diff --git a/audio/2.0/vts/functional/ValidateAudioConfiguration.cpp b/audio/2.0/vts/functional/ValidateAudioConfiguration.cpp
index ee49023..ec3259a 100644
--- a/audio/2.0/vts/functional/ValidateAudioConfiguration.cpp
+++ b/audio/2.0/vts/functional/ValidateAudioConfiguration.cpp
@@ -28,6 +28,7 @@
         const auto configPath = folder + '/' + configName;
         if (access(configPath.c_str(), R_OK) == 0) {
             ASSERT_VALID_XML(configPath.c_str(), configSchemaPath);
+            return; // The framework does not read past the first config file found
         }
     }
 }
diff --git a/broadcastradio/1.1/default/BroadcastRadio.cpp b/broadcastradio/1.1/default/BroadcastRadio.cpp
index 38b4b99..1bcfd82 100644
--- a/broadcastradio/1.1/default/BroadcastRadio.cpp
+++ b/broadcastradio/1.1/default/BroadcastRadio.cpp
@@ -64,7 +64,7 @@
             }),
             AmFmBandConfig({
                 Band::FM_HD,
-                87900,   // lowerLimit
+                87700,   // lowerLimit
                 107900,  // upperLimit
                 {200},   // spacings
             }),
diff --git a/configstore/1.0/default/seccomp_policy/configstore@1.0-arm64.policy b/configstore/1.0/default/seccomp_policy/configstore@1.0-arm64.policy
index 7e3dfe0c..43bf1fa 100644
--- a/configstore/1.0/default/seccomp_policy/configstore@1.0-arm64.policy
+++ b/configstore/1.0/default/seccomp_policy/configstore@1.0-arm64.policy
@@ -39,6 +39,7 @@
 rt_sigreturn: 1
 getrlimit: 1
 madvise: 1
+clock_gettime: 1
 
 # used during process crash by crash_dump to dump process info
 rt_sigprocmask: 1
diff --git a/current.txt b/current.txt
index f045fd8..5207c6a 100644
--- a/current.txt
+++ b/current.txt
@@ -244,5 +244,6 @@
 86ba9c03978b79a742e990420bc5ced0673d25a939f82572996bef92621e2014 android.hardware.cas@1.0::IMediaCasService
 503da837d1a67cbdb7c08a033e927e5430ae1b159d98bf72c6336b4dcc5e76f5 android.hardware.cas.native@1.0::types
 619600109232ed64b827c8a11beed8070b1827ae464547d7aa146cf0473b4bca android.hardware.cas.native@1.0::IDescrambler
+0a159f81359cd4f71bbe00972ee8403ea79351fb7c0cd48be72ebb3e424dbaef android.hardware.radio@1.0::types
 f4945e397b5dea41bb64518dfde59be71245d8a125fd1e0acffeb57ac7b08fed android.hardware.thermal@1.1::IThermal
 c8bc853546dd55584611def2a9fa1d99f657e3366c976d2f60fe6b8aa6d2cb87 android.hardware.thermal@1.1::IThermalCallback
diff --git a/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoDecTest.cpp b/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoDecTest.cpp
index 1d4fd67..a5b5524 100644
--- a/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoDecTest.cpp
+++ b/media/omx/1.0/vts/functional/video/VtsHalMediaOmxV1_0TargetVideoDecTest.cpp
@@ -839,7 +839,7 @@
     OMX_VIDEO_PARAM_PORTFORMATTYPE portFormat;
     *eColorFormat = OMX_COLOR_FormatUnused;
     portFormat.nIndex = 0;
-    while (1) {
+    while (portFormat.nIndex < 512) {
         status = getPortParam(omxNode, OMX_IndexParamVideoPortFormat,
                               kPortIndexOutput, &portFormat);
         if (status != ::android::hardware::media::omx::V1_0::Status::OK) break;
@@ -853,7 +853,9 @@
             break;
         }
         if (OMX_COLOR_FormatYUV420SemiPlanar == portFormat.eColorFormat ||
-            OMX_COLOR_FormatYUV420Planar == portFormat.eColorFormat) {
+            OMX_COLOR_FormatYUV420Planar == portFormat.eColorFormat ||
+            OMX_COLOR_FormatYUV420PackedPlanar == portFormat.eColorFormat ||
+            OMX_COLOR_FormatYUV420PackedSemiPlanar == portFormat.eColorFormat) {
             *eColorFormat = portFormat.eColorFormat;
             break;
         }
diff --git a/neuralnetworks/1.0/IDevice.hal b/neuralnetworks/1.0/IDevice.hal
index 9e19097..b6f9433 100644
--- a/neuralnetworks/1.0/IDevice.hal
+++ b/neuralnetworks/1.0/IDevice.hal
@@ -21,12 +21,63 @@
 import IEvent;
 import IPreparedModel;
 
+/**
+ * This interface represents a device driver.
+ */
 interface IDevice {
-    initialize() generates(Capabilities capabilities);
+    /**
+     * Gets the capabilities of a driver.
+     *
+     * @return status ErrorStatus::NONE if successful.
+     * @return capabilities Capabilities of the driver.
+     */
+    getCapabilities() generates (ErrorStatus status, Capabilities capabilities);
 
-    getSupportedSubgraph(Model model) generates(vec<bool> supported);
+    /**
+     * Gets the supported operations in a model.
+     *
+     * getSupportedSubgraph provides a more nuanced indication on whether a
+     * model is able to be compiled by the driver. Having the entire model
+     * allows for additional information such as tensor shapes to inputs or
+     * tensor strides, information which is not known in "initialize".
+     *
+     * @param model A model whose operations--and their corresponding
+     *              operands--are to be verified by the driver.
+     * @return status ErrorStatus::NONE if successful.
+     * @return supportedOperations A list of supported operations, where true
+     *                             indicates the operation is supported and
+     *                             false indicates the operation is not
+     *                             supported. The index of "supported"
+     *                             corresponds with the index of the operation
+     *                             it is describing.
+     */
+    getSupportedOperations(Model model)
+            generates (ErrorStatus status, vec<bool> supportedOperations);
 
-    prepareModel(Model model, IEvent event) generates(IPreparedModel preparedModel);
+    /**
+     * Prepares a model for execution.
+     *
+     * prepareModel is used to make any necessary transformations or alternative
+     * representations to a model for execution, possible including
+     * transformations on the constant data, optimization on the model's graph,
+     * or compilation into the device's native binary.
+     *
+     * The only information that may be unknown to the model at this stage is
+     * the shape of the tensors, which may only be known at execution time.
+     *
+     * @param model The model to be prepared for execution.
+     * @param event A synchronization callback that must be signaled once the
+     *              execution has finished.
+     * @return status ErrorStatus::NONE if successful.
+     * @return preparedModel A handle to the resultant prepared model.
+     */
+    prepareModel(Model model, IEvent event)
+            generates (ErrorStatus status, IPreparedModel preparedModel);
 
-    getStatus() generates(DeviceStatus status);
+    /**
+     * Returns the current status of a driver.
+     *
+     * @return status Status of the driver.
+     */
+    getStatus() generates (DeviceStatus status);
 };
diff --git a/neuralnetworks/1.0/IEvent.hal b/neuralnetworks/1.0/IEvent.hal
index 63afeaf..2ebda58 100644
--- a/neuralnetworks/1.0/IEvent.hal
+++ b/neuralnetworks/1.0/IEvent.hal
@@ -29,21 +29,15 @@
  * indicate to the Neuralnetworks runtime whether the computation was
  * successful or not, and that the corresponding output is ready to be
  * consumed if the execution was successful.
- *
- * TODO: Mention that "notify" is also called by a runtime thread
- * during CPU fallback execution? Depends on whether the HIDL comments
- * are strictly for vendors or not.
  */
 interface IEvent {
 
     /**
-     * IEvent::notify is called by the server thread (i.e. the thread doing the
-     * work) to mark the event as completed so that any threads requiring the
-     * corresponding resources can continue executing.
+     * IEvent::notify is called by the server thread (i.e., the thread doing
+     * the work) to mark the event as completed so that any threads requiring
+     * the corresponding output can continue executing.
      *
-     * @param status Status of the execution associated with the Event.
-     *               Should be SUCCESS or ERROR.
+     * @param status ErrorStatus::NONE if successful.
      */
-    oneway notify(Status status);
-
+    oneway notify(ErrorStatus status);
 };
diff --git a/neuralnetworks/1.0/IPreparedModel.hal b/neuralnetworks/1.0/IPreparedModel.hal
index 428ddc7..a7c3342 100644
--- a/neuralnetworks/1.0/IPreparedModel.hal
+++ b/neuralnetworks/1.0/IPreparedModel.hal
@@ -14,13 +14,33 @@
  * limitations under the License.
  */
 
-/* This HAL is a work in progress */
-
 package android.hardware.neuralnetworks@1.0;
 
 import IEvent;
 
+/**
+ * IPreparedModel describes a model that has been prepared for execution and
+ * is used to launch executions.
+ */
 interface IPreparedModel {
-    // Multiple threads can call this execute function concurrently.
-    execute(Request request, IEvent event) generates(bool success);
+    /**
+     * Spawns an asynchronous execution on a prepared model.
+     *
+     * Executions are asynchronous with respect to the Neuralnetworks runtime.
+     * To support this, IPreparedModel::execute must spawn a new task and return
+     * whether the task was successfully launched. The asynchronous task which
+     * performs the execution must call event's IEvent::notify with the status
+     * of the execution immediately after the execution has finished.
+     *
+     * Multiple threads can call this execute function concurrently.
+     *
+     * @param request The input and output information on which the prepared
+     *                model is to be executed.
+     *                prepared model.
+     * @param event A callback used for synchronization that must be signaled
+     *              once the execution has finished.
+     * @return status ErrorStatus::NONE if the asynchronous task was
+     *                successfully launched.
+     */
+    execute(Request request, IEvent event) generates (ErrorStatus status);
 };
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 844c44c..39e3d34 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -14,68 +14,945 @@
  * limitations under the License.
  */
 
-/* This HAL is a work in progress */
-
 package android.hardware.neuralnetworks@1.0;
 
-// The types an operand can have.
-// These values are the same as found in the NeuralNetworks.h file.
-// When modifying, be sure to update HAL_NUM_OPERAND_TYPES in HalIntefaces.h.
-enum OperandType : uint32_t {
-    FLOAT16                   = 0,
-    FLOAT32                   = 1,
-    INT8                      = 2,
-    UINT8                     = 3,
-    INT16                     = 4,
-    UINT16                    = 5,
-    INT32                     = 6,
-    UINT32                    = 7,
-    TENSOR_FLOAT16            = 8,
-    TENSOR_FLOAT32            = 9,
-    TENSOR_INT32              = 10,
-    TENSOR_QUANT8_ASYMM       = 11,
+/**
+ * Operand types.
+ *
+ * The type of an operand in a model.
+ *
+ * Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors
+ * with at least one dimension). Types not prefaced by TENSOR_* represent
+ * scalar values and must have no dimensions.
+ */
+enum OperandType : int32_t {
+    /**
+     * The following entries are used to declare scalars.
+     */
+    FLOAT32             = 0,
+    INT32               = 1,
+    UINT32              = 2,
+
+    /**
+     * The following entries are used to declare tensors.
+     */
+    TENSOR_FLOAT32      = 3,
+    TENSOR_INT32        = 4,
+
+    /**
+     * A tensor of 8 bit integers that represent real numbers.
+     *
+     * Attached to this tensor are two numbers that can be used to convert the
+     * 8 bit integer to the real value and vice versa. These two numbers are:
+     * - scale: a 32 bit floating point value
+     * - zero_value: a 32 bit integer
+     *
+     * The formula is:
+     * real_value = (integer_value - zero_value) * scale.
+     */
+    TENSOR_QUANT8_ASYMM = 5,
+
+    /**
+     * The following entries are OEM specific operand types.
+     */
+    OEM                 = 10000,
+    TENSOR_OEM_BYTE     = 10001,
 };
 
-// The type of operations.  Unlike the operation types found in
-// NeuralNetworks.h file, these specify the data type they operate on.
-// This is done to simplify the work of drivers.
-// TODO: Currently they are the same.  Add a conversion when finalizing the model.
-// When modifying, be sure to update HAL_NUM_OPERATION_TYPES in HalIntefaces.h.
-enum OperationType : uint32_t {
-    OEM_OPERATION                = 0,
-    ADD                          = 1,
-    AVERAGE_POOL_2D              = 2,
-    CONCATENATION                = 3,
-    CONV_2D                      = 4,
-    DEPTHWISE_CONV_2D            = 5,
-    DEPTH_TO_SPACE               = 6,
-    DEQUANTIZE                   = 7,
-    EMBEDDING_LOOKUP             = 8,
-    FAKE_QUANT                   = 9,
-    FLOOR                        = 10,
-    FULLY_CONNECTED              = 11,
-    HASHTABLE_LOOKUP             = 12,
-    L2_NORMALIZATION             = 13,
-    L2_POOL_2D                   = 14,
-    LOCAL_RESPONSE_NORMALIZATION = 15,
-    LOGISTIC                     = 16,
-    LSH_PROJECTION               = 17,
-    LSTM                         = 18,
-    MAX_POOL_2D                  = 19,
-    MUL                          = 20,
-    RELU                         = 21,
-    RELU1                        = 22,
-    RELU6                        = 23,
-    RESHAPE                      = 24,
-    RESIZE_BILINEAR              = 25,
-    RNN                          = 26,
-    SOFTMAX                      = 27,
-    SPACE_TO_DEPTH               = 28,
-    SVDF                         = 29,
-    TANH                         = 30,
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
+enum OperationType : int32_t {
+    /**
+     * Adds two tensors, elment-wise.
+     *
+     * Takes two input tensors of identical type 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}
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0: A tensor.
+     * 1: A tensor of the same type, and compatible dimensions as input0.
+     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The sum, a tensor of the same type as input0.
+     */
+    ADD = 0,
+
+    /**
+     * Performs a 2-D average pooling operation.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     *
+     * The values in output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the filter width.
+     * 8: An INT32 value, specifying the filter height.
+     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     */
+    AVERAGE_POOL_2D = 1,
+
+    /**
+     * Concatenates the input tensors along the given dimension.
+     *
+     * The input tensors must have identical type and the same dimensions except the
+     * dimension along the concatenation axis.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0 ~ n: The list on n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]
+     * n+1: An INT32 value, specifying the concatenation axis.
+     * n+2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output, a tensor of the same type as the input tensors.
+          The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+     */
+    CONCATENATION = 2,
+
+    /**
+     * Performs an 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 output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         sum_{i, j} (
+     *             input[batch, row + i, col + j, k] *
+     *             filter[channel, row + i, col + j, k] +
+     *             bias[channel]
+     *         )
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 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.
+     * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *    also be of {@link OperandType::TENSOR_FLOAT32}.
+     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *    should be of {@link OperandType::TENSOR_INT32}.
+     * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     */
+    CONV_2D = 3,
+
+    /**
+     * Performs an depthwise 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_in] containing
+     * in_channels 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 output Tensor is 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[di, dj, k, q]
+     *         )
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 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.
+     * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *    also be of {@link OperandType::TENSOR_FLOAT32}.
+     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *    should be of {@link OperandType::TENSOR_INT32}.
+     * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 9: An INT32 value, specifying the depthwise multiplier.
+     * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     */
+    DEPTHWISE_CONV_2D = 4,
+
+    /**
+     * 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 types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+     *    block_size * block_size must be a divisor of the input depth.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
+     *    depth/(block_size*block_size)].
+     */
+    DEPTH_TO_SPACE = 5,
+
+    /**
+     * Dequantizes the input tensor.
+     *
+     * The formula is:
+     *     output = (input - zero_value) * scale.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0, but with type
+          {@link OperandType::TENSOR_FLOAT32}.
+     */
+    DEQUANTIZE = 6,
+
+    /**
+     * Looks up items from a given tensor.
+     *
+     * Each item in the output is a raw copy of the corresponding item in
+     * the input “values”. If the the given “lookup” indices are out of bounds,
+     * the op will fail and an error will be reported.
+     *
+     * Inputs:
+     * * 0: Values. An n-D tensor of any type X (where n >= 2). E.g., if n is 2,
+     *      then the shape would be [lookup_dimension, values_dimension], where
+     *      “lookup_dimension” corresponds to the indexing dimension in the lookup
+     *      table, and “values_dimension” to the contents.
+     * * 1: Lookups. An 1-D tensor of type T, of shape [lookup_size], where
+     *      “lookup_size” is the number of elements to look for, and each entry
+     *      corresponds to the first dimension of the “values” tensor.
+     *
+     * Output:
+     * * 0: A n-D tensor of type X and the same rank and shape as the “values”
+     *      tensor, except for the first dimension which has size “lookup_size”.
+     */
+    EMBEDDING_LOOKUP = 7,
+
+    /**
+     * Computes element-wise floor() on the input tensor.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0: A tensor.
+     *
+     * Ouputs:
+     * 0: The output, a tensor of the same type and dimensions as input0.
+     */
+    FLOOR = 8,
+
+    /**
+     * 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 types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
+     *    a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
+     *    [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
+     *    and “input_size” is the size of the input.
+     * 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} type, the bias should
+     *    also be of {@link OperandType::TENSOR_FLOAT32}.
+     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *    should be of {@link OperandType::TENSOR_INT32}.
+     * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output tensor, of shape [batch_size, num_units].
+     */
+    FULLY_CONNECTED = 9,
+
+    /**
+     * Looks up values of a hash table with given keys.
+     *
+     * Inputs:
+     * * 0: Lookups. A 1-D int32 tensor with shape [ k ].
+     * * 1: Keys. A 1-D int32 tensor with shape [ n ], *MUST* be sorted in
+     *      ascending order.
+     * * 2: Values. A tensor with shape [ n … ].
+     *
+     * Outputs:
+     * * 0: Output. A tensor with shape [ k …].
+     * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup
+     *      hits or not.
+     */
+    HASHTABLE_LOOKUP = 10,
+
+    /**
+     * Applies L2 normalization along a the depth dimension.
+     *
+     * The values in output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         input[batch, row, col, channel] /
+     *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
+     *
+     * For x with more dimensions, independently normalizes each 1-D slice along dimension dim.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     */
+    L2_NORMALIZATION = 11,
+
+    /**
+     * Performs an 2-D L2 pooling operation.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     *
+     * The values in output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the filter width.
+     * 8: An INT32 value, specifying the filter height.
+     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     */
+    L2_POOL_2D = 12,
+
+    /**
+     * 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.
+     *
+     * In details:
+     *     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)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the radius of the normalization window.
+     * 2: A FLOAT32 value, specifying the bias, must not be zero.
+     * 3: A FLOAT32 value, specifying the scale factor, alpha.
+     * 4: A FLOAT32 value, specifying the exponent, beta.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    LOCAL_RESPONSE_NORMALIZATION = 13,
+
+    /**
+     * Computes sigmoid activation on the input tensor element-wise.
+     *
+     * In details:
+     *     output = 1 / (1 + exp(-input))
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    LOGISTIC = 14,
+
+    /**
+     * Projects an input to a bit vector via locality senstive hashing.
+     *
+     * Inputs:
+     * * 0: Hash functions. Dim.size == 2, DataType: Float.
+     *            Tensor[0].Dim[0]: Number of hash functions.
+     *            Tensor[0].Dim[1]: Number of seeds per hash functions.
+     *            Tensor[0].Dim[1] <= 32 in sparse case.
+     *
+     * * 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(=1).
+     *          Computed bit vector is considered to be sparse.
+     *          Each output element is an int32 made up of multiple bits computed from
+     *          hash functions.
+     *
+     *        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.
+     */
+    LSH_PROJECTION = 15,
+
+    /**
+     * Long short-term memory unit (LSTM) recurrent network layer.
+     *
+     * The default non-peephole implementation is based on:
+     * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+     * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
+     * Computation, 9(8):1735-1780, 1997.
+     *
+     * The peephole implementation 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.
+     *
+     * 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 class has the following independently optional inputs:
+     * * If input gate (if CIFG): “input_to_forget_weights”,
+     *   “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”.
+     * * If no peephole connections: “cell_to_input_weights”,
+     *   “cell_to_forget_weights”, “cell_to_output_weights”.
+     * * If no projection layer: “projection_weights” and “projection_bias”.
+     * * If no projection bias: “projection_bias”.
+     *
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Inputs:
+     * * 0: Input.
+     *      A 2-D tensor of type T, of shape [batch_size, input_size], where
+     *      “batch_size” corresponds to the batching dimension, and “input_size”
+     *      is the size of the input.
+     * * 1: input_to_input_weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size], where
+     *      “num_units” corresponds to the number of cell units.
+     * * 2: input_to_forget_weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size].
+     * * 3: input_to_cell_weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size].
+     * * 4: input_to_output_weights.
+     *      A 2-D tensor of type T, of shape [num_units, input_size].
+     * * 5: recurrent_to_input_weights.
+     *      A 2-D tensor of type T, 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: recurrent_to_forget_weights.
+     *      A 2-D tensor of type T, of shape [num_units, output_size].
+     * * 7: recurrent_to_cell_weights.
+     *      A 2-D tensor of type T, of shape [num_units, output_size].
+     * * 8: recurrent_to_output_weights.
+     *      A 2-D tensor of type T, of shape [num_units, output_size].
+     * * 9: cell_to_input_weights.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 10:cell_to_forget_weights.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 11:cell_to_output_weights.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 12:input_gate_bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 13:forget_gate_bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 14:cell_bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 15:output_gate_bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     * * 16:projection_weights.
+     *      A 2-D tensor of type T, of shape [output_size, num_units].
+     * * 17:projection_bias.
+     *      A 1-D tensor of type T, of shape [output_size].
+     *
+     * Parameters:
+     * * 18:fused_activation_function.
+     *      An (optional) ActivationFunctionType indicating the activation
+     *      function.
+     *      If “NONE” is specified then it results in a linear activation.
+     * * 19:cell_clip.
+     *      A 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.
+     * * 20:proj_clip.
+     *      A 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.
+     *
+     * Outputs:
+     * * 0: scratch_buffer.
+     *      A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
+     * * 1: output_state.
+     *      A 2-D tensor of type T, of shape [batch_size, output_size].
+     * * 2: cell_state.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     * * 3: output.
+     *      A 2-D tensor of type T, of shape [batch_size, output_size]. This is
+     *      effectively the same as the current “output_state” value.
+     */
+    LSTM = 16,
+
+    /**
+     * Performs an 2-D max pooling operation.
+     *
+     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     *
+     * The values in output Tensor is computed as:
+     *     output[batch, row, col, channel] =
+     *         max_{i, j} (input[batch, row + i, col + j, channel])
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
+     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
+     * 7: An INT32 value, specifying the filter width.
+     * 8: An INT32 value, specifying the filter height.
+     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     */
+    MAX_POOL_2D = 17,
+
+    /**
+     * Multiplies two tensors, elment-wise.
+     *
+     * Takes two input tensors of identical type 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.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: up to 4
+     *
+     * Inputs:
+     * 0: A tensor.
+     * 1: A tensor of the same type, and compatible dimensions as input0.
+     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *    Specifies the activation to invoke on the result of each addition.
+     *
+     * Ouputs:
+     * 0: The product, a tensor of the same type as input0.
+     */
+    MUL = 18,
+
+    /**
+     * Computes rectified linear activation on the input tensor element-wise.
+     *
+     * In details:
+     *     output = max(0, input)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    RELU = 19,
+
+    /**
+     * Computes rectified linear 1 activation on the input tensor element-wise.
+     *
+     * In details:
+     *     output = min(1.f, max(-1.f, input))
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    RELU1 = 20,
+
+    /**
+     * Computes rectified linear 6 activation on the input tensor element-wise.
+     *
+     * In details:
+     *     output = min(6, max(0, input))
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    RELU6 = 21,
+
+    /**
+     * 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 types: {@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 type {@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.
+     *
+     * Ouputs:
+     * 0: The output tensor, of shape specified by the input shape.
+     */
+    RESHAPE = 22,
+
+    /**
+     * Resizes images to given size using the bilinear interpretation.
+     *
+     * Resized images will be distorted if their original aspect ratio is not the same as input.
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * 1: An INT32 value, specifying the output width of the output tensor.
+     * 2: An INT32 value, specifying the output height of the output tensor.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
+     */
+    RESIZE_BILINEAR = 23,
+
+    /**
+     * 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 types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Inputs:
+     * * 0: input.
+     *      A 2-D tensor of type T, 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 type T, of shape [num_units, input_size], where
+     *      “num_units” corresponds to the number of units.
+     * * 2: recurrent_weights.
+     *      A 2-D tensor of type T, of shape [num_units, num_units], with columns
+     *      corresponding to the weights from each unit.
+     * * 3: bias.
+     *      A 1-D tensor of type T, of shape [num_units].
+     *
+     *    For FLOAT32 input tensor, bias must also be FLOAT32.
+     *    For UINT8 input tensor, bias must be INT32.
+     *
+     * Parameters
+     * * 4: fused_activation_function.
+     *      An (optional) ActivationFunctionType indicating the activation
+     *      function. If “NONE” is specified then it results in a linear
+     *      activation.
+     *
+     * * 5: Hidden state.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     *
+     * Outputs:
+     * * 0: output.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units]. This is
+     *      effectively the same as the current state value.
+     */
+    RNN = 24,
+
+    /**
+     * Computes the softmax activation on the input tensor element-wise, per batch, by
+     * normalizing the input vector so the maximum coefficient is zero.
+     *
+     * In details:
+     *     output[batch, i] =
+     *         exp((input[batch, i] - max(input[batch, :])) * beta) /
+     *         sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 2 or 4.
+     *
+     * Inputs:
+     * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+     * 1: A FLOAT32 value, specifying the scaling factor for the exponent, beta.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    SOFTMAX = 25,
+
+    /**
+     * 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 types: {@link OperandType::TENSOR_FLOAT32}
+     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor rank: 4, with "NHWC" data layout.
+     *
+     * Inputs:
+     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+     *    block_size must be a divisor of both the input height and width.
+     *
+     * Ouputs:
+     * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
+     *    depth*block_size*block_size].
+     */
+    SPACE_TO_DEPTH = 26,
+
+    /**
+     * 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, "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 types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Inputs:
+     * * 0: input.
+     *      A 2-D tensor of type T, 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 type T, of shape [num_units, input_size], where
+     *      “num_units” corresponds to the number of units.
+     * * 2: weights_time.
+     *      A 2-D tensor of type T, of shape [num_units, memory_size], where
+     *      “memory_size” corresponds to the fixed-size of the memory.
+     * * 3: bias.
+     *      A optional 1-D tensor of type T, of shape [num_units].
+     *
+     *    For FLOAT32 input tensor, bias must also be FLOAT32.
+     *    For UINT8 input tensor, bias must be INT32.
+     *
+     * Parameters:
+     * * 4: rank.
+     *      The rank of the SVD approximation.
+     * * 5: fused_activation_function.
+     *      An (optional) ActivationFunctionType indicating the activation function.
+     *      If “NONE” is specified then it results in a linear activation.
+     *
+     * Outputs:
+     * * 0: state.
+     *      A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+     * * 1: output.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     */
+    SVDF = 27,
+
+    /**
+     * Computes hyperbolic tangent of input tensor element-wise.
+     *
+     * In details:
+     *     output = tanh(input)
+     *
+     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor rank: up to 4.
+     *
+     * Inputs:
+     * 0: A tensor, specifying the input.
+     *
+     * Ouputs:
+     * 0: The output tensor of same shape as input0.
+     */
+    TANH = 28,
+
+    /**
+     * OEM specific operation.
+     *
+     * This operation is OEM specific. It should only be used for OEM applications.
+     */
+    OEM_OPERATION = 10000,
 };
 
-// Fused activation functions
+/**
+ * Fused activation function types.
+ */
 enum FusedActivationFunc : int32_t {
     NONE  = 0,
     RELU  = 1,
@@ -83,134 +960,313 @@
     RELU6 = 3,
 };
 
-// How an operand is used.
-enum OperandLifeTime : uint32_t {
-    // The operand is internal to the model.  It's created by an operation
-    // and consumed by other operations.
+/**
+ * How an operand is used.
+ */
+enum OperandLifeTime : int32_t {
+    /**
+     * The operand is internal to the model.  It's created by an operation
+     * and consumed by other operations.
+     */
     TEMPORARY_VARIABLE,
-    // The operand is an input of the model. An operand can't be both
-    // input and output of a model.
+
+    /**
+     * The operand is an input of the model. An operand can't be both
+     * input and output of a model.
+     */
     MODEL_INPUT,
-    // The operand is an output of the model.
+
+    /**
+     * The operand is an output of the model.
+     */
     MODEL_OUTPUT,
-    // The operand is a constant found in Model.operandValues.
+
+    /**
+     * The operand is a constant found in Model.operandValues.
+     */
     CONSTANT_COPY,
-    // The operand is a constant that was specified via a Memory object.
-    CONSTANT_REFERENCE
+
+    /**
+     * The operand is a constant that was specified via a Memory object.
+     */
+    CONSTANT_REFERENCE,
 };
 
-// Status of a device.
-enum DeviceStatus : uint32_t {
+/**
+ * Status of a device.
+ */
+enum DeviceStatus : int32_t {
     AVAILABLE,
     BUSY,
     OFFLINE,
-    UNKNOWN  // Do we need this?
+    UNKNOWN,
 };
 
-// For the reference workload
-// Used by a driver to report its performance characteristics.
-// TODO revisit the data types and scales.
-struct PerformanceInfo {
-    float execTime;    // in nanoseconds
-    float powerUsage;  // in picoJoules
-};
-
+/**
+ * A typed operation.
+ */
 struct OperationTuple {
-    // The type of operation.
+    /**
+     * The type of operation.
+     */
     OperationType operationType;
-    // The input data type of operation.
+
+    /**
+     * The input data type of operation.
+     */
     OperandType operandType;
 };
 
-// The capabilities of a driver.
+/**
+ * Performance information for the reference workload.
+ *
+ * Used by a driver to report its performance characteristics.
+ */
+struct PerformanceInfo {
+    /**
+     * Execution time in nanoseconds.
+     */
+    float execTime;
+
+    /**
+     * Power usage in picoJoules.
+     */
+    float powerUsage;
+};
+
+/**
+ * The capabilities of a driver.
+ */
 struct Capabilities {
+    /**
+     * A collection of typed operations supported by the driver.
+     */
     vec<OperationTuple> supportedOperationTuples;
-    // TODO Do the same for baseline model IDs
+
+    /**
+     * Indicates whether a driver caches its prepared model for reuse the next
+     * time the application begins. This is useful because the model may have
+     * been prepared in a previous run.
+     *
+     * True if caching is supported, false otherwise.
+     */
     bool cachesCompilation;
-    // TODO revisit the data types and scales.
-    float bootupTime;  // in nanoseconds
-    PerformanceInfo float16Performance;
+
+    /**
+     * Driver performance when operating on float32 data.
+     */
     PerformanceInfo float32Performance;
+
+    /**
+     * Driver performance when operating on asymmetric 8-bit quantized data.
+     */
     PerformanceInfo quantized8Performance;
 };
 
-// Describes the location of a data object.
+/**
+ * Describes the location of a data object.
+ */
 struct DataLocation {
-    // The index of the memory pool where this location is found.
-    // Two special values can also be used.  See the LOCATION_* constants above.
+    /**
+     * The index of the memory pool where this location is found.
+     */
     uint32_t poolIndex;
-    // Offset in bytes from the start of the pool.
+
+    /**
+     * Offset in bytes from the start of the pool.
+     */
     uint32_t offset;
-    // The length of the data, in bytes.
+
+    /**
+     * The length of the data in bytes.
+     */
     uint32_t length;
 };
 
+/**
+ * Describes one operand of the model's graph.
+ */
 struct Operand {
+    /**
+     * Data type of the operand.
+     */
     OperandType type;
+
+    /**
+     * Dimensions of the operand.
+     */
     vec<uint32_t> dimensions;
 
-    // The number of operations that uses this operand as input.
-    // TODO It would be nice to track the actual consumers, e.g. vec<uint32_t> consumers;
+    /**
+     * The number of operations that use this operand as input.
+     */
     uint32_t numberOfConsumers;
 
+    /**
+     * Quantized scale of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+     */
     float scale;
+
+    /**
+     * Quantized zero-point offset of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+     */
     int32_t zeroPoint;
 
-    // How the operand is used.
+    /**
+     * How the operand is used.
+     */
     OperandLifeTime lifetime;
 
-    // Where to find the data for this operand.
-    // If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, or MODEL_OUTPUT:
-    // - All the fields will be 0.
-    // If the lifetime is CONSTANT_COPY:
-    // - location.poolIndex is 0.
-    // - location.offset is the offset in bytes into Model.operandValues.
-    // - location.length is set.
-    // If the lifetime is CONSTANT_REFERENCE:
-    // - location.poolIndex is set.
-    // - location.offset is the offset in bytes into the specified pool.
-    // - location.length is set.
+    /**
+     * Where to find the data for this operand.
+     * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, or MODEL_OUTPUT:
+     * - All the fields will be 0.
+     * If the lifetime is CONSTANT_COPY:
+     * - location.poolIndex is 0.
+     * - location.offset is the offset in bytes into Model.operandValues.
+     * - location.length is set.
+     * If the lifetime is CONSTANT_REFERENCE:
+     * - location.poolIndex is set.
+     * - location.offset is the offset in bytes into the specified pool.
+     * - location.length is set.
+     */
     DataLocation location;
 };
 
-// Describes one operation of the graph.
+/**
+ * Describes one operation of the model's graph.
+ */
 struct Operation {
-    // The tuple describing the operation type and input type.
+    /**
+     * The tuple describing the operation type and input type.
+     */
     OperationTuple opTuple;
-    // Describes the table that contains the indexes of the inputs of the
-    // operation. The offset is the index in the operandIndexes table.
+
+    /**
+     * 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.
+
+    /**
+     * 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
+ * weights or scalars added at construction time. The only information that
+ * might not be known is the shape of the input tensors.
+ */
 struct Model {
+    /**
+     * All operands included in the model.
+     */
     vec<Operand> operands;
+
+    /**
+     * All operations included in the model.
+     *
+     * The operations are sorted into execution order.
+     */
     vec<Operation> operations;
+
+    /**
+     * Input indexes of the model.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
     vec<uint32_t> inputIndexes;
+
+    /**
+     * Output indexes of the model.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
     vec<uint32_t> outputIndexes;
+
+    /**
+     * A byte buffer containing operand data that were copied into the model.
+     */
     vec<uint8_t> operandValues;
+
+    /**
+     * A collection of shared memory pools containing operand data that were
+     * registered by the model.
+     */
     vec<memory> pools;
 };
 
+/**
+ * Metadata information specifying the location of the input or output data and
+ * any updates to the input or output operand.
+ */
 struct RequestArgument {
-    // The location within one of the memory pools
+    /**
+     * The location within one of the memory pools passed in the Request.
+     */
     DataLocation location;
-    // If dimensions.size() > 0, dimension information was provided along with the
-    // argument.  This can be the case for models that accept inputs of varying size.
-    // This can't change the rank, just the value of the dimensions that were
-    // unspecified in the model.
+
+    /**
+     * Updated dimension information.
+     *
+     * If dimensions.size() > 0, dimension information was provided along with the
+     * argument.  This can be the case for models that accept inputs of varying size.
+     * This can't change the rank, just the value of the dimensions that were
+     * unspecified in the model.
+     */
     vec<uint32_t> dimensions;
 };
 
+/**
+ * Inputs to be sent to and outputs to be retrieved from a prepared model.
+ *
+ * A Request serves two primary tasks:
+ * 1) Provides the input and output data to be used when executing the model.
+ * 2) Specifies any updates to the input operand metadata that were left
+ *    unspecified at model preparation time.
+ */
 struct Request {
+    /**
+     * Input data and information to be used in the execution of a prepared
+     * model.
+     *
+     * The index of the input corresponds to the index in Model.inputIndexes.
+     *   E.g., input[i] corresponds to Model.inputIndexes[i].
+     */
     vec<RequestArgument> inputs;
+
+    /**
+     * Output data and information to be used in the execution of a prepared
+     * model.
+     *
+     * The index of the output corresponds to the index in Model.outputIndexes.
+     *   E.g., output[i] corresponds to Model.outputIndexes[i].
+     */
     vec<RequestArgument> outputs;
+
+    /**
+     * A collection of shared memory pools containing operand data for both the
+     * inputs and the outputs to a model.
+     */
     vec<memory> pools;
 };
 
-enum Status : uint32_t {
-    SUCCESS,
-    ERROR,
+/**
+ * Return status of a function.
+ */
+enum ErrorStatus : int32_t {
+    NONE,
+    DEVICE_UNAVAILABLE,
+    GENERAL_FAILURE,
+    OUTPUT_INSUFFICIENT_SIZE,
+    INVALID_ARGUMENT,
 };
diff --git a/neuralnetworks/1.0/vts/functional/Android.bp b/neuralnetworks/1.0/vts/functional/Android.bp
index 1efff0e..2318430 100644
--- a/neuralnetworks/1.0/vts/functional/Android.bp
+++ b/neuralnetworks/1.0/vts/functional/Android.bp
@@ -18,6 +18,7 @@
     name: "VtsHalNeuralnetworksV1_0TargetTest",
     srcs: [
         "Event.cpp",
+        "GeneratedTestHarness.cpp",
         "VtsHalNeuralnetworksV1_0TargetTest.cpp",
     ],
     defaults: ["VtsHalTargetTestDefaults"],
@@ -27,4 +28,8 @@
         "android.hidl.memory@1.0",
         "libhidlmemory",
     ],
+    header_libs: [
+        "libneuralnetworks_generated_test_harness_headers",
+        "libneuralnetworks_generated_tests",
+    ],
 }
diff --git a/neuralnetworks/1.0/vts/functional/Event.cpp b/neuralnetworks/1.0/vts/functional/Event.cpp
index 67de4f5..efaacb3 100644
--- a/neuralnetworks/1.0/vts/functional/Event.cpp
+++ b/neuralnetworks/1.0/vts/functional/Event.cpp
@@ -21,10 +21,10 @@
     //     thread::join failed: Resource deadlock would occur
 }
 
-Return<void> Event::notify(ReturnedStatus status) {
+Return<void> Event::notify(ErrorStatus status) {
     {
         std::lock_guard<std::mutex> lock(mMutex);
-        mStatus = status == ReturnedStatus::SUCCESS ? Status::SUCCESS : Status::ERROR;
+        mStatus = status == ErrorStatus::NONE ? Status::SUCCESS : Status::ERROR;
         if (mStatus == Status::SUCCESS && mCallback != nullptr) {
             bool success = mCallback();
             if (!success) {
diff --git a/neuralnetworks/1.0/vts/functional/Event.h b/neuralnetworks/1.0/vts/functional/Event.h
index 4f7f2f6..7dd4070 100644
--- a/neuralnetworks/1.0/vts/functional/Event.h
+++ b/neuralnetworks/1.0/vts/functional/Event.h
@@ -24,8 +24,6 @@
 using ::android::hardware::Void;
 using ::android::sp;
 
-using ReturnedStatus = ::android::hardware::neuralnetworks::V1_0::Status;
-
 /**
  * The Event class is used internally by the Neuralnetworks runtime to
  * synchronize between different threads. An asynchronous task is launched
@@ -77,9 +75,9 @@
      *
      * IEvent::notify can be called at most once on a given event.
      *
-     * @param neuralnetworks::V1_0::Status SUCCESS or ERROR
+     * @param neuralnetworks::V1_0::ErrorStatus ErrorStatus::NONE on success
      */
-    Return<void> notify(ReturnedStatus status) override;
+    Return<void> notify(ErrorStatus status) override;
 
     /**
      * Event::poll returns the current status of the event.
diff --git a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
new file mode 100644
index 0000000..db90ac2
--- /dev/null
+++ b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
@@ -0,0 +1,203 @@
+/*
+ * Copyright (C) 2017 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 "Event.h"
+#include "TestHarness.h"
+#include "VtsHalNeuralnetworksV1_0TargetTest.h"
+
+#include <android-base/logging.h>
+#include <android/hidl/memory/1.0/IMemory.h>
+#include <hidlmemory/mapping.h>
+
+namespace android {
+namespace hardware {
+namespace neuralnetworks {
+namespace V1_0 {
+namespace vts {
+namespace functional {
+// allocator helper
+hidl_memory allocateSharedMemory(int64_t size, const std::string& type = "ashmem");
+
+namespace generated_tests {
+using ::android::hardware::neuralnetworks::V1_0::implementation::Event;
+using ::generated_tests::filter;
+using ::generated_tests::for_all;
+using ::generated_tests::for_each;
+using ::generated_tests::resize_accordingly;
+using ::generated_tests::MixedTyped;
+using ::generated_tests::MixedTypedExampleType;
+using ::generated_tests::Float32Operands;
+using ::generated_tests::Int32Operands;
+using ::generated_tests::Quant8Operands;
+// Top level driver for models and examples generated by test_generator.py
+// Test driver for those generated from ml/nn/runtime/test/spec
+void Execute(const sp<IDevice>& device, std::function<Model(void)> create_model,
+             std::function<bool(int)> is_ignored,
+             const std::vector<MixedTypedExampleType>& examples) {
+    Model model = create_model();
+    sp<IPreparedModel> preparedModel;
+    sp<Event> preparationEvent = new Event();
+    ASSERT_NE(nullptr, preparationEvent.get());
+    Return<void> prepareRet = device->prepareModel(
+        model, preparationEvent, [&](ErrorStatus status, const sp<IPreparedModel>& prepared) {
+            EXPECT_EQ(ErrorStatus::NONE, status);
+            preparedModel = prepared;
+        });
+    ASSERT_TRUE(prepareRet.isOk());
+    ASSERT_NE(nullptr, preparedModel.get());
+    Event::Status preparationStatus = preparationEvent->wait();
+    EXPECT_EQ(Event::Status::SUCCESS, preparationStatus);
+
+    const uint32_t INPUT = 0;
+    const uint32_t OUTPUT = 1;
+
+    int example_no = 1;
+    for (auto& example : examples) {
+        SCOPED_TRACE(example_no++);
+
+        const MixedTyped& inputs = example.first;
+        const MixedTyped& golden = example.second;
+
+        std::vector<RequestArgument> inputs_info, outputs_info;
+        uint32_t inputSize = 0, outputSize = 0;
+
+        // This function only partially specifies the metadata (vector of RequestArguments).
+        // The contents are copied over below.
+        for_all(inputs, [&inputs_info, &inputSize](int index, auto, auto s) {
+            if (inputs_info.size() <= static_cast<size_t>(index)) inputs_info.resize(index + 1);
+            RequestArgument arg = {
+                .location = {.poolIndex = INPUT, .offset = 0, .length = static_cast<uint32_t>(s)},
+                .dimensions = {},
+            };
+            inputs_info[index] = arg;
+            inputSize += s;
+        });
+        // Compute offset for inputs 1 and so on
+        {
+            size_t offset = 0;
+            for (auto& i : inputs_info) {
+                i.location.offset = offset;
+                offset += i.location.length;
+            }
+        }
+
+        MixedTyped test;  // holding test results
+
+        // Go through all outputs, initialize RequestArgument descriptors
+        resize_accordingly<float>(golden, test);
+        resize_accordingly<int32_t>(golden, test);
+        resize_accordingly<uint8_t>(golden, test);
+        for_all(golden, [&outputs_info, &outputSize](int index, auto, auto s) {
+            if (outputs_info.size() <= static_cast<size_t>(index)) outputs_info.resize(index + 1);
+            RequestArgument arg = {
+                .location = {.poolIndex = OUTPUT, .offset = 0, .length = static_cast<uint32_t>(s)},
+                .dimensions = {},
+            };
+            outputs_info[index] = arg;
+            outputSize += s;
+        });
+        // Compute offset for outputs 1 and so on
+        {
+            size_t offset = 0;
+            for (auto& i : outputs_info) {
+                i.location.offset = offset;
+                offset += i.location.length;
+            }
+        }
+        std::vector<hidl_memory> pools = {allocateSharedMemory(inputSize),
+                                          allocateSharedMemory(outputSize)};
+        ASSERT_NE(0ull, pools[INPUT].size());
+        ASSERT_NE(0ull, pools[OUTPUT].size());
+
+        // load data
+        sp<IMemory> inputMemory = mapMemory(pools[INPUT]);
+        sp<IMemory> outputMemory = mapMemory(pools[OUTPUT]);
+        ASSERT_NE(nullptr, inputMemory.get());
+        ASSERT_NE(nullptr, outputMemory.get());
+        char* inputPtr = reinterpret_cast<char*>(static_cast<void*>(inputMemory->getPointer()));
+        char* outputPtr = reinterpret_cast<char*>(static_cast<void*>(outputMemory->getPointer()));
+        ASSERT_NE(nullptr, inputPtr);
+        ASSERT_NE(nullptr, outputPtr);
+        inputMemory->update();
+        outputMemory->update();
+
+        // Go through all inputs, copy the values
+        for_all(inputs, [&inputs_info, inputPtr](int index, auto p, auto s) {
+            char* begin = (char*)p;
+            char* end = begin + s;
+            // TODO: handle more than one input
+            std::copy(begin, end, inputPtr + inputs_info[index].location.offset);
+        });
+
+        inputMemory->commit();
+        outputMemory->commit();
+        // execute request
+        sp<Event> executionEvent = new Event();
+        ASSERT_NE(nullptr, executionEvent.get());
+        Return<ErrorStatus> executeStatus = preparedModel->execute(
+            {.inputs = inputs_info, .outputs = outputs_info, .pools = pools}, executionEvent);
+        ASSERT_TRUE(executeStatus.isOk());
+        EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executeStatus));
+        Event::Status eventStatus = executionEvent->wait();
+        EXPECT_EQ(Event::Status::SUCCESS, eventStatus);
+
+        // validate results
+        outputMemory->read();
+#define COPY_BACK(ty)                                                              \
+    for_each<ty>(test, [&outputs_info, outputPtr](int index, std::vector<ty>& m) { \
+        RequestArgument& i = outputs_info[index];                                  \
+        ASSERT_EQ(m.size(), i.location.length / sizeof(ty));                       \
+        char* begin = outputPtr + i.location.offset;                               \
+        memcpy(m.data(), begin, i.location.length);                                \
+    });
+        COPY_BACK(float);
+        COPY_BACK(int32_t);
+        COPY_BACK(uint8_t);
+#undef COPY_BACK
+        outputMemory->commit();
+        // Filter out don't cares
+        MixedTyped filtered_golden;
+        MixedTyped filtered_test;
+        filter<float>(golden, &filtered_golden, is_ignored);
+        filter<float>(test, &filtered_test, is_ignored);
+        filter<int32_t>(golden, &filtered_golden, is_ignored);
+        filter<int32_t>(test, &filtered_test, is_ignored);
+        filter<uint8_t>(golden, &filtered_golden, is_ignored);
+        filter<uint8_t>(test, &filtered_test, is_ignored);
+
+        // We want "close-enough" results for float
+        for_each<float>(filtered_golden, [&filtered_test](int index, auto& golden_float) {
+            auto& test_float_operands = std::get<Float32Operands>(filtered_test);
+            auto& test_float = test_float_operands[index];
+            for (unsigned int i = 0; i < golden_float.size(); i++) {
+                SCOPED_TRACE(i);
+                EXPECT_NEAR(golden_float[i], test_float[i], 1.e-5);
+            }
+        });
+        EXPECT_EQ(std::get<Int32Operands>(filtered_golden), std::get<Int32Operands>(filtered_test));
+        EXPECT_EQ(std::get<Quant8Operands>(filtered_golden),
+                  std::get<Quant8Operands>(filtered_test));
+    }
+}
+
+}  // namespace generated_tests
+
+}  // namespace functional
+}  // namespace vts
+}  // namespace V1_0
+}  // namespace neuralnetworks
+}  // namespace hardware
+}  // namespace android
diff --git a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp
index 9c64c04..453e3e5 100644
--- a/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp
+++ b/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp
@@ -16,12 +16,13 @@
 
 #define LOG_TAG "neuralnetworks_hidl_hal_test"
 
-#include "Event.h"
 #include "VtsHalNeuralnetworksV1_0TargetTest.h"
+#include "Event.h"
+#include "TestHarness.h"
+
 #include <android-base/logging.h>
 #include <android/hidl/memory/1.0/IMemory.h>
 #include <hidlmemory/mapping.h>
-#include <string>
 
 namespace android {
 namespace hardware {
@@ -31,6 +32,11 @@
 namespace functional {
 
 using ::android::hardware::neuralnetworks::V1_0::implementation::Event;
+using ::generated_tests::MixedTypedExampleType;
+namespace generated_tests {
+extern void Execute(const sp<IDevice>&, std::function<Model(void)>, std::function<bool(int)>,
+                    const std::vector<MixedTypedExampleType>&);
+}
 
 // A class for test environment setup
 NeuralnetworksHidlEnvironment::NeuralnetworksHidlEnvironment() {}
@@ -64,24 +70,24 @@
 
 // status test
 TEST_F(NeuralnetworksHidlTest, StatusTest) {
-    DeviceStatus status = device->getStatus();
-    EXPECT_EQ(DeviceStatus::AVAILABLE, status);
+    Return<DeviceStatus> status = device->getStatus();
+    ASSERT_TRUE(status.isOk());
+    EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
 }
 
 // initialization
-TEST_F(NeuralnetworksHidlTest, InitializeTest) {
-    Return<void> ret = device->initialize([](const Capabilities& capabilities) {
-        EXPECT_NE(nullptr, capabilities.supportedOperationTuples.data());
-        EXPECT_NE(0ull, capabilities.supportedOperationTuples.size());
-        EXPECT_EQ(0u, static_cast<uint32_t>(capabilities.cachesCompilation) & ~0x1);
-        EXPECT_LT(0.0f, capabilities.bootupTime);
-        EXPECT_LT(0.0f, capabilities.float16Performance.execTime);
-        EXPECT_LT(0.0f, capabilities.float16Performance.powerUsage);
-        EXPECT_LT(0.0f, capabilities.float32Performance.execTime);
-        EXPECT_LT(0.0f, capabilities.float32Performance.powerUsage);
-        EXPECT_LT(0.0f, capabilities.quantized8Performance.execTime);
-        EXPECT_LT(0.0f, capabilities.quantized8Performance.powerUsage);
-    });
+TEST_F(NeuralnetworksHidlTest, GetCapabilitiesTest) {
+    Return<void> ret =
+        device->getCapabilities([](ErrorStatus status, const Capabilities& capabilities) {
+            EXPECT_EQ(ErrorStatus::NONE, status);
+            EXPECT_NE(nullptr, capabilities.supportedOperationTuples.data());
+            EXPECT_NE(0ull, capabilities.supportedOperationTuples.size());
+            EXPECT_EQ(0u, static_cast<uint32_t>(capabilities.cachesCompilation) & ~0x1);
+            EXPECT_LT(0.0f, capabilities.float32Performance.execTime);
+            EXPECT_LT(0.0f, capabilities.float32Performance.powerUsage);
+            EXPECT_LT(0.0f, capabilities.quantized8Performance.execTime);
+            EXPECT_LT(0.0f, capabilities.quantized8Performance.powerUsage);
+        });
     EXPECT_TRUE(ret.isOk());
 }
 
@@ -104,9 +110,7 @@
             .scale = 0.0f,
             .zeroPoint = 0,
             .lifetime = OperandLifeTime::MODEL_INPUT,
-            .location = {.poolIndex = 0,
-                         .offset = 0,
-                         .length = 0},
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
         },
         {
             .type = OperandType::TENSOR_FLOAT32,
@@ -115,9 +119,7 @@
             .scale = 0.0f,
             .zeroPoint = 0,
             .lifetime = OperandLifeTime::CONSTANT_COPY,
-            .location = {.poolIndex = 0,
-                         .offset = 0,
-                         .length = size},
+            .location = {.poolIndex = 0, .offset = 0, .length = size},
         },
         {
             .type = OperandType::INT32,
@@ -126,9 +128,7 @@
             .scale = 0.0f,
             .zeroPoint = 0,
             .lifetime = OperandLifeTime::CONSTANT_COPY,
-            .location = {.poolIndex = 0,
-                         .offset = size,
-                         .length = sizeof(int32_t)},
+            .location = {.poolIndex = 0, .offset = size, .length = sizeof(int32_t)},
         },
         {
             .type = OperandType::TENSOR_FLOAT32,
@@ -137,9 +137,7 @@
             .scale = 0.0f,
             .zeroPoint = 0,
             .lifetime = OperandLifeTime::MODEL_OUTPUT,
-            .location = {.poolIndex = 0,
-                         .offset = 0,
-                         .length = 0},
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
         },
     };
 
@@ -169,6 +167,7 @@
         .pools = pools,
     };
 }
+}  // anonymous namespace
 
 // allocator helper
 hidl_memory allocateSharedMemory(int64_t size, const std::string& type = "ashmem") {
@@ -189,16 +188,16 @@
 
     return memory;
 }
-}  // anonymous namespace
 
 // supported subgraph test
-TEST_F(NeuralnetworksHidlTest, SupportedSubgraphTest) {
+TEST_F(NeuralnetworksHidlTest, SupportedOperationsTest) {
     Model model = createTestModel();
-    std::vector<bool> supported;
-    Return<void> ret = device->getSupportedSubgraph(
-        model, [&](const hidl_vec<bool>& hidl_supported) { supported = hidl_supported; });
-    ASSERT_TRUE(ret.isOk());
-    EXPECT_EQ(/*model.operations.size()*/ 0ull, supported.size());
+    Return<void> ret = device->getSupportedOperations(
+        model, [&](ErrorStatus status, const hidl_vec<bool>& supported) {
+            EXPECT_EQ(ErrorStatus::NONE, status);
+            EXPECT_EQ(model.operations.size(), supported.size());
+        });
+    EXPECT_TRUE(ret.isOk());
 }
 
 // execute simple graph
@@ -211,9 +210,15 @@
 
     // prepare request
     Model model = createTestModel();
+    sp<IPreparedModel> preparedModel;
     sp<Event> preparationEvent = new Event();
     ASSERT_NE(nullptr, preparationEvent.get());
-    sp<IPreparedModel> preparedModel = device->prepareModel(model, preparationEvent);
+    Return<void> prepareRet = device->prepareModel(
+        model, preparationEvent, [&](ErrorStatus status, const sp<IPreparedModel>& prepared) {
+            EXPECT_EQ(ErrorStatus::NONE, status);
+            preparedModel = prepared;
+        });
+    ASSERT_TRUE(prepareRet.isOk());
     ASSERT_NE(nullptr, preparedModel.get());
     Event::Status preparationStatus = preparationEvent->wait();
     EXPECT_EQ(Event::Status::SUCCESS, preparationStatus);
@@ -251,11 +256,12 @@
     // execute request
     sp<Event> executionEvent = new Event();
     ASSERT_NE(nullptr, executionEvent.get());
-    bool success = preparedModel->execute({.inputs = inputs, .outputs = outputs, .pools = pools},
-                                          executionEvent);
-    EXPECT_TRUE(success);
-    Event::Status executionStatus = executionEvent->wait();
-    EXPECT_EQ(Event::Status::SUCCESS, executionStatus);
+    Return<ErrorStatus> executeStatus = preparedModel->execute(
+        {.inputs = inputs, .outputs = outputs, .pools = pools}, executionEvent);
+    ASSERT_TRUE(executeStatus.isOk());
+    EXPECT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(executeStatus));
+    Event::Status eventStatus = executionEvent->wait();
+    EXPECT_EQ(Event::Status::SUCCESS, eventStatus);
 
     // validate results { 1+5, 2+6, 3+7, 4+8 }
     outputMemory->read();
@@ -264,8 +270,15 @@
     EXPECT_EQ(expectedData, outputData);
 }
 
+// Mixed-typed examples
+typedef MixedTypedExampleType MixedTypedExample;
+
+// in frameworks/ml/nn/runtime/tests/generated/
+#include "all_generated_vts_tests.cpp"
+
 // TODO: Add tests for execution failure, or wait_for/wait_until timeout.
-//       Discussion: https://googleplex-android-review.git.corp.google.com/#/c/platform/hardware/interfaces/+/2654636/5/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp@222
+//       Discussion:
+//       https://googleplex-android-review.git.corp.google.com/#/c/platform/hardware/interfaces/+/2654636/5/neuralnetworks/1.0/vts/functional/VtsHalNeuralnetworksV1_0TargetTest.cpp@222
 
 }  // namespace functional
 }  // namespace vts
diff --git a/radio/1.0/types.hal b/radio/1.0/types.hal
index c5d7f8a..4d22bc0 100644
--- a/radio/1.0/types.hal
+++ b/radio/1.0/types.hal
@@ -1507,8 +1507,8 @@
     int32_t lac;                          // 16-bit Location Area Code, 0..65535, INT_MAX if unknown
     int32_t cid;                          // 16-bit GSM Cell Identity described in
                                           // TS 27.007, 0..65535, INT_MAX if unknown
-    int32_t arfcn;                        // 16-bit GSM Absolute RF channel number, INT_MAX if
-                                          // unknown
+    int32_t arfcn;                        // 16-bit GSM Absolute RF channel number; this value must
+                                          // be valid
     uint8_t bsic;                         // 6-bit Base Station Identity Code, 0xFF if unknown
 };
 
@@ -1520,9 +1520,9 @@
     int32_t cid;                          // 28-bit UMTS Cell Identity described in
                                           // TS 25.331, 0..268435455, INT_MAX if unknown
     int32_t psc;                          // 9-bit UMTS Primary Scrambling Code described in
-                                          // TS 25.331, 0..511, INT_MAX if unknown
-    int32_t uarfcn;                       // 16-bit UMTS Absolute RF Channel Number, INT_MAX if
-                                          // unknown
+                                          // TS 25.331, 0..511; this value must be valid
+    int32_t uarfcn;                       // 16-bit UMTS Absolute RF Channel Number; this value must
+                                          // be valid
 };
 
 struct CellIdentityCdma {
@@ -1547,10 +1547,10 @@
                                           // unknown
     int32_t ci;                           // 28-bit Cell Identity described in TS TS 27.007, INT_MAX
                                           // if unknown
-    int32_t pci;                          // physical cell id 0..503, INT_MAX if unknown
+    int32_t pci;                          // physical cell id 0..503; this value must be valid
     int32_t tac;                          // 16-bit tracking area code, INT_MAX if unknown
-    int32_t earfcn;                       // 18-bit LTE Absolute RC Channel Number, INT_MAX if
-                                          // unknown
+    int32_t earfcn;                       // 18-bit LTE Absolute RF Channel Number; this value must
+                                          // be valid
 };
 
 struct CellIdentityTdscdma {