default;
+
+    virtual ~IVehicleServer() = default;
+
+    // Receive the get property configuration request from HAL.
+    // Return a list of all property config
+    virtual std::vector<VehiclePropConfig> onGetAllPropertyConfig() const = 0;
+
+    // Receive the set property request from HAL.
+    // Process the setting and return the status code
+    virtual StatusCode onSetProperty(const VehiclePropValue& value) = 0;
+
+    // Receive a new property value from car (via direct connection to the car bus or the emulator)
+    // and forward the value to HAL
+    virtual void onPropertyValueFromCar(const VehiclePropValue& value) = 0;
+};
+
+/**
+ *  If Android has direct access to the vehicle, then the client and
+ *  the server may act in passthrough mode to avoid extra IPC
+ *
+ *  Template is used here for spliting the logic of operating Android objects (VehicleClientType),
+ *  talking to cars (VehicleServerType) and the commucation between client and server (passthrough
+ *  mode in this case), so that we can easily combine different parts together without duplicating
+ *  codes (for example, in Google VHAL, the server talks to the fake car in the same way no matter
+ *  if it is on top of passthrough connector or VSOCK or any other communication channels between
+ *  client and server)
+ *
+ *  The alternative may be factoring the common logic of every operations for both client and
+ *  server. Which is not always the case. Making sure different non-template connectors calling
+ *  the same method is hard, especially when the engineer maintaining the code may not be aware
+ *  of it when making changes. Template is a clean and easy way to solve this problem in this
+ *  case.
+ */
+template <typename VehicleClientType, typename VehicleServerType>
+class IPassThroughConnector : public VehicleClientType, public VehicleServerType {
+    static_assert(std::is_base_of_v<IVehicleClient, VehicleClientType>);
+    static_assert(std::is_base_of_v<IVehicleServer, VehicleServerType>);
+
+  public:
+    std::vector<VehiclePropConfig> getAllPropertyConfig() const override {
+        return this->onGetAllPropertyConfig();
+    }
+
+    StatusCode setProperty(const VehiclePropValue& value) override {
+        return this->onSetProperty(value);
+    }
+
+    void onPropertyValueFromCar(const VehiclePropValue& value) override {
+        return this->onPropertyValue(value);
+    }
+
+    // To be implemented:
+    // virtual std::vector<VehiclePropConfig> onGetAllPropertyConfig() = 0;
+    // virtual void onPropertyValue(const VehiclePropValue& value) = 0;
+    // virtual StatusCode onSetProperty(const VehiclePropValue& value) = 0;
+};
+
+}  // namespace V2_0
+}  // namespace vehicle
+}  // namespace automotive
+}  // namespace hardware
+}  // namespace android
+
+#endif  // android_hardware_automotive_vehicle_V2_0_VehicleConnector_H_
diff --git a/automotive/vehicle/2.0/default/impl/vhal_v2_0/SocketComm.cpp b/automotive/vehicle/2.0/default/impl/vhal_v2_0/SocketComm.cpp
index 9eb8894..068333c 100644
--- a/automotive/vehicle/2.0/default/impl/vhal_v2_0/SocketComm.cpp
+++ b/automotive/vehicle/2.0/default/impl/vhal_v2_0/SocketComm.cpp
@@ -92,7 +92,10 @@
     }
 
     ALOGI("%s: Listening for connections on port %d", __FUNCTION__, DEBUG_SOCKET);
-    ::listen(mListenFd, 1);
+    if (::listen(mListenFd, 1) == -1) {
+        ALOGE("%s: Error on listening: errno: %d: %s", __FUNCTION__, errno, strerror(errno));
+        return false;
+    }
     return true;
 }
 
diff --git a/configstore/1.1/default/seccomp_policy/configstore@1.1-arm64.policy b/configstore/1.1/default/seccomp_policy/configstore@1.1-arm64.policy
index 937fddd..a609620 100644
--- a/configstore/1.1/default/seccomp_policy/configstore@1.1-arm64.policy
+++ b/configstore/1.1/default/seccomp_policy/configstore@1.1-arm64.policy
@@ -45,6 +45,7 @@
 getdents64: 1
 clock_gettime: 1
 getpid: 1
+gettid: 1
 
 # used during process crash by crash_dump to dump process info
 rt_sigprocmask: 1
diff --git a/current.txt b/current.txt
index 74586b7..dc2e6b0 100644
--- a/current.txt
+++ b/current.txt
@@ -594,17 +594,20 @@
 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
 3e01d4446cd69fd1c48f8572efd97487bc179564b32bd795800b97bbe10be37b android.hardware.wifi@1.4::IWifi
 a64467bae843569f0d465c5be7f0c7a5b987985b55a3ef4794dd5afc68538650 android.hardware.wifi.supplicant@1.3::ISupplicant
 44445b8a03d7b9e68b2fbd954672c18a8fce9e32851b0692f4f4ab3407f86ecb android.hardware.wifi.supplicant@1.3::ISupplicantStaIface
 619fc9839ec6e369cfa9b28e3e9412e6885720ff8f9b5750c1b6ffb905120391 android.hardware.wifi.supplicant@1.3::ISupplicantStaIfaceCallback
 c9273429fcf98d797d3bb07fdba6f1be95bf960f9255cde169fd1ca4db85f856 android.hardware.wifi.supplicant@1.3::ISupplicantStaNetwork
 9b0a3ab6f4f74b971ed094426d8a443e29b512ff03e1ab50c07156396cdb2483 android.hardware.wifi.supplicant@1.3::types
-41c602462ccd1b19cfd645994be4de4c07fc197ff58a54e84476b31908e61e21 android.hardware.radio@1.5::types
-a8691c71747c3f14f7a043598e856425077f755e55990507a9132ad62f8ab3f7 android.hardware.radio@1.5::IRadio
+d3636ff9d5fef59f59f678887209156b2608d29f676fb1e600fe33b7e57a8a61 android.hardware.radio@1.5::types
+c8e81d912827a5d49b2ddcdc4eb4556c5d231a899a1dca879309e04210daa4a0 android.hardware.radio@1.5::IRadio
 a62a93faf173b14a6175b683ebf61ffa568dc61f81e369d2dce7b1265e86cf2f android.hardware.radio@1.5::IRadioIndication
-15daf260aaf6781b911450bc94e1a164901f9c0fe0bda68f8434f0a903f66e05 android.hardware.radio@1.5::IRadioResponse
+260ce05806d753d728f844d405e832179ed7d9b65986ec18fef3d21cf7285587 android.hardware.radio@1.5::IRadioResponse
diff --git a/health/storage/1.0/vts/functional/Android.bp b/health/storage/1.0/vts/functional/Android.bp
index 87502f8..a30cdde 100644
--- a/health/storage/1.0/vts/functional/Android.bp
+++ b/health/storage/1.0/vts/functional/Android.bp
@@ -22,6 +22,6 @@
     shared_libs: [
         "libhidlbase",
     ],
-    test_suites: ["general-tests"],
+    test_suites: ["general-tests", "vts-core"],
 }
 
diff --git a/health/storage/1.0/vts/functional/VtsHalHealthStorageV1_0TargetTest.cpp b/health/storage/1.0/vts/functional/VtsHalHealthStorageV1_0TargetTest.cpp
index 2365124..eaa44ec 100644
--- a/health/storage/1.0/vts/functional/VtsHalHealthStorageV1_0TargetTest.cpp
+++ b/health/storage/1.0/vts/functional/VtsHalHealthStorageV1_0TargetTest.cpp
@@ -14,11 +14,12 @@
  * limitations under the License.
  */
 
-#include <VtsHalHidlTargetTestBase.h>
-#include <VtsHalHidlTargetTestEnvBase.h>
 #include <android-base/logging.h>
 #include <android/hardware/health/storage/1.0/IStorage.h>
+#include <gtest/gtest.h>
+#include <hidl/GtestPrinter.h>
 #include <hidl/HidlTransportSupport.h>
+#include <hidl/ServiceManagement.h>
 #include <unistd.h>
 #include <thread>
 
@@ -101,25 +102,10 @@
     Result mResult{Result::UNKNOWN_ERROR};
 };
 
-/** Test environment for Health Storage HIDL HAL. */
-class HealthStorageHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
-   public:
-    /** get the test environment singleton */
-    static HealthStorageHidlEnvironment* Instance() {
-        static HealthStorageHidlEnvironment* instance = new HealthStorageHidlEnvironment();
-        return instance;
-    }
-    virtual void registerTestServices() override { registerTestService<IStorage>(); }
-
-   private:
-    HealthStorageHidlEnvironment() {}
-};
-
-class HealthStorageHidlTest : public ::testing::VtsHalHidlTargetTestBase {
+class HealthStorageHidlTest : public ::testing::TestWithParam<std::string> {
    public:
     virtual void SetUp() override {
-        fs = ::testing::VtsHalHidlTargetTestBase::getService<IStorage>(
-            HealthStorageHidlEnvironment::Instance()->getServiceName<IStorage>());
+        fs = IStorage::getService(GetParam());
 
         ASSERT_NE(fs, nullptr);
         LOG(INFO) << "Service is remote " << fs->isRemote();
@@ -153,7 +139,7 @@
 /**
  * Ensure garbage collection works on null callback.
  */
-TEST_F(HealthStorageHidlTest, GcNullCallback) {
+TEST_P(HealthStorageHidlTest, GcNullCallback) {
     auto ret = fs->garbageCollect(kDevGcTimeoutSec, nullptr);
 
     ASSERT_OK(ret);
@@ -167,28 +153,20 @@
 /**
  * Ensure garbage collection works on non-null callback.
  */
-TEST_F(HealthStorageHidlTest, GcNonNullCallback) {
+TEST_P(HealthStorageHidlTest, GcNonNullCallback) {
     sp<GcCallback> cb = new GcCallback();
     auto ret = fs->garbageCollect(kDevGcTimeoutSec, cb);
     ASSERT_OK(ret);
     cb->waitForResult(kDevGcTimeout + kDevGcTolerance + kRpcTime, Result::SUCCESS);
 }
 
+INSTANTIATE_TEST_SUITE_P(
+        PerInstance, HealthStorageHidlTest,
+        testing::ValuesIn(android::hardware::getAllHalInstanceNames(IStorage::descriptor)),
+        android::hardware::PrintInstanceNameToString);
+
 }  // namespace V1_0
 }  // namespace storage
 }  // namespace health
 }  // namespace hardware
 }  // namespace android
-
-int main(int argc, char** argv) {
-    using ::android::hardware::configureRpcThreadpool;
-    using ::android::hardware::health::storage::V1_0::HealthStorageHidlEnvironment;
-
-    configureRpcThreadpool(1, false /* callerWillJoin*/);
-    ::testing::AddGlobalTestEnvironment(HealthStorageHidlEnvironment::Instance());
-    ::testing::InitGoogleTest(&argc, argv);
-    HealthStorageHidlEnvironment::Instance()->init(&argc, argv);
-    int status = RUN_ALL_TESTS();
-    LOG(INFO) << "Test result = " << status;
-    return status;
-}
diff --git a/keymaster/4.0/vts/functional/keymaster_hidl_hal_test.cpp b/keymaster/4.0/vts/functional/keymaster_hidl_hal_test.cpp
index a7cbb36..0e12283 100644
--- a/keymaster/4.0/vts/functional/keymaster_hidl_hal_test.cpp
+++ b/keymaster/4.0/vts/functional/keymaster_hidl_hal_test.cpp
@@ -4338,25 +4338,29 @@
  * to specify how many following bytes will be used to encode the length.
  */
 TEST_F(AttestationTest, AttestationApplicationIDLengthProperlyEncoded) {
-    auto creation_time = std::chrono::system_clock::now();
-    ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
-                                                 .Authorization(TAG_NO_AUTH_REQUIRED)
-                                                 .EcdsaSigningKey(EcCurve::P_256)
-                                                 .Digest(Digest::SHA_2_256)));
+    std::vector<uint32_t> app_id_lengths{143, 258};
+    for (uint32_t length : app_id_lengths) {
+        auto creation_time = std::chrono::system_clock::now();
+        ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+                                                     .Authorization(TAG_NO_AUTH_REQUIRED)
+                                                     .EcdsaSigningKey(EcCurve::P_256)
+                                                     .Digest(Digest::SHA_2_256)));
 
-    hidl_vec<hidl_vec<uint8_t>> cert_chain;
-    const string app_id(143, 'a');
-    ASSERT_EQ(ErrorCode::OK,
-              AttestKey(AuthorizationSetBuilder()
-                                .Authorization(TAG_ATTESTATION_CHALLENGE, HidlBuf("challenge"))
-                                .Authorization(TAG_ATTESTATION_APPLICATION_ID, HidlBuf(app_id)),
-                        &cert_chain));
-    EXPECT_GE(cert_chain.size(), 2U);
+        hidl_vec<hidl_vec<uint8_t>> cert_chain;
+        const string app_id(length, 'a');
+        ASSERT_EQ(ErrorCode::OK,
+                  AttestKey(AuthorizationSetBuilder()
+                                    .Authorization(TAG_ATTESTATION_CHALLENGE, HidlBuf("challenge"))
+                                    .Authorization(TAG_ATTESTATION_APPLICATION_ID, HidlBuf(app_id)),
+                            &cert_chain));
+        EXPECT_GE(cert_chain.size(), 2U);
 
-    EXPECT_TRUE(verify_attestation_record("challenge", app_id,                    //
-                                          key_characteristics_.softwareEnforced,  //
-                                          key_characteristics_.hardwareEnforced,  //
-                                          SecLevel(), cert_chain[0], creation_time));
+        EXPECT_TRUE(verify_attestation_record("challenge", app_id,                    //
+                                              key_characteristics_.softwareEnforced,  //
+                                              key_characteristics_.hardwareEnforced,  //
+                                              SecLevel(), cert_chain[0], creation_time));
+        CheckedDeleteKey();
+    }
 }
 /*
  * AttestationTest.AesAttestation
@@ -4600,6 +4604,57 @@
     }
 }
 
+typedef KeymasterHidlTest TransportLimitTest;
+
+/*
+ * TransportLimitTest.LargeFinishInput
+ *
+ * Verifies that passing large input data to finish either succeeds or fails as expected.
+ */
+TEST_F(TransportLimitTest, LargeFinishInput) {
+    ASSERT_EQ(ErrorCode::OK, GenerateKey(AuthorizationSetBuilder()
+                                                 .Authorization(TAG_NO_AUTH_REQUIRED)
+                                                 .AesEncryptionKey(128)
+                                                 .BlockMode(BlockMode::ECB)
+                                                 .Padding(PaddingMode::NONE)));
+
+    for (int msg_size = 10 /*1KB*/; msg_size <= 17 /*128KB*/; msg_size++) {
+        auto cipher_params =
+                AuthorizationSetBuilder().BlockMode(BlockMode::ECB).Padding(PaddingMode::NONE);
+
+        AuthorizationSet out_params;
+        EXPECT_EQ(ErrorCode::OK, Begin(KeyPurpose::ENCRYPT, cipher_params, &out_params));
+
+        string plain_message = std::string(1 << msg_size, 'x');
+        string encrypted_message;
+        auto rc = Finish(plain_message, &encrypted_message);
+
+        if (rc == ErrorCode::OK) {
+            EXPECT_EQ(plain_message.size(), encrypted_message.size())
+                    << "Encrypt finish returned OK, but did not consume all of the given input";
+        } else {
+            EXPECT_EQ(ErrorCode::INVALID_INPUT_LENGTH, rc)
+                    << "Encrypt finish failed in an unexpected way when given a large input";
+            continue;
+        }
+        cipher_params.push_back(out_params);
+
+        EXPECT_EQ(ErrorCode::OK, Begin(KeyPurpose::DECRYPT, cipher_params));
+
+        string decrypted_message;
+        rc = Finish(encrypted_message, &decrypted_message);
+
+        if (rc == ErrorCode::OK) {
+            EXPECT_EQ(plain_message.size(), decrypted_message.size())
+                    << "Decrypt finish returned OK, did not consume all of the given input";
+        } else {
+            EXPECT_EQ(ErrorCode::INVALID_INPUT_LENGTH, rc)
+                    << "Encrypt finish failed in an unexpected way when given a large input";
+        }
+    }
+
+    CheckedDeleteKey();
+}
 
 }  // namespace test
 }  // namespace V4_0
diff --git a/neuralnetworks/1.2/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.2/vts/functional/GeneratedTestHarness.cpp
index aacb385..c1bf494 100644
--- a/neuralnetworks/1.2/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.2/vts/functional/GeneratedTestHarness.cpp
@@ -58,8 +58,20 @@
 using V1_1::ExecutionPreference;
 using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
 
+namespace {
+
+enum class Executor { ASYNC, SYNC, BURST };
+
 enum class OutputType { FULLY_SPECIFIED, UNSPECIFIED, INSUFFICIENT };
 
+struct TestConfig {
+    Executor executor;
+    MeasureTiming measureTiming;
+    OutputType outputType;
+};
+
+}  // namespace
+
 Model createModel(const TestModel& testModel) {
     // Model operands.
     hidl_vec<Operand> operands(testModel.operands.size());
@@ -194,31 +206,31 @@
     return android::nn::ExecutionBurstController::create(preparedModel,
                                                          std::chrono::microseconds{0});
 }
-enum class Executor { ASYNC, SYNC, BURST };
 
 void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestModel& testModel,
-                           Executor executor, MeasureTiming measure, OutputType outputType) {
+                           const TestConfig& testConfig) {
     // If output0 does not have size larger than one byte, we can not test with insufficient buffer.
-    if (outputType == OutputType::INSUFFICIENT && !isOutputSizeGreaterThanOne(testModel, 0)) {
+    if (testConfig.outputType == OutputType::INSUFFICIENT &&
+        !isOutputSizeGreaterThanOne(testModel, 0)) {
         return;
     }
 
     Request request = createRequest(testModel);
-    if (outputType == OutputType::INSUFFICIENT) {
+    if (testConfig.outputType == OutputType::INSUFFICIENT) {
         makeOutputInsufficientSize(/*outputIndex=*/0, &request);
     }
 
     ErrorStatus executionStatus;
     hidl_vec<OutputShape> outputShapes;
     Timing timing;
-    switch (executor) {
+    switch (testConfig.executor) {
         case Executor::ASYNC: {
             SCOPED_TRACE("asynchronous");
 
             // launch execution
             sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-            Return<ErrorStatus> executionLaunchStatus =
-                    ExecutePreparedModel(preparedModel, request, measure, executionCallback);
+            Return<ErrorStatus> executionLaunchStatus = ExecutePreparedModel(
+                    preparedModel, request, testConfig.measureTiming, executionCallback);
             ASSERT_TRUE(executionLaunchStatus.isOk());
             EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executionLaunchStatus));
 
@@ -234,8 +246,8 @@
             SCOPED_TRACE("synchronous");
 
             // execute
-            Return<ErrorStatus> executionReturnStatus =
-                    ExecutePreparedModel(preparedModel, request, measure, &outputShapes, &timing);
+            Return<ErrorStatus> executionReturnStatus = ExecutePreparedModel(
+                    preparedModel, request, testConfig.measureTiming, &outputShapes, &timing);
             ASSERT_TRUE(executionReturnStatus.isOk());
             executionStatus = static_cast<ErrorStatus>(executionReturnStatus);
 
@@ -258,14 +270,14 @@
             // execute burst
             int n;
             std::tie(n, outputShapes, timing, std::ignore) =
-                    controller->compute(request, measure, keys);
+                    controller->compute(request, testConfig.measureTiming, keys);
             executionStatus = nn::convertResultCodeToErrorStatus(n);
 
             break;
         }
     }
 
-    if (outputType != OutputType::FULLY_SPECIFIED &&
+    if (testConfig.outputType != OutputType::FULLY_SPECIFIED &&
         executionStatus == ErrorStatus::GENERAL_FAILURE) {
         LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
                      "execute model that it does not support.";
@@ -274,7 +286,7 @@
                   << std::endl;
         GTEST_SKIP();
     }
-    if (measure == MeasureTiming::NO) {
+    if (testConfig.measureTiming == MeasureTiming::NO) {
         EXPECT_EQ(UINT64_MAX, timing.timeOnDevice);
         EXPECT_EQ(UINT64_MAX, timing.timeInDriver);
     } else {
@@ -283,7 +295,7 @@
         }
     }
 
-    switch (outputType) {
+    switch (testConfig.outputType) {
         case OutputType::FULLY_SPECIFIED:
             // If the model output operands are fully specified, outputShapes must be either
             // either empty, or have the same number of elements as the number of outputs.
@@ -321,44 +333,29 @@
 
 void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestModel& testModel,
                            bool testDynamicOutputShape) {
+    std::initializer_list<OutputType> outputTypesList;
+    std::initializer_list<MeasureTiming> measureTimingList;
+    std::initializer_list<Executor> executorList;
+
     if (testDynamicOutputShape) {
-        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
-                              OutputType::UNSPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
-                              OutputType::UNSPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
-                              OutputType::UNSPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
-                              OutputType::UNSPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
-                              OutputType::UNSPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
-                              OutputType::UNSPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
-                              OutputType::INSUFFICIENT);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
-                              OutputType::INSUFFICIENT);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
-                              OutputType::INSUFFICIENT);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
-                              OutputType::INSUFFICIENT);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
-                              OutputType::INSUFFICIENT);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
-                              OutputType::INSUFFICIENT);
+        outputTypesList = {OutputType::UNSPECIFIED, OutputType::INSUFFICIENT};
+        measureTimingList = {MeasureTiming::NO, MeasureTiming::YES};
+        executorList = {Executor::ASYNC, Executor::SYNC, Executor::BURST};
     } else {
-        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
-                              OutputType::FULLY_SPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
-                              OutputType::FULLY_SPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
-                              OutputType::FULLY_SPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
-                              OutputType::FULLY_SPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
-                              OutputType::FULLY_SPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
-                              OutputType::FULLY_SPECIFIED);
+        outputTypesList = {OutputType::FULLY_SPECIFIED};
+        measureTimingList = {MeasureTiming::NO, MeasureTiming::YES};
+        executorList = {Executor::ASYNC, Executor::SYNC, Executor::BURST};
+    }
+
+    for (const OutputType outputType : outputTypesList) {
+        for (const MeasureTiming measureTiming : measureTimingList) {
+            for (const Executor executor : executorList) {
+                const TestConfig testConfig = {.executor = executor,
+                                               .measureTiming = measureTiming,
+                                               .outputType = outputType};
+                EvaluatePreparedModel(preparedModel, testModel, testConfig);
+            }
+        }
     }
 }
 
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..2ec2988 100644
--- a/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.3/vts/functional/GeneratedTestHarness.cpp
@@ -63,15 +63,26 @@
 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;
 using V1_2::implementation::ExecutionCallback;
 using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
 
+namespace {
+
+enum class Executor { ASYNC, SYNC, BURST };
+
 enum class OutputType { FULLY_SPECIFIED, UNSPECIFIED, INSUFFICIENT };
 
+struct TestConfig {
+    Executor executor;
+    MeasureTiming measureTiming;
+    OutputType outputType;
+};
+
+}  // namespace
+
 Model createModel(const TestModel& testModel) {
     // Model operands.
     hidl_vec<Operand> operands(testModel.operands.size());
@@ -206,31 +217,31 @@
     return android::nn::ExecutionBurstController::create(preparedModel,
                                                          std::chrono::microseconds{0});
 }
-enum class Executor { ASYNC, SYNC, BURST };
 
 void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestModel& testModel,
-                           Executor executor, MeasureTiming measure, OutputType outputType) {
+                           const TestConfig& testConfig) {
     // If output0 does not have size larger than one byte, we can not test with insufficient buffer.
-    if (outputType == OutputType::INSUFFICIENT && !isOutputSizeGreaterThanOne(testModel, 0)) {
+    if (testConfig.outputType == OutputType::INSUFFICIENT &&
+        !isOutputSizeGreaterThanOne(testModel, 0)) {
         return;
     }
 
     Request request = createRequest(testModel);
-    if (outputType == OutputType::INSUFFICIENT) {
+    if (testConfig.outputType == OutputType::INSUFFICIENT) {
         makeOutputInsufficientSize(/*outputIndex=*/0, &request);
     }
 
     ErrorStatus executionStatus;
     hidl_vec<OutputShape> outputShapes;
     Timing timing;
-    switch (executor) {
+    switch (testConfig.executor) {
         case Executor::ASYNC: {
             SCOPED_TRACE("asynchronous");
 
             // launch execution
             sp<ExecutionCallback> executionCallback = new ExecutionCallback();
-            Return<ErrorStatus> executionLaunchStatus =
-                    ExecutePreparedModel(preparedModel, request, measure, executionCallback);
+            Return<ErrorStatus> executionLaunchStatus = ExecutePreparedModel(
+                    preparedModel, request, testConfig.measureTiming, executionCallback);
             ASSERT_TRUE(executionLaunchStatus.isOk());
             EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executionLaunchStatus));
 
@@ -246,8 +257,8 @@
             SCOPED_TRACE("synchronous");
 
             // execute
-            Return<ErrorStatus> executionReturnStatus =
-                    ExecutePreparedModel(preparedModel, request, measure, &outputShapes, &timing);
+            Return<ErrorStatus> executionReturnStatus = ExecutePreparedModel(
+                    preparedModel, request, testConfig.measureTiming, &outputShapes, &timing);
             ASSERT_TRUE(executionReturnStatus.isOk());
             executionStatus = static_cast<ErrorStatus>(executionReturnStatus);
 
@@ -270,14 +281,14 @@
             // execute burst
             int n;
             std::tie(n, outputShapes, timing, std::ignore) =
-                    controller->compute(request, measure, keys);
+                    controller->compute(request, testConfig.measureTiming, keys);
             executionStatus = nn::convertResultCodeToErrorStatus(n);
 
             break;
         }
     }
 
-    if (outputType != OutputType::FULLY_SPECIFIED &&
+    if (testConfig.outputType != OutputType::FULLY_SPECIFIED &&
         executionStatus == ErrorStatus::GENERAL_FAILURE) {
         LOG(INFO) << "NN VTS: Early termination of test because vendor service cannot "
                      "execute model that it does not support.";
@@ -286,7 +297,7 @@
                   << std::endl;
         GTEST_SKIP();
     }
-    if (measure == MeasureTiming::NO) {
+    if (testConfig.measureTiming == MeasureTiming::NO) {
         EXPECT_EQ(UINT64_MAX, timing.timeOnDevice);
         EXPECT_EQ(UINT64_MAX, timing.timeInDriver);
     } else {
@@ -295,7 +306,7 @@
         }
     }
 
-    switch (outputType) {
+    switch (testConfig.outputType) {
         case OutputType::FULLY_SPECIFIED:
             // If the model output operands are fully specified, outputShapes must be either
             // either empty, or have the same number of elements as the number of outputs.
@@ -333,44 +344,29 @@
 
 void EvaluatePreparedModel(const sp<IPreparedModel>& preparedModel, const TestModel& testModel,
                            bool testDynamicOutputShape) {
+    std::initializer_list<OutputType> outputTypesList;
+    std::initializer_list<MeasureTiming> measureTimingList;
+    std::initializer_list<Executor> executorList;
+
     if (testDynamicOutputShape) {
-        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
-                              OutputType::UNSPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
-                              OutputType::UNSPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
-                              OutputType::UNSPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
-                              OutputType::UNSPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
-                              OutputType::UNSPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
-                              OutputType::UNSPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
-                              OutputType::INSUFFICIENT);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
-                              OutputType::INSUFFICIENT);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
-                              OutputType::INSUFFICIENT);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
-                              OutputType::INSUFFICIENT);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
-                              OutputType::INSUFFICIENT);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
-                              OutputType::INSUFFICIENT);
+        outputTypesList = {OutputType::UNSPECIFIED, OutputType::INSUFFICIENT};
+        measureTimingList = {MeasureTiming::NO, MeasureTiming::YES};
+        executorList = {Executor::ASYNC, Executor::SYNC, Executor::BURST};
     } else {
-        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::NO,
-                              OutputType::FULLY_SPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::NO,
-                              OutputType::FULLY_SPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::NO,
-                              OutputType::FULLY_SPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::ASYNC, MeasureTiming::YES,
-                              OutputType::FULLY_SPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::SYNC, MeasureTiming::YES,
-                              OutputType::FULLY_SPECIFIED);
-        EvaluatePreparedModel(preparedModel, testModel, Executor::BURST, MeasureTiming::YES,
-                              OutputType::FULLY_SPECIFIED);
+        outputTypesList = {OutputType::FULLY_SPECIFIED};
+        measureTimingList = {MeasureTiming::NO, MeasureTiming::YES};
+        executorList = {Executor::ASYNC, Executor::SYNC, Executor::BURST};
+    }
+
+    for (const OutputType outputType : outputTypesList) {
+        for (const MeasureTiming measureTiming : measureTimingList) {
+            for (const Executor executor : executorList) {
+                const TestConfig testConfig = {.executor = executor,
+                                               .measureTiming = measureTiming,
+                                               .outputType = outputType};
+                EvaluatePreparedModel(preparedModel, testModel, testConfig);
+            }
+        }
     }
 }
 
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/1.5/IRadio.hal b/radio/1.5/IRadio.hal
index de20dd0..74ec56d 100644
--- a/radio/1.5/IRadio.hal
+++ b/radio/1.5/IRadio.hal
@@ -17,6 +17,8 @@
 package android.hardware.radio@1.5;
 
 import @1.4::IRadio;
+import @1.5::AccessNetwork;
+import @1.5::SignalThresholdInfo;
 
 /**
  * This interface is used by telephony and telecom to talk to cellular radio.
@@ -27,4 +29,30 @@
  * setResponseFunctions must work with @1.5::IRadioResponse and @1.5::IRadioIndication.
  */
 interface IRadio extends @1.4::IRadio {
+
+    /**
+     * Sets the signal strength reporting criteria.
+     *
+     * The resulting reporting rules are the AND of all the supplied criteria. For each RAN
+     * The hysteresisDb and thresholds apply to only the following measured quantities:
+     * -GERAN    - RSSI
+     * -CDMA2000 - RSSI
+     * -UTRAN    - RSCP
+     * -EUTRAN   - RSRP/RSRQ/RSSNR
+     * -NGRAN    - SSRSRP/SSRSRQ/SSSINR
+     *
+     * Note: Reporting criteria must be individually set for each RAN. For any unset reporting
+     * criteria, the value is implementation-defined.
+     *
+     * Response callback is
+     * IRadioResponse.setSignalStrengthReportingCriteriaResponse_1_5()
+     *
+     * @param serial Serial number of request.
+     * @param signalThresholdInfo Signal threshold info including the threshold values,
+     *                            hysteresisDb, and hysteresisMs. See @1.5::SignalThresholdInfo
+     *                            for details.
+     * @param accessNetwork The type of network for which to apply these thresholds.
+     */
+    oneway setSignalStrengthReportingCriteria_1_5(int32_t serial,
+            SignalThresholdInfo signalThresholdInfo, AccessNetwork accessNetwork);
 };
diff --git a/radio/1.5/IRadioResponse.hal b/radio/1.5/IRadioResponse.hal
index d4c4f76..91dc1e0 100644
--- a/radio/1.5/IRadioResponse.hal
+++ b/radio/1.5/IRadioResponse.hal
@@ -23,4 +23,13 @@
  * Interface declaring response functions to solicited radio requests.
  */
 interface IRadioResponse extends @1.4::IRadioResponse {
+    /**
+     * @param info Response info struct containing response type, serial no. and error
+     *
+     * Valid errors returned:
+     *   RadioError:NONE
+     *   RadioError:INVALID_ARGUMENTS
+     *   RadioError:RADIO_NOT_AVAILABLE
+     */
+    oneway setSignalStrengthReportingCriteriaResponse_1_5(RadioResponseInfo info);
 };
diff --git a/radio/1.5/types.hal b/radio/1.5/types.hal
index a639a8d..216ca1f 100644
--- a/radio/1.5/types.hal
+++ b/radio/1.5/types.hal
@@ -15,3 +15,100 @@
  */
 
 package android.hardware.radio@1.5;
+
+import @1.4::AccessNetwork;
+
+/**
+ * Defining signal strength type.
+ */
+enum SignalMeasurementType : int32_t {
+    /**
+     * Received Signal Strength Indication.
+     * Range: -113 dBm and -51 dBm
+     * Used RAN: GERAN, CDMA2000
+     * Reference: 3GPP TS 27.007 section 8.5.
+     */
+    RSSI = 1,
+    /**
+     * Received Signal Code Power.
+     * Range: -120 dBm to -25 dBm;
+     * Used RAN: UTRAN
+     * Reference: 3GPP TS 25.123, section 9.1.1.1
+     */
+    RSCP = 2,
+    /**
+     * Reference Signal Received Power.
+     * Range: -140 dBm to -44 dBm;
+     * Used RAN: EUTRAN
+     * Reference: 3GPP TS 36.133 9.1.4
+     */
+    RSRP = 3,
+    /**
+     * Reference Signal Received Quality
+     * Range: -20 dB to -3 dB;
+     * Used RAN: EUTRAN
+     * Reference: 3GPP TS 36.133 9.1.7
+     */
+    RSRQ = 4,
+    /**
+     * Reference Signal Signal to Noise Ratio
+     * Range: -20 dB to -30 dB;
+     * Used RAN: EUTRAN
+     * Reference: 3GPP TS 36.101 8.1.1
+     */
+    RSSNR = 5,
+    /**
+     * 5G SS reference signal received power.
+     * Range: -140 dBm to -44 dBm.
+     * Used RAN: NGRAN
+     * Reference: 3GPP TS 38.215.
+     */
+    SSRSRP = 6,
+    /**
+     * 5G SS reference signal received quality.
+     * Range: -20 dB to -3 dB.
+     * Used RAN: NGRAN
+     * Reference: 3GPP TS 38.215.
+     */
+    SSRSRQ = 7,
+    /**
+     * 5G SS signal-to-noise and interference ratio.
+     * Range: -23 dB to 40 dB
+     * Used RAN: NGRAN
+     * Reference: 3GPP TS 38.215 section 5.1.*, 3GPP TS 38.133 section 10.1.16.1.
+     */
+    SSSINR = 8,
+};
+
+/**
+ * Contains the threshold values of each signal measurement type.
+ */
+struct SignalThresholdInfo {
+    /** Signal Measurement Type */
+    SignalMeasurementType signalMeasurement;
+
+    /** A hysteresis time in milliseconds to prevent flapping. A value of 0 disables hysteresis */
+    int32_t hysteresisMs;
+
+    /**
+     * An interval in dB defining the required magnitude change between reports.
+     * hysteresisDb must be smaller than the smallest threshold delta.
+     * An interval value of 0 disables hysteresis.
+     */
+    int32_t hysteresisDb;
+
+    /**
+     * List of threshold values.
+     * Range and unit must reference specific @1.5::SignalMeasurementType.
+     * The threshold values for which to apply criteria.
+     * A vector size of 0 disables the use of thresholds for reporting.
+     */
+    vec<int32_t> thresholds;
+};
+
+enum AccessNetwork : @1.4::AccessNetwork {
+    /**
+     *  Next-Generation Radio Access Network (NGRAN)
+     */
+    NGRAN = 6,
+};
\ No newline at end of file
diff --git a/radio/1.5/vts/functional/radio_hidl_hal_api.cpp b/radio/1.5/vts/functional/radio_hidl_hal_api.cpp
index b86fa5f..650ede4 100644
--- a/radio/1.5/vts/functional/radio_hidl_hal_api.cpp
+++ b/radio/1.5/vts/functional/radio_hidl_hal_api.cpp
@@ -17,3 +17,266 @@
 #include <radio_hidl_hal_utils_v1_5.h>
 
 #define ASSERT_OK(ret) ASSERT_TRUE(ret.isOk())
+
+/*
+ * Test IRadio.setSignalStrengthReportingCriteria_1_5() with invalid hysteresisDb
+ */
+TEST_F(RadioHidlTest_v1_5, setSignalStrengthReportingCriteria_1_5_invalidHysteresisDb) {
+    serial = GetRandomSerialNumber();
+
+    ::android::hardware::radio::V1_5::SignalThresholdInfo signalThresholdInfo;
+    signalThresholdInfo.signalMeasurement = SignalMeasurementType::RSSI;
+    signalThresholdInfo.hysteresisMs = 5000;
+    signalThresholdInfo.hysteresisDb = 10;  // hysteresisDb too large given threshold list deltas
+    signalThresholdInfo.thresholds = {-109, -103, -97, -89};
+
+    Return<void> res = radio_v1_5->setSignalStrengthReportingCriteria_1_5(
+            serial, signalThresholdInfo, ::android::hardware::radio::V1_5::AccessNetwork::GERAN);
+    ASSERT_OK(res);
+    EXPECT_EQ(std::cv_status::no_timeout, wait());
+    EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_5->rspInfo.type);
+    EXPECT_EQ(serial, radioRsp_v1_5->rspInfo.serial);
+
+    ALOGI("setSignalStrengthReportingCriteria_1_5_invalidHysteresisDb, rspInfo.error = %s\n",
+          toString(radioRsp_v1_5->rspInfo.error).c_str());
+    ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_5->rspInfo.error, {RadioError::INVALID_ARGUMENTS}));
+}
+
+/*
+ * Test IRadio.setSignalStrengthReportingCriteria_1_5() with empty thresholds
+ */
+TEST_F(RadioHidlTest_v1_5, setSignalStrengthReportingCriteria_1_5_EmptyThresholds) {
+    serial = GetRandomSerialNumber();
+
+    ::android::hardware::radio::V1_5::SignalThresholdInfo signalThresholdInfo;
+    signalThresholdInfo.signalMeasurement = SignalMeasurementType::RSSI;
+    signalThresholdInfo.hysteresisMs = 0;
+    signalThresholdInfo.hysteresisDb = 0;
+
+    Return<void> res = radio_v1_5->setSignalStrengthReportingCriteria_1_5(
+            serial, signalThresholdInfo, ::android::hardware::radio::V1_5::AccessNetwork::GERAN);
+    ASSERT_OK(res);
+    EXPECT_EQ(std::cv_status::no_timeout, wait());
+    EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_5->rspInfo.type);
+    EXPECT_EQ(serial, radioRsp_v1_5->rspInfo.serial);
+
+    ALOGI("setSignalStrengthReportingCriteria_1_5_EmptyParams, rspInfo.error = %s\n",
+          toString(radioRsp_v1_5->rspInfo.error).c_str());
+    ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_5->rspInfo.error, {RadioError::NONE}));
+}
+
+/*
+ * Test IRadio.setSignalStrengthReportingCriteria_1_5() for GERAN
+ */
+TEST_F(RadioHidlTest_v1_5, setSignalStrengthReportingCriteria_1_5_Geran) {
+    serial = GetRandomSerialNumber();
+
+    ::android::hardware::radio::V1_5::SignalThresholdInfo signalThresholdInfo;
+    signalThresholdInfo.signalMeasurement = SignalMeasurementType::RSSI;
+    signalThresholdInfo.hysteresisMs = 5000;
+    signalThresholdInfo.hysteresisDb = 2;
+    signalThresholdInfo.thresholds = {-109, -103, -97, -89};
+
+    Return<void> res = radio_v1_5->setSignalStrengthReportingCriteria_1_5(
+            serial, signalThresholdInfo, ::android::hardware::radio::V1_5::AccessNetwork::GERAN);
+    ASSERT_OK(res);
+    EXPECT_EQ(std::cv_status::no_timeout, wait());
+    EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_5->rspInfo.type);
+    EXPECT_EQ(serial, radioRsp_v1_5->rspInfo.serial);
+
+    ALOGI("setSignalStrengthReportingCriteria_1_5_Geran, rspInfo.error = %s\n",
+          toString(radioRsp_v1_5->rspInfo.error).c_str());
+    ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_5->rspInfo.error, {RadioError::NONE}));
+}
+
+/*
+ * Test IRadio.setSignalStrengthReportingCriteria_1_5() for UTRAN
+ */
+TEST_F(RadioHidlTest_v1_5, setSignalStrengthReportingCriteria_1_5_Utran) {
+    serial = GetRandomSerialNumber();
+
+    ::android::hardware::radio::V1_5::SignalThresholdInfo signalThresholdInfo;
+    signalThresholdInfo.signalMeasurement = SignalMeasurementType::RSCP;
+    signalThresholdInfo.hysteresisMs = 5000;
+    signalThresholdInfo.hysteresisDb = 2;
+    signalThresholdInfo.thresholds = {-110, -97, -73, -49, -25};
+
+    Return<void> res = radio_v1_5->setSignalStrengthReportingCriteria_1_5(
+            serial, signalThresholdInfo, ::android::hardware::radio::V1_5::AccessNetwork::UTRAN);
+    ASSERT_OK(res);
+    EXPECT_EQ(std::cv_status::no_timeout, wait());
+    EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_5->rspInfo.type);
+    EXPECT_EQ(serial, radioRsp_v1_5->rspInfo.serial);
+
+    ALOGI("setSignalStrengthReportingCriteria_1_5_Utran, rspInfo.error = %s\n",
+          toString(radioRsp_v1_5->rspInfo.error).c_str());
+    ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_5->rspInfo.error, {RadioError::NONE}));
+}
+
+/*
+ * Test IRadio.setSignalStrengthReportingCriteria_1_5() for EUTRAN
+ */
+TEST_F(RadioHidlTest_v1_5, setSignalStrengthReportingCriteria_1_5_Eutran_RSRP) {
+    serial = GetRandomSerialNumber();
+
+    ::android::hardware::radio::V1_5::SignalThresholdInfo signalThresholdInfo;
+    signalThresholdInfo.signalMeasurement = SignalMeasurementType::RSRP;
+    signalThresholdInfo.hysteresisMs = 5000;
+    signalThresholdInfo.hysteresisDb = 2;
+    signalThresholdInfo.thresholds = {-128, -108, -88, -68};
+
+    Return<void> res = radio_v1_5->setSignalStrengthReportingCriteria_1_5(
+            serial, signalThresholdInfo, ::android::hardware::radio::V1_5::AccessNetwork::EUTRAN);
+    ASSERT_OK(res);
+    EXPECT_EQ(std::cv_status::no_timeout, wait());
+    EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_5->rspInfo.type);
+    EXPECT_EQ(serial, radioRsp_v1_5->rspInfo.serial);
+
+    ALOGI("setSignalStrengthReportingCriteria_1_5_Eutran, rspInfo.error = %s\n",
+          toString(radioRsp_v1_5->rspInfo.error).c_str());
+    ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_5->rspInfo.error, {RadioError::NONE}));
+}
+
+/*
+ * Test IRadio.setSignalStrengthReportingCriteria_1_5() for EUTRAN
+ */
+TEST_F(RadioHidlTest_v1_5, setSignalStrengthReportingCriteria_1_5_Eutran_RSRQ) {
+    serial = GetRandomSerialNumber();
+
+    ::android::hardware::radio::V1_5::SignalThresholdInfo signalThresholdInfo;
+    signalThresholdInfo.signalMeasurement = SignalMeasurementType::RSRQ;
+    signalThresholdInfo.hysteresisMs = 5000;
+    signalThresholdInfo.hysteresisDb = 2;
+    signalThresholdInfo.thresholds = {-27, -20, -13, -6};
+
+    Return<void> res = radio_v1_5->setSignalStrengthReportingCriteria_1_5(
+            serial, signalThresholdInfo, ::android::hardware::radio::V1_5::AccessNetwork::EUTRAN);
+    ASSERT_OK(res);
+    EXPECT_EQ(std::cv_status::no_timeout, wait());
+    EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_5->rspInfo.type);
+    EXPECT_EQ(serial, radioRsp_v1_5->rspInfo.serial);
+
+    ALOGI("setSignalStrengthReportingCriteria_1_5_Eutran, rspInfo.error = %s\n",
+          toString(radioRsp_v1_5->rspInfo.error).c_str());
+    ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_5->rspInfo.error, {RadioError::NONE}));
+}
+
+/*
+ * Test IRadio.setSignalStrengthReportingCriteria_1_5() for EUTRAN
+ */
+TEST_F(RadioHidlTest_v1_5, setSignalStrengthReportingCriteria_1_5_Eutran_RSSNR) {
+    serial = GetRandomSerialNumber();
+
+    ::android::hardware::radio::V1_5::SignalThresholdInfo signalThresholdInfo;
+    signalThresholdInfo.signalMeasurement = SignalMeasurementType::RSSNR;
+    signalThresholdInfo.hysteresisMs = 5000;
+    signalThresholdInfo.hysteresisDb = 2;
+    signalThresholdInfo.thresholds = {-10, 0, 10, 20};
+
+    Return<void> res = radio_v1_5->setSignalStrengthReportingCriteria_1_5(
+            serial, signalThresholdInfo, ::android::hardware::radio::V1_5::AccessNetwork::EUTRAN);
+    ASSERT_OK(res);
+    EXPECT_EQ(std::cv_status::no_timeout, wait());
+    EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_5->rspInfo.type);
+    EXPECT_EQ(serial, radioRsp_v1_5->rspInfo.serial);
+
+    ALOGI("setSignalStrengthReportingCriteria_1_5_Eutran, rspInfo.error = %s\n",
+          toString(radioRsp_v1_5->rspInfo.error).c_str());
+    ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_5->rspInfo.error, {RadioError::NONE}));
+}
+
+/*
+ * Test IRadio.setSignalStrengthReportingCriteria_1_5() for CDMA2000
+ */
+TEST_F(RadioHidlTest_v1_5, setSignalStrengthReportingCriteria_1_5_Cdma2000) {
+    serial = GetRandomSerialNumber();
+
+    ::android::hardware::radio::V1_5::SignalThresholdInfo signalThresholdInfo;
+    signalThresholdInfo.signalMeasurement = SignalMeasurementType::RSSI;
+    signalThresholdInfo.hysteresisMs = 5000;
+    signalThresholdInfo.hysteresisDb = 2;
+    signalThresholdInfo.thresholds = {-105, -90, -75, -65};
+
+    Return<void> res = radio_v1_5->setSignalStrengthReportingCriteria_1_5(
+            serial, signalThresholdInfo, ::android::hardware::radio::V1_5::AccessNetwork::CDMA2000);
+    ASSERT_OK(res);
+    EXPECT_EQ(std::cv_status::no_timeout, wait());
+    EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_5->rspInfo.type);
+    EXPECT_EQ(serial, radioRsp_v1_5->rspInfo.serial);
+
+    ALOGI("setSignalStrengthReportingCriteria_1_5_Cdma2000, rspInfo.error = %s\n",
+          toString(radioRsp_v1_5->rspInfo.error).c_str());
+    ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_5->rspInfo.error, {RadioError::NONE}));
+}
+
+/*
+ * Test IRadio.setSignalStrengthReportingCriteria_1_5() for NGRAN_SSRSRP
+ */
+TEST_F(RadioHidlTest_v1_5, setSignalStrengthReportingCriteria_1_5_NGRAN_SSRSRP) {
+    serial = GetRandomSerialNumber();
+
+    ::android::hardware::radio::V1_5::SignalThresholdInfo signalThresholdInfo;
+    signalThresholdInfo.signalMeasurement = SignalMeasurementType::SSRSRP;
+    signalThresholdInfo.hysteresisMs = 5000;
+    signalThresholdInfo.hysteresisDb = 0;
+    signalThresholdInfo.thresholds = {-105, -90, -75, -65};
+
+    Return<void> res = radio_v1_5->setSignalStrengthReportingCriteria_1_5(
+            serial, signalThresholdInfo, ::android::hardware::radio::V1_5::AccessNetwork::NGRAN);
+    ASSERT_OK(res);
+    EXPECT_EQ(std::cv_status::no_timeout, wait());
+    EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_5->rspInfo.type);
+    EXPECT_EQ(serial, radioRsp_v1_5->rspInfo.serial);
+
+    ALOGI("setSignalStrengthReportingCriteria_1_5_NGRAN_SSRSRP, rspInfo.error = %s\n",
+          toString(radioRsp_v1_5->rspInfo.error).c_str());
+    ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_5->rspInfo.error, {RadioError::NONE}));
+}
+
+/*
+ * Test IRadio.setSignalStrengthReportingCriteria_1_5() for NGRAN_SSRSRQ
+ */
+TEST_F(RadioHidlTest_v1_5, setSignalStrengthReportingCriteria_1_5_NGRAN_SSRSRQ) {
+    serial = GetRandomSerialNumber();
+
+    ::android::hardware::radio::V1_5::SignalThresholdInfo signalThresholdInfo;
+    signalThresholdInfo.signalMeasurement = SignalMeasurementType::SSRSRQ;
+    signalThresholdInfo.hysteresisMs = 5000;
+    signalThresholdInfo.hysteresisDb = 0;
+    signalThresholdInfo.thresholds = {-15, -10, -5, -4};
+
+    Return<void> res = radio_v1_5->setSignalStrengthReportingCriteria_1_5(
+            serial, signalThresholdInfo, ::android::hardware::radio::V1_5::AccessNetwork::NGRAN);
+    ASSERT_OK(res);
+    EXPECT_EQ(std::cv_status::no_timeout, wait());
+    EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_5->rspInfo.type);
+    EXPECT_EQ(serial, radioRsp_v1_5->rspInfo.serial);
+
+    ALOGI("setSignalStrengthReportingCriteria_1_5_NGRAN_SSRSRQ, rspInfo.error = %s\n",
+          toString(radioRsp_v1_5->rspInfo.error).c_str());
+    ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_5->rspInfo.error, {RadioError::NONE}));
+}
+
+/*
+ * Test IRadio.setSignalStrengthReportingCriteria_1_5() for NGRAN_SSSINR
+ */
+TEST_F(RadioHidlTest_v1_5, setSignalStrengthReportingCriteria_1_5_NGRAN_SSSINR) {
+    serial = GetRandomSerialNumber();
+
+    ::android::hardware::radio::V1_5::SignalThresholdInfo signalThresholdInfo;
+    signalThresholdInfo.signalMeasurement = SignalMeasurementType::SSSINR;
+    signalThresholdInfo.hysteresisMs = 5000;
+    signalThresholdInfo.hysteresisDb = 0;
+    signalThresholdInfo.thresholds = {-10, 3, 16, 18};
+
+    Return<void> res = radio_v1_5->setSignalStrengthReportingCriteria_1_5(
+            serial, signalThresholdInfo, ::android::hardware::radio::V1_5::AccessNetwork::NGRAN);
+    ASSERT_OK(res);
+    EXPECT_EQ(std::cv_status::no_timeout, wait());
+    EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_5->rspInfo.type);
+    EXPECT_EQ(serial, radioRsp_v1_5->rspInfo.serial);
+
+    ALOGI("setSignalStrengthReportingCriteria_1_5_NGRAN_SSSINR, rspInfo.error = %s\n",
+          toString(radioRsp_v1_5->rspInfo.error).c_str());
+    ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_5->rspInfo.error, {RadioError::NONE}));
+}
\ No newline at end of file
diff --git a/radio/1.5/vts/functional/radio_hidl_hal_utils_v1_5.h b/radio/1.5/vts/functional/radio_hidl_hal_utils_v1_5.h
index 799702b..683fdfc 100644
--- a/radio/1.5/vts/functional/radio_hidl_hal_utils_v1_5.h
+++ b/radio/1.5/vts/functional/radio_hidl_hal_utils_v1_5.h
@@ -521,6 +521,9 @@
     Return<void> getAllowedCarriersResponse_1_4(const RadioResponseInfo& info,
                                                 const CarrierRestrictionsWithPriority& carriers,
                                                 SimLockMultiSimPolicy multiSimPolicy);
+
+    /* 1.5 Api */
+    Return<void> setSignalStrengthReportingCriteriaResponse_1_5(const RadioResponseInfo& info);
 };
 
 /* Callback class for radio indication */
diff --git a/radio/1.5/vts/functional/radio_response.cpp b/radio/1.5/vts/functional/radio_response.cpp
index 1e5cc47..29a9250 100644
--- a/radio/1.5/vts/functional/radio_response.cpp
+++ b/radio/1.5/vts/functional/radio_response.cpp
@@ -885,3 +885,11 @@
     parent_v1_5.notify(info.serial);
     return Void();
 }
+
+/* 1.5 Apis */
+Return<void> RadioResponse_v1_5::setSignalStrengthReportingCriteriaResponse_1_5(
+        const RadioResponseInfo& info) {
+    rspInfo = info;
+    parent_v1_5.notify(info.serial);
+    return Void();
+}
\ No newline at end of file
diff --git a/soundtrigger/2.0/vts/functional/Android.bp b/soundtrigger/2.0/vts/functional/Android.bp
index f6207c4..13dcdec 100644
--- a/soundtrigger/2.0/vts/functional/Android.bp
+++ b/soundtrigger/2.0/vts/functional/Android.bp
@@ -19,5 +19,5 @@
     defaults: ["VtsHalTargetTestDefaults"],
     srcs: ["VtsHalSoundtriggerV2_0TargetTest.cpp"],
     static_libs: ["android.hardware.soundtrigger@2.0"],
-    test_suites: ["general-tests"],
+    test_suites: ["general-tests", "vts-core"],
 }
diff --git a/soundtrigger/2.0/vts/functional/VtsHalSoundtriggerV2_0TargetTest.cpp b/soundtrigger/2.0/vts/functional/VtsHalSoundtriggerV2_0TargetTest.cpp
index 59ac13e..d7a7d08 100644
--- a/soundtrigger/2.0/vts/functional/VtsHalSoundtriggerV2_0TargetTest.cpp
+++ b/soundtrigger/2.0/vts/functional/VtsHalSoundtriggerV2_0TargetTest.cpp
@@ -23,15 +23,15 @@
 
 #include <android/log.h>
 #include <cutils/native_handle.h>
+#include <gtest/gtest.h>
+#include <hidl/GtestPrinter.h>
+#include <hidl/ServiceManagement.h>
 #include <log/log.h>
 
 #include <android/hardware/audio/common/2.0/types.h>
 #include <android/hardware/soundtrigger/2.0/ISoundTriggerHw.h>
 #include <android/hardware/soundtrigger/2.0/types.h>
 
-#include <VtsHalHidlTargetTestBase.h>
-#include <VtsHalHidlTargetTestEnvBase.h>
-
 #define SHORT_TIMEOUT_PERIOD (1)
 
 using ::android::hardware::audio::common::V2_0::AudioDevice;
@@ -86,27 +86,11 @@
   int mCount;
 };
 
-// Test environment for SoundTrigger HIDL HAL.
-class SoundTriggerHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
-   public:
-    // get the test environment singleton
-    static SoundTriggerHidlEnvironment* Instance() {
-        static SoundTriggerHidlEnvironment* instance = new SoundTriggerHidlEnvironment;
-        return instance;
-    }
-
-    virtual void registerTestServices() override { registerTestService<ISoundTriggerHw>(); }
-
-   private:
-    SoundTriggerHidlEnvironment() {}
-};
-
 // The main test class for Sound Trigger HIDL HAL.
-class SoundTriggerHidlTest : public ::testing::VtsHalHidlTargetTestBase {
+class SoundTriggerHidlTest : public ::testing::TestWithParam<std::string> {
  public:
   virtual void SetUp() override {
-      mSoundTriggerHal = ::testing::VtsHalHidlTargetTestBase::getService<ISoundTriggerHw>(
-          SoundTriggerHidlEnvironment::Instance()->getServiceName<ISoundTriggerHw>());
+      mSoundTriggerHal = ISoundTriggerHw::getService(GetParam());
       ASSERT_NE(nullptr, mSoundTriggerHal.get());
       mCallback = new SoundTriggerHwCallback(*this);
       ASSERT_NE(nullptr, mCallback.get());
@@ -167,7 +151,7 @@
  *  - the implementation supports at least one sound model and one key phrase
  *  - the implementation supports at least VOICE_TRIGGER recognition mode
  */
-TEST_F(SoundTriggerHidlTest, GetProperties) {
+TEST_P(SoundTriggerHidlTest, GetProperties) {
   ISoundTriggerHw::Properties halProperties;
   Return<void> hidlReturn;
   int ret = -ENODEV;
@@ -194,7 +178,7 @@
  * There is no way to verify that implementation actually can load a sound model because each
  * sound model is vendor specific.
  */
-TEST_F(SoundTriggerHidlTest, LoadInvalidModelFail) {
+TEST_P(SoundTriggerHidlTest, LoadInvalidModelFail) {
   Return<void> hidlReturn;
   int ret = -ENODEV;
   ISoundTriggerHw::PhraseSoundModel model;
@@ -220,7 +204,7 @@
  * Verifies that:
  *  - the implementation returns error when passed a sound model with random data.
  */
-TEST_F(SoundTriggerHidlTest, LoadGenericSoundModelFail) {
+TEST_P(SoundTriggerHidlTest, LoadGenericSoundModelFail) {
   int ret = -ENODEV;
   ISoundTriggerHw::SoundModel model;
   SoundModelHandle handle = 0;
@@ -251,7 +235,7 @@
  *  - the implementation returns an error when called without a valid loaded sound model
  *
  */
-TEST_F(SoundTriggerHidlTest, UnloadModelNoModelFail) {
+TEST_P(SoundTriggerHidlTest, UnloadModelNoModelFail) {
   Return<int32_t> hidlReturn(0);
   SoundModelHandle halHandle = 0;
 
@@ -271,7 +255,7 @@
  * There is no way to verify that implementation actually starts recognition because no model can
  * be loaded.
  */
-TEST_F(SoundTriggerHidlTest, StartRecognitionNoModelFail) {
+TEST_P(SoundTriggerHidlTest, StartRecognitionNoModelFail) {
     Return<int32_t> hidlReturn(0);
     SoundModelHandle handle = 0;
     PhraseRecognitionExtra phrase;
@@ -299,7 +283,7 @@
  *  - the implementation returns an error when called without an active recognition running
  *
  */
-TEST_F(SoundTriggerHidlTest, StopRecognitionNoAStartFail) {
+TEST_P(SoundTriggerHidlTest, StopRecognitionNoAStartFail) {
     Return<int32_t> hidlReturn(0);
     SoundModelHandle handle = 0;
 
@@ -316,7 +300,7 @@
  *  - the implementation implements this optional method or indicates it is not support by
  *  returning -ENOSYS
  */
-TEST_F(SoundTriggerHidlTest, stopAllRecognitions) {
+TEST_P(SoundTriggerHidlTest, stopAllRecognitions) {
     Return<int32_t> hidlReturn(0);
 
     hidlReturn = mSoundTriggerHal->stopAllRecognitions();
@@ -325,11 +309,7 @@
     EXPECT_TRUE(hidlReturn == 0 || hidlReturn == -ENOSYS);
 }
 
-int main(int argc, char** argv) {
-    ::testing::AddGlobalTestEnvironment(SoundTriggerHidlEnvironment::Instance());
-    ::testing::InitGoogleTest(&argc, argv);
-    SoundTriggerHidlEnvironment::Instance()->init(&argc, argv);
-    int status = RUN_ALL_TESTS();
-    ALOGI("Test result = %d", status);
-    return status;
-}
+INSTANTIATE_TEST_SUITE_P(
+        PerInstance, SoundTriggerHidlTest,
+        testing::ValuesIn(android::hardware::getAllHalInstanceNames(ISoundTriggerHw::descriptor)),
+        android::hardware::PrintInstanceNameToString);
diff --git a/soundtrigger/2.1/vts/functional/Android.bp b/soundtrigger/2.1/vts/functional/Android.bp
index f1eb35d..7830fe2 100644
--- a/soundtrigger/2.1/vts/functional/Android.bp
+++ b/soundtrigger/2.1/vts/functional/Android.bp
@@ -25,5 +25,5 @@
                  "android.hardware.soundtrigger@2.1",
                  "libhidlmemory"
                  ],
-    test_suites: ["general-tests"],
+    test_suites: ["general-tests", "vts-core"],
 }
diff --git a/soundtrigger/2.1/vts/functional/VtsHalSoundtriggerV2_1TargetTest.cpp b/soundtrigger/2.1/vts/functional/VtsHalSoundtriggerV2_1TargetTest.cpp
index 0a2eeac..7f06ed9 100644
--- a/soundtrigger/2.1/vts/functional/VtsHalSoundtriggerV2_1TargetTest.cpp
+++ b/soundtrigger/2.1/vts/functional/VtsHalSoundtriggerV2_1TargetTest.cpp
@@ -23,6 +23,9 @@
 
 #include <android/log.h>
 #include <cutils/native_handle.h>
+#include <gtest/gtest.h>
+#include <hidl/GtestPrinter.h>
+#include <hidl/ServiceManagement.h>
 #include <log/log.h>
 
 #include <android/hardware/audio/common/2.0/types.h>
@@ -32,9 +35,6 @@
 #include <android/hidl/allocator/1.0/IAllocator.h>
 #include <hidlmemory/mapping.h>
 
-#include <VtsHalHidlTargetTestBase.h>
-#include <VtsHalHidlTargetTestEnvBase.h>
-
 #define SHORT_TIMEOUT_PERIOD (1)
 
 using ::android::sp;
@@ -94,27 +94,11 @@
     int mCount;
 };
 
-// Test environment for SoundTrigger HIDL HAL.
-class SoundTriggerHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
-   public:
-    // get the test environment singleton
-    static SoundTriggerHidlEnvironment* Instance() {
-        static SoundTriggerHidlEnvironment* instance = new SoundTriggerHidlEnvironment;
-        return instance;
-    }
-
-    virtual void registerTestServices() override { registerTestService<ISoundTriggerHw>(); }
-
-   private:
-    SoundTriggerHidlEnvironment() {}
-};
-
 // The main test class for Sound Trigger HIDL HAL.
-class SoundTriggerHidlTest : public ::testing::VtsHalHidlTargetTestBase {
+class SoundTriggerHidlTest : public ::testing::TestWithParam<std::string> {
    public:
     virtual void SetUp() override {
-        mSoundTriggerHal = ::testing::VtsHalHidlTargetTestBase::getService<ISoundTriggerHw>(
-            SoundTriggerHidlEnvironment::Instance()->getServiceName<ISoundTriggerHw>());
+        mSoundTriggerHal = ISoundTriggerHw::getService(GetParam());
         ASSERT_NE(nullptr, mSoundTriggerHal.get());
         mCallback = new SoundTriggerHwCallback(*this);
         ASSERT_NE(nullptr, mCallback.get());
@@ -196,7 +180,7 @@
  *  - the implementation supports at least one sound model and one key phrase
  *  - the implementation supports at least VOICE_TRIGGER recognition mode
  */
-TEST_F(SoundTriggerHidlTest, GetProperties) {
+TEST_P(SoundTriggerHidlTest, GetProperties) {
     ISoundTriggerHw::Properties halProperties;
     Return<void> hidlReturn;
     int ret = -ENODEV;
@@ -223,7 +207,7 @@
  * There is no way to verify that implementation actually can load a sound model because each
  * sound model is vendor specific.
  */
-TEST_F(SoundTriggerHidlTest, LoadInvalidModelFail) {
+TEST_P(SoundTriggerHidlTest, LoadInvalidModelFail) {
     Return<void> hidlReturn;
     int ret = -ENODEV;
     V2_0_ISoundTriggerHw::PhraseSoundModel model;
@@ -252,7 +236,7 @@
  * There is no way to verify that implementation actually can load a sound model because each
  * sound model is vendor specific.
  */
-TEST_F(SoundTriggerHidlTest, LoadInvalidModelFail_2_1) {
+TEST_P(SoundTriggerHidlTest, LoadInvalidModelFail_2_1) {
     Return<void> hidlReturn;
     int ret = -ENODEV;
     ISoundTriggerHw::PhraseSoundModel model;
@@ -277,7 +261,7 @@
  * Verifies that:
  *  - the implementation returns an error when passed an empty sound model
  */
-TEST_F(SoundTriggerHidlTest, LoadEmptyGenericSoundModelFail) {
+TEST_P(SoundTriggerHidlTest, LoadEmptyGenericSoundModelFail) {
     int ret = -ENODEV;
     V2_0_ISoundTriggerHw::SoundModel model;
     SoundModelHandle handle = 0;
@@ -301,7 +285,7 @@
  * Verifies that:
  *  - the implementation returns error when passed a sound model with random data.
  */
-TEST_F(SoundTriggerHidlTest, LoadGenericSoundModelFail) {
+TEST_P(SoundTriggerHidlTest, LoadGenericSoundModelFail) {
     int ret = -ENODEV;
     V2_0_ISoundTriggerHw::SoundModel model;
     SoundModelHandle handle = 0;
@@ -329,7 +313,7 @@
  * Verifies that:
  *  - the implementation returns error when passed a sound model with random data.
  */
-TEST_F(SoundTriggerHidlTest, LoadEmptyGenericSoundModelFail_2_1) {
+TEST_P(SoundTriggerHidlTest, LoadEmptyGenericSoundModelFail_2_1) {
     int ret = -ENODEV;
     ISoundTriggerHw::SoundModel model;
     SoundModelHandle handle = 0;
@@ -353,7 +337,7 @@
  * Verifies that:
  *  - the implementation returns error when passed a sound model with random data.
  */
-TEST_F(SoundTriggerHidlTest, LoadGenericSoundModelFail_2_1) {
+TEST_P(SoundTriggerHidlTest, LoadGenericSoundModelFail_2_1) {
     int ret = -ENODEV;
     ISoundTriggerHw::SoundModel model;
     SoundModelHandle handle = 0;
@@ -394,7 +378,7 @@
  *  - the implementation returns an error when called without a valid loaded sound model
  *
  */
-TEST_F(SoundTriggerHidlTest, UnloadModelNoModelFail) {
+TEST_P(SoundTriggerHidlTest, UnloadModelNoModelFail) {
     Return<int32_t> hidlReturn(0);
     SoundModelHandle halHandle = 0;
 
@@ -414,7 +398,7 @@
  * There is no way to verify that implementation actually starts recognition because no model can
  * be loaded.
  */
-TEST_F(SoundTriggerHidlTest, StartRecognitionNoModelFail) {
+TEST_P(SoundTriggerHidlTest, StartRecognitionNoModelFail) {
     Return<int32_t> hidlReturn(0);
     SoundModelHandle handle = 0;
     PhraseRecognitionExtra phrase;
@@ -444,7 +428,7 @@
  * There is no way to verify that implementation actually starts recognition because no model can
  * be loaded.
  */
-TEST_F(SoundTriggerHidlTest, StartRecognitionNoModelFail_2_1) {
+TEST_P(SoundTriggerHidlTest, StartRecognitionNoModelFail_2_1) {
     Return<int32_t> hidlReturn(0);
     SoundModelHandle handle = 0;
     PhraseRecognitionExtra phrase;
@@ -472,7 +456,7 @@
  *  - the implementation returns an error when called without an active recognition running
  *
  */
-TEST_F(SoundTriggerHidlTest, StopRecognitionNoAStartFail) {
+TEST_P(SoundTriggerHidlTest, StopRecognitionNoAStartFail) {
     Return<int32_t> hidlReturn(0);
     SoundModelHandle handle = 0;
 
@@ -489,7 +473,7 @@
  *  - the implementation implements this optional method or indicates it is not supported by
  *  returning -ENOSYS
  */
-TEST_F(SoundTriggerHidlTest, stopAllRecognitions) {
+TEST_P(SoundTriggerHidlTest, stopAllRecognitions) {
     Return<int32_t> hidlReturn(0);
 
     hidlReturn = mSoundTriggerHal->stopAllRecognitions();
@@ -498,11 +482,7 @@
     EXPECT_TRUE(hidlReturn == 0 || hidlReturn == -ENOSYS);
 }
 
-int main(int argc, char** argv) {
-    ::testing::AddGlobalTestEnvironment(SoundTriggerHidlEnvironment::Instance());
-    ::testing::InitGoogleTest(&argc, argv);
-    SoundTriggerHidlEnvironment::Instance()->init(&argc, argv);
-    int status = RUN_ALL_TESTS();
-    ALOGI("Test result = %d", status);
-    return status;
-}
+INSTANTIATE_TEST_SUITE_P(
+        PerInstance, SoundTriggerHidlTest,
+        testing::ValuesIn(android::hardware::getAllHalInstanceNames(ISoundTriggerHw::descriptor)),
+        android::hardware::PrintInstanceNameToString);
diff --git a/soundtrigger/2.2/vts/functional/Android.bp b/soundtrigger/2.2/vts/functional/Android.bp
index 08ccd7b..b5d241d 100644
--- a/soundtrigger/2.2/vts/functional/Android.bp
+++ b/soundtrigger/2.2/vts/functional/Android.bp
@@ -23,5 +23,5 @@
         "android.hardware.soundtrigger@2.1",
         "android.hardware.soundtrigger@2.2",
     ],
-    test_suites: ["general-tests"],
+    test_suites: ["general-tests", "vts-core"],
 }
diff --git a/soundtrigger/2.2/vts/functional/VtsHalSoundtriggerV2_2TargetTest.cpp b/soundtrigger/2.2/vts/functional/VtsHalSoundtriggerV2_2TargetTest.cpp
index 0f37816..1cce5a1 100644
--- a/soundtrigger/2.2/vts/functional/VtsHalSoundtriggerV2_2TargetTest.cpp
+++ b/soundtrigger/2.2/vts/functional/VtsHalSoundtriggerV2_2TargetTest.cpp
@@ -23,42 +23,26 @@
 
 #include <android/log.h>
 #include <cutils/native_handle.h>
+#include <gtest/gtest.h>
+#include <hidl/GtestPrinter.h>
+#include <hidl/ServiceManagement.h>
 #include <log/log.h>
 
 #include <android/hardware/audio/common/2.0/types.h>
 #include <android/hardware/soundtrigger/2.0/ISoundTriggerHw.h>
 #include <android/hardware/soundtrigger/2.2/ISoundTriggerHw.h>
 
-#include <VtsHalHidlTargetTestBase.h>
-#include <VtsHalHidlTargetTestEnvBase.h>
-
 using ::android::sp;
 using ::android::hardware::Return;
 using ::android::hardware::soundtrigger::V2_0::ISoundTriggerHwCallback;
 using ::android::hardware::soundtrigger::V2_0::SoundModelHandle;
 using ::android::hardware::soundtrigger::V2_2::ISoundTriggerHw;
 
-// Test environment for SoundTrigger HIDL HAL.
-class SoundTriggerHidlEnvironment : public ::testing::VtsHalHidlTargetTestEnvBase {
-   public:
-    // get the test environment singleton
-    static SoundTriggerHidlEnvironment* Instance() {
-        static SoundTriggerHidlEnvironment* instance = new SoundTriggerHidlEnvironment;
-        return instance;
-    }
-
-    void registerTestServices() override { registerTestService<ISoundTriggerHw>(); }
-
-   private:
-    SoundTriggerHidlEnvironment() {}
-};
-
 // The main test class for Sound Trigger HIDL HAL.
-class SoundTriggerHidlTest : public ::testing::VtsHalHidlTargetTestBase {
+class SoundTriggerHidlTest : public ::testing::TestWithParam<std::string> {
    public:
     void SetUp() override {
-        mSoundTriggerHal = ::testing::VtsHalHidlTargetTestBase::getService<ISoundTriggerHw>(
-            SoundTriggerHidlEnvironment::Instance()->getServiceName<ISoundTriggerHw>());
+        mSoundTriggerHal = ISoundTriggerHw::getService(GetParam());
         ASSERT_NE(nullptr, mSoundTriggerHal.get());
     }
 
@@ -77,18 +61,13 @@
  *  - the implementation returns -ENOSYS with invalid model handle
  *
  */
-TEST_F(SoundTriggerHidlTest, GetModelStateInvalidModel) {
+TEST_P(SoundTriggerHidlTest, GetModelStateInvalidModel) {
     SoundModelHandle handle = 0;
     Return<int32_t> hidlReturn = mSoundTriggerHal->getModelState(handle);
     EXPECT_TRUE(hidlReturn.isOk());
     EXPECT_EQ(-ENOSYS, hidlReturn);
 }
-
-int main(int argc, char** argv) {
-    ::testing::AddGlobalTestEnvironment(SoundTriggerHidlEnvironment::Instance());
-    ::testing::InitGoogleTest(&argc, argv);
-    SoundTriggerHidlEnvironment::Instance()->init(&argc, argv);
-    int status = RUN_ALL_TESTS();
-    ALOGI("Test result = %d", status);
-    return status;
-}
+INSTANTIATE_TEST_SUITE_P(
+        PerInstance, SoundTriggerHidlTest,
+        testing::ValuesIn(android::hardware::getAllHalInstanceNames(ISoundTriggerHw::descriptor)),
+        android::hardware::PrintInstanceNameToString);
diff --git a/wifi/1.0/vts/functional/Android.bp b/wifi/1.0/vts/functional/Android.bp
index 6fa6e7e..f3068f2 100644
--- a/wifi/1.0/vts/functional/Android.bp
+++ b/wifi/1.0/vts/functional/Android.bp
@@ -30,6 +30,7 @@
     ],
     static_libs: [
         "android.hardware.wifi@1.0",
+        "libwifi-system-iface"
     ],
 }
 
@@ -49,6 +50,7 @@
         "android.hardware.wifi@1.1",
         "android.hardware.wifi@1.2",
         "android.hardware.wifi@1.3",
+        "libwifi-system-iface"
     ],
     test_suites: ["general-tests"],
 }
@@ -66,6 +68,7 @@
     static_libs: [
         "VtsHalWifiV1_0TargetTestUtil",
         "android.hardware.wifi@1.0",
+        "libwifi-system-iface"
     ],
     test_suites: ["general-tests"],
 }
@@ -83,6 +86,7 @@
     static_libs: [
         "VtsHalWifiV1_0TargetTestUtil",
         "android.hardware.wifi@1.0",
+        "libwifi-system-iface"
     ],
     test_suites: ["general-tests"],
 }
diff --git a/wifi/1.0/vts/functional/wifi_hidl_test_utils.cpp b/wifi/1.0/vts/functional/wifi_hidl_test_utils.cpp
index f89f7b4..d584d4b 100644
--- a/wifi/1.0/vts/functional/wifi_hidl_test_utils.cpp
+++ b/wifi/1.0/vts/functional/wifi_hidl_test_utils.cpp
@@ -18,12 +18,15 @@
 
 #include <VtsHalHidlTargetTestBase.h>
 
+#include <wifi_system/interface_tool.h>
+
 #include "wifi_hidl_call_util.h"
 #include "wifi_hidl_test_utils.h"
 
 using ::android::hardware::wifi::V1_0::IWifi;
 using ::android::hardware::wifi::V1_0::IWifiApIface;
 using ::android::hardware::wifi::V1_0::IWifiChip;
+using ::android::hardware::wifi::V1_0::IWifiIface;
 using ::android::hardware::wifi::V1_0::IWifiNanIface;
 using ::android::hardware::wifi::V1_0::IWifiP2pIface;
 using ::android::hardware::wifi::V1_0::IWifiRttController;
@@ -36,6 +39,7 @@
 using ::android::sp;
 using ::android::hardware::hidl_string;
 using ::android::hardware::hidl_vec;
+using ::android::wifi_system::InterfaceTool;
 
 extern WifiHidlEnvironment* gEnv;
 
@@ -131,6 +135,16 @@
     return status_and_chip.second;
 }
 
+void setIfaceUp(const sp<IWifiIface>& iface) {
+    // Set the iface up before retrurning the object.
+    const auto& status_and_name = HIDL_INVOKE(iface, getName);
+    if (status_and_name.first.code == WifiStatusCode::SUCCESS) {
+        const auto& iface_name = status_and_name.second;
+        InterfaceTool iface_tool;
+        iface_tool.SetUpState(iface_name.c_str(), true);
+    }
+}
+
 sp<IWifiApIface> getWifiApIface(const std::string& instance_name) {
     sp<IWifiChip> wifi_chip = getWifiChip(instance_name);
     if (!wifi_chip.get()) {
@@ -143,6 +157,7 @@
     if (status_and_iface.first.code != WifiStatusCode::SUCCESS) {
         return nullptr;
     }
+    setIfaceUp(status_and_iface.second);
     return status_and_iface.second;
 }
 
@@ -158,6 +173,7 @@
     if (status_and_iface.first.code != WifiStatusCode::SUCCESS) {
         return nullptr;
     }
+    setIfaceUp(status_and_iface.second);
     return status_and_iface.second;
 }
 
@@ -173,6 +189,7 @@
     if (status_and_iface.first.code != WifiStatusCode::SUCCESS) {
         return nullptr;
     }
+    setIfaceUp(status_and_iface.second);
     return status_and_iface.second;
 }
 
@@ -188,6 +205,7 @@
     if (status_and_iface.first.code != WifiStatusCode::SUCCESS) {
         return nullptr;
     }
+    setIfaceUp(status_and_iface.second);
     return status_and_iface.second;
 }
 
@@ -196,7 +214,7 @@
     if (!wifi_chip.get()) {
         return nullptr;
     }
-    sp<IWifiStaIface> wifi_sta_iface = getWifiStaIface();
+    sp<IWifiStaIface> wifi_sta_iface = getWifiStaIface(instance_name);
     if (!wifi_sta_iface.get()) {
         return nullptr;
     }
diff --git a/wifi/1.1/vts/functional/Android.bp b/wifi/1.1/vts/functional/Android.bp
index 6662314..6d7635d 100644
--- a/wifi/1.1/vts/functional/Android.bp
+++ b/wifi/1.1/vts/functional/Android.bp
@@ -26,6 +26,7 @@
         "android.hardware.wifi@1.1",
         "android.hardware.wifi@1.2",
         "android.hardware.wifi@1.3",
+        "libwifi-system-iface"
     ],
     test_suites: ["general-tests"],
 }
diff --git a/wifi/1.2/vts/functional/Android.bp b/wifi/1.2/vts/functional/Android.bp
index b2956ce..97853d0 100644
--- a/wifi/1.2/vts/functional/Android.bp
+++ b/wifi/1.2/vts/functional/Android.bp
@@ -28,6 +28,7 @@
         "android.hardware.wifi@1.1",
         "android.hardware.wifi@1.2",
         "android.hardware.wifi@1.3",
+        "libwifi-system-iface"
     ],
     test_suites: ["general-tests"],
 }
@@ -44,6 +45,7 @@
         "android.hardware.wifi@1.0",
         "android.hardware.wifi@1.1",
         "android.hardware.wifi@1.2",
+        "libwifi-system-iface"
     ],
     test_suites: ["general-tests"],
 }
diff --git a/wifi/1.3/vts/functional/Android.bp b/wifi/1.3/vts/functional/Android.bp
index 53c8f08..9ffda8b 100644
--- a/wifi/1.3/vts/functional/Android.bp
+++ b/wifi/1.3/vts/functional/Android.bp
@@ -28,5 +28,6 @@
         "android.hardware.wifi@1.1",
         "android.hardware.wifi@1.2",
         "android.hardware.wifi@1.3",
+        "libwifi-system-iface"
     ],
 }
diff --git a/wifi/1.4/vts/functional/Android.bp b/wifi/1.4/vts/functional/Android.bp
index 42c60f2..215a810 100644
--- a/wifi/1.4/vts/functional/Android.bp
+++ b/wifi/1.4/vts/functional/Android.bp
@@ -29,5 +29,6 @@
         "android.hardware.wifi@1.2",
         "android.hardware.wifi@1.3",
         "android.hardware.wifi@1.4",
+        "libwifi-system-iface"
     ],
 }