diff --git a/camera/provider/2.4/default/CameraProvider.cpp b/camera/provider/2.4/default/CameraProvider.cpp
index 8e37b26..6313939 100644
--- a/camera/provider/2.4/default/CameraProvider.cpp
+++ b/camera/provider/2.4/default/CameraProvider.cpp
@@ -298,7 +298,8 @@
         return true;
     }
 
-    mPreferredHal3MinorVersion = property_get_int32("ro.camera.wrapper.hal3TrebleMinorVersion", 3);
+    mPreferredHal3MinorVersion =
+        property_get_int32("ro.vendor.camera.wrapper.hal3TrebleMinorVersion", 3);
     ALOGV("Preferred HAL 3 minor version is %d", mPreferredHal3MinorVersion);
     switch(mPreferredHal3MinorVersion) {
         case 2:
diff --git a/compatibility_matrices/Android.mk b/compatibility_matrices/Android.mk
index 948b4fe..ee97433 100644
--- a/compatibility_matrices/Android.mk
+++ b/compatibility_matrices/Android.mk
@@ -34,30 +34,47 @@
 LOCAL_MODULE := framework_compatibility_matrix.legacy.xml
 LOCAL_MODULE_STEM := compatibility_matrix.legacy.xml
 LOCAL_SRC_FILES := $(LOCAL_MODULE_STEM)
-LOCAL_KERNEL_VERSIONS := 3.18.0 4.4.0 4.9.0
+LOCAL_KERNEL_VERSIONS := \
+    3.18.0 \
+    4.4.0 \
+    4.9.0 \
+    4.14.0 \
+
 include $(BUILD_FRAMEWORK_COMPATIBILITY_MATRIX)
 
 include $(CLEAR_VARS)
 LOCAL_MODULE := framework_compatibility_matrix.1.xml
 LOCAL_MODULE_STEM := compatibility_matrix.1.xml
 LOCAL_SRC_FILES := $(LOCAL_MODULE_STEM)
-LOCAL_KERNEL_VERSIONS := 3.18.0 4.4.0 4.9.0
+LOCAL_KERNEL_VERSIONS := \
+    3.18.0 \
+    4.4.0 \
+    4.9.0 \
+    4.14.0 \
+
 include $(BUILD_FRAMEWORK_COMPATIBILITY_MATRIX)
 
 include $(CLEAR_VARS)
 LOCAL_MODULE := framework_compatibility_matrix.2.xml
 LOCAL_MODULE_STEM := compatibility_matrix.2.xml
 LOCAL_SRC_FILES := $(LOCAL_MODULE_STEM)
-LOCAL_KERNEL_VERSIONS := 3.18.0 4.4.0 4.9.0
-include $(BUILD_FRAMEWORK_COMPATIBILITY_MATRIX)
+LOCAL_KERNEL_VERSIONS := \
+    3.18.0 \
+    4.4.0 \
+    4.9.0 \
+    4.14.0 \
 
-# TODO(b/72409164): STOPSHIP: update kernel version requirements
+include $(BUILD_FRAMEWORK_COMPATIBILITY_MATRIX)
 
 include $(CLEAR_VARS)
 LOCAL_MODULE := framework_compatibility_matrix.3.xml
 LOCAL_MODULE_STEM := compatibility_matrix.3.xml
 LOCAL_SRC_FILES := $(LOCAL_MODULE_STEM)
-LOCAL_KERNEL_VERSIONS := 4.4.0 4.9.0
+LOCAL_KERNEL_VERSIONS := \
+    4.4.0 \
+    4.9.0 \
+    4.14.0 \
+
 include $(BUILD_FRAMEWORK_COMPATIBILITY_MATRIX)
 
 # Framework Compatibility Matrix (common to all FCM versions)
diff --git a/current.txt b/current.txt
index ddbca36..7e4b102 100644
--- a/current.txt
+++ b/current.txt
@@ -241,11 +241,11 @@
 86ba9c03978b79a742e990420bc5ced0673d25a939f82572996bef92621e2014 android.hardware.cas@1.0::IMediaCasService
 503da837d1a67cbdb7c08a033e927e5430ae1b159d98bf72c6336b4dcc5e76f5 android.hardware.cas.native@1.0::types
 619600109232ed64b827c8a11beed8070b1827ae464547d7aa146cf0473b4bca android.hardware.cas.native@1.0::IDescrambler
-246a56d37d57a47224562c9d077b4a2886ce6242b9311bd98a17325944c280d7 android.hardware.neuralnetworks@1.0::types
 93eb3757ceaf21590fa4cd1d4a7dfe3b3794af5396100a6d25630879352abce9 android.hardware.neuralnetworks@1.0::IDevice
 f66f9a38541bf92001d3adcce678cd7e3da2262124befb460b1c9aea9492813b android.hardware.neuralnetworks@1.0::IExecutionCallback
 953607822954435874f4b81686440a604e2a88cdd2d9164c6293f3d5772510d7 android.hardware.neuralnetworks@1.0::IPreparedModel
 73e03573494ba96f0e711ab7f1956c5b2d54c3da690cd7ecf4d6d0f287447730 android.hardware.neuralnetworks@1.0::IPreparedModelCallback
+246a56d37d57a47224562c9d077b4a2886ce6242b9311bd98a17325944c280d7 android.hardware.neuralnetworks@1.0::types
 f4945e397b5dea41bb64518dfde59be71245d8a125fd1e0acffeb57ac7b08fed android.hardware.thermal@1.1::IThermal
 c8bc853546dd55584611def2a9fa1d99f657e3366c976d2f60fe6b8aa6d2cb87 android.hardware.thermal@1.1::IThermalCallback
 
@@ -259,7 +259,8 @@
 251594ea9b27447bfa005ebd806e58fb0ae4aad84a69938129c9800ec0c64eda android.hardware.gnss@1.0::IGnssMeasurementCallback
 4e7169919d24fbe5573e5bcd683d0bd7abf553a4e6c34c41f9dfc1e12050db07 android.hardware.gnss@1.0::IGnssNavigationMessageCallback
 5804ca86611d72e5481f022b3a0c1b334217f2e4988dad25730c42af2d1f4d1c android.hardware.neuralnetworks@1.0::IDevice
-6721fc5b64d997f3eda15b762a0dd9f3fa414926219dbca58312972d565b4bee android.hardware.neuralnetworks@1.0::types
+12e8dca4ab7d8aadd0ef8f1b438021938e2396139e85db2ed65783b08800aa52 android.hardware.neuralnetworks@1.0::IExecutionCallback
+702f9a4cd3b7486a4b04f7155b737757ac2ca4b3548976d5782ad3cae9ff9780 android.hardware.neuralnetworks@1.0::types
 d4840db8efabdf1e4b344fc981cd36e5fe81a39aff6e199f6d06c1c8da413efd android.hardware.radio@1.0::types
 b280c4704dfcc548a9bf127b59b7c3578f460c50cce70a06b66fe0df8b27cff0 android.hardware.wifi@1.0::types
 
@@ -337,8 +338,8 @@
 b8c7ed58aa8740361e63d0ce9e7c94227572a629f356958840b34809d2393a7c android.hardware.media.bufferpool@1.0::IClientManager
 4a2c0dc82780e6c90731725a103feab8ab6ecf85a64e049b9cbd2b2c61620fe1 android.hardware.media.bufferpool@1.0::IConnection
 6aef1218e5949f867b0104752ac536c1b707222a403341720de90141df129e3e android.hardware.media.bufferpool@1.0::types
-3e4d8e0085ebe8549efb8ad4b8b400a141a3fa3f47ae23696b3e05a1612eb003 android.hardware.neuralnetworks@1.1::IDevice
-e808a6f61cd7b47887c599d8843e67a2dcbf4ec5aadd5d22fdce93020070ef1b android.hardware.neuralnetworks@1.1::types
+7698dc2382a2eeb43541840e3ee624f34108efdfb976b2bfa7c13ef15fb8c4c4 android.hardware.neuralnetworks@1.1::IDevice
+5604001029a255648a9e955de0a822a48d9ba7cc259b106fb8be0cd43dc8eece android.hardware.neuralnetworks@1.1::types
 8d3d86da0bfa4bf070970d8303c659f67f35d670c287d45a3f542e4fedadd578 android.hardware.nfc@1.1::INfc
 e85f566698d2a2c28100e264fcf2c691a066756ddf8dd341d009ff50cfe10614 android.hardware.nfc@1.1::INfcClientCallback
 5e278fcaa3287d397d8eebe1c22aaa28150f5caae1cf9381cd6dc32cb37899c5 android.hardware.nfc@1.1::types
diff --git a/keymaster/3.0/vts/functional/keymaster_hidl_hal_test.cpp b/keymaster/3.0/vts/functional/keymaster_hidl_hal_test.cpp
index fbe5237..3a181a9 100644
--- a/keymaster/3.0/vts/functional/keymaster_hidl_hal_test.cpp
+++ b/keymaster/3.0/vts/functional/keymaster_hidl_hal_test.cpp
@@ -2918,28 +2918,6 @@
 }
 
 /*
- * EncryptionOperationsTest.AesEcbWithUserId
- *
- * Verifies that AES ECB mode works when Tag::USER_ID is specified.
- */
-TEST_F(EncryptionOperationsTest, AesEcbWithUserId) {
-    string key = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
-    ASSERT_EQ(ErrorCode::OK, ImportKey(AuthorizationSetBuilder()
-                                           .Authorization(TAG_NO_AUTH_REQUIRED)
-                                           .Authorization(TAG_USER_ID, 0)
-                                           .AesEncryptionKey(key.size() * 8)
-                                           .EcbMode()
-                                           .Padding(PaddingMode::PKCS7),
-                                       KeyFormat::RAW, key));
-
-    string message = "Hello World!";
-    auto params = AuthorizationSetBuilder().BlockMode(BlockMode::ECB).Padding(PaddingMode::PKCS7);
-    string ciphertext = EncryptMessage(message, params);
-    string plaintext = DecryptMessage(ciphertext, params);
-    EXPECT_EQ(message, plaintext);
-}
-
-/*
  * EncryptionOperationsTest.AesEcbRoundTripSuccess
  *
  * Verifies that AES encryption fails in the correct way when an unauthorized mode is specified.
diff --git a/neuralnetworks/1.0/IExecutionCallback.hal b/neuralnetworks/1.0/IExecutionCallback.hal
index ef0f454..9c06166 100644
--- a/neuralnetworks/1.0/IExecutionCallback.hal
+++ b/neuralnetworks/1.0/IExecutionCallback.hal
@@ -28,7 +28,7 @@
      * ErrorStatus resulting from the execution. If the asynchronous task
      * is not launched, notify must be invoked with the appropriate error.
      *
-     * @return param Error status returned from launching the asynchronous task
+     * @param status Error status returned from launching the asynchronous task
      *               (if the launch fails) or from the asynchronous task itself
      *               (if the launch succeeds). Must be:
      *               - NONE if the asynchronous execution was successful
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 5b8f22c..8c07fcc 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -24,38 +24,40 @@
  * 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.
+ *
+ * Although many types are defined, most operators accept just a few
+ * types. Most used are {@link OperandType::TENSOR_FLOAT32},
+ * {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * and {@link OperandType::INT32}.
  */
 enum OperandType : int32_t {
-    /**
-     * The following entries are used to declare scalars.
-     */
+    /** A 32 bit floating point scalar value. */
     FLOAT32             = 0,
+    /** A signed 32 bit integer scalar value. */
     INT32               = 1,
+    /** An unsigned 32 bit integer scalar value. */
     UINT32              = 2,
 
-    /**
-     * The following entries are used to declare tensors.
-     */
+    /** A tensor of 32 bit floating point values. */
     TENSOR_FLOAT32      = 3,
+    /** A tensor of 32 bit integer values. */
     TENSOR_INT32        = 4,
-
-    /**
-     * A tensor of 8 bit integers that represent real numbers.
+    /** 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 greater than zero
-     * - zero_value: a 32 bit integer
+     * - scale: a 32 bit floating point value greater than zero.
+     * - zeroPoint: a 32 bit integer, in range [0, 255].
      *
      * The formula is:
-     * real_value = (integer_value - zero_value) * scale.
+     * real_value = (integer_value - zeroPoint) * scale.
      */
     TENSOR_QUANT8_ASYMM = 5,
 
-    /**
-     * The following entries are OEM specific operand types.
-     */
+    /** OEM specific scalar value. */
     OEM                 = 10000,
+
+    /** A tensor of OEM specific values. */
     TENSOR_OEM_BYTE     = 10001,
 };
 
@@ -66,9 +68,9 @@
  */
 enum OperationType : int32_t {
     /**
-     * Adds two tensors, elment-wise.
+     * Adds two tensors, element-wise.
      *
-     * Takes two input tensors of identical type and compatible dimensions.  The output
+     * 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:
@@ -79,22 +81,25 @@
      * It starts with the trailing dimensions, and works its way forward.
      *
      * Example:
-     *     input1.dimension =    {4, 1, 2}
+     *
+     *     input1.dimension = {4, 1, 2}
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@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 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.
+     * * 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.
+     * Outputs:
+     * * 0: The sum, a tensor of the same type as input0.
      */
     ADD = 0,
 
@@ -103,29 +108,50 @@
      *
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are 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.
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
-     * 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.
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels)
+     * data layout.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * 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.
+     * * 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 stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 6: An INT32 value, specifying the stride when walking through input
+     *      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.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 2: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the filter width.
+     * * 5: An INT32 value, specifying the filter height.
+     * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
      */
     AVERAGE_POOL_2D = 1,
 
@@ -135,19 +161,21 @@
      * 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 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.
+     * * 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm].
+     *            For inputs of {@link OperandType::TENSOR_QUANT8_ASYMM} type, all
+     *            input tensors must have the same scale and zeroPoint.
+     * * n: An INT32 value, specifying the concatenation axis.
      *
-     * Ouputs:
-     * 0: The output, a tensor of the same type as the input tensors.
-          The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+     * Outputs:
+     * * 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,
 
@@ -159,7 +187,8 @@
      *
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[batch, row, col, channel] =
      *         sum_{i, j} (
      *             input[batch, row + i, col + j, k] *
@@ -167,77 +196,135 @@
      *             bias[channel]
      *         )
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * 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.
+     * Both explicit padding and implicit padding are supported.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     * 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.
+     * * 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}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 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 stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 8: An INT32 value, specifying the stride when walking through input
+     *      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.
+     *
+     * 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.
+     * * 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}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 4: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 5: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+     *      condition must be satisfied: output_scale > input_scale * filter_scale.
      */
     CONV_2D = 3,
 
     /**
-     * Performs an depthwise 2-D convolution operation.
+     * Performs a 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
+     * 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 output Tensor is computed as:
+     * 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[di, dj, k, q]
+     *             filter[1, di, dj, k * channel_multiplier + q]
      *         )
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * 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 [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 {@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.
+     * Both explicit padding and implicit padding are supported.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     * 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.
+     * * 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}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 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 stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 8: An INT32 value, specifying the stride when walking through input
+     *      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.
+     *
+     * 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 {@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}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 4: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 5: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 6: An INT32 value, specifying the depthwise multiplier.
+     * * 7: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *       Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+     *      condition must be satisfied: output_scale > input_scale * filter_scale.
      */
     DEPTHWISE_CONV_2D = 4,
 
@@ -255,18 +342,20 @@
      * 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 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.
+     * * 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)].
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
+     *      depth/(block_size*block_size)].
      */
     DEPTH_TO_SPACE = 5,
 
@@ -274,53 +363,69 @@
      * Dequantizes the input tensor.
      *
      * The formula is:
-     *     output = (input - zero_value) * scale.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *     output = (input - zeroPoint) * 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}.
+     * * 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}.
+     * Outputs:
+     * * 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.
+     * Looks up sub-tensors in the input 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.
+     * 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.
      *
      * 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.
+     * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32} type.
+     *      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 of type X and the same rank and shape as the “values”
-     *      tensor, except for the first dimension which has size “lookup_size”.
+     * * 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.
      */
     EMBEDDING_LOOKUP = 7,
 
     /**
      * Computes element-wise floor() on the input tensor.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: A tensor.
+     * * 0: A tensor.
      *
-     * Ouputs:
-     * 0: The output, a tensor of the same type and dimensions as input0.
+     * Outputs:
+     * * 0: The output tensor, of the same type and dimensions as the input tensor.
      */
     FLOOR = 8,
 
@@ -329,66 +434,104 @@
      * 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 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.
+     * * 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}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 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].
+     * Outputs:
+     * * 0: The output tensor, of shape [batch_size, num_units].
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+     *      condition must be satisfied: output_scale > input_scale * filter_scale.
      */
     FULLY_CONNECTED = 9,
 
     /**
-     * Looks up values of a hash table with given keys.
+     * 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.
      *
      * 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 … ].
+     * * 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 …].
-     * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup
-     *      hits or not.
+     * * 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 = 10,
 
     /**
-     * Applies L2 normalization along a the depth dimension.
+     * Applies L2 normalization along the depth dimension.
      *
-     * The values in output Tensor is computed as:
+     * 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 x with more dimensions, independently normalizes each 1-D slice along dimension dim.
+     * For input tensor 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.
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels).
      *
      * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth].
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
      */
     L2_NORMALIZATION = 11,
 
@@ -397,28 +540,48 @@
      *
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are 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 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.
+     * Both explicit padding and implicit padding are supported.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * Inputs (explicit padding):
+     * * 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 stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 6: An INT32 value, specifying the stride when walking through input
+     *      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.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 2: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the filter width.
+     * * 5: An INT32 value, specifying the filter height.
+     * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
      */
     L2_POOL_2D = 12,
 
@@ -429,41 +592,49 @@
      * 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:
+     * 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)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * 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.
+     * * 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.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     LOCAL_RESPONSE_NORMALIZATION = 13,
 
     /**
      * Computes sigmoid activation on the input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = 1 / (1 + exp(-input))
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * 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.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM} type,
+     *      the scale must be 1.f / 256 and the zeroPoint must be 0.
      */
     LOGISTIC = 14,
 
@@ -502,102 +673,165 @@
     LSH_PROJECTION = 15,
 
     /**
-     * Long short-term memory unit (LSTM) recurrent network layer.
+     * Performs a single time step in a Long Short-Term Memory (LSTM) layer
      *
-     * The default non-peephole implementation is based on:
-     * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+     * 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.
+     *
+     * The operation has the following independently optional inputs:
+     * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights (\f$W_{hi}\f$),
+     *   cell-to-input (\f$W_{ci}\f$) weights, and input gate bias (\f$b_i\f$) either all have values,
+     *   or none of them have values (i.e., all set to null). 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}
+     * * 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 none of them have values.
+     *   If they have values, the peephole optimization is used.
+     * * 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.
+     *
+     * 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 is based on:
+     * 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 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:
+     * Supported tensor types (type T):
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Inputs:
-     * * 0: Input.
+     * * 0: The input (\f$x_t\f$).
      *      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.
+     * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
      *      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.
+     * * 2: The input-to-forget weights (\f$W_{xf}\f$).
      *      A 2-D tensor of type T, of shape [num_units, input_size].
-     * * 3: input_to_cell_weights.
+     * * 3: The input-to-cell weights (\f$W_{xc}\f$).
      *      A 2-D tensor of type T, of shape [num_units, input_size].
-     * * 4: input_to_output_weights.
+     * * 4: The input-to-output weights (\f$W_{xo}\f$).
      *      A 2-D tensor of type T, of shape [num_units, input_size].
-     * * 5: recurrent_to_input_weights.
+     * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
      *      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.
+     * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
      *      A 2-D tensor of type T, of shape [num_units, output_size].
-     * * 7: recurrent_to_cell_weights.
+     * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
      *      A 2-D tensor of type T, of shape [num_units, output_size].
-     * * 8: recurrent_to_output_weights.
+     * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
      *      A 2-D tensor of type T, of shape [num_units, output_size].
-     * * 9: cell_to_input_weights.
+     * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 10:cell_to_forget_weights.
+     * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 11:cell_to_output_weights.
+     * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 12:input_gate_bias.
+     * * 12:The input gate bias (\f$b_i\f$). Optional.
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 13:forget_gate_bias.
+     * * 13:The forget gate bias (\f$b_f\f$).
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 14:cell_bias.
+     * * 14:The cell bias (\f$b_c\f$).
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 15:output_gate_bias.
+     * * 15:The output gate bias (\f$b_o\f$).
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 16:projection_weights.
+     * * 16:The projection weights (\f$W_{proj}\f$). Optional.
      *      A 2-D tensor of type T, of shape [output_size, num_units].
-     * * 17:projection_bias.
+     * * 17:The projection bias (\f$b_{proj}\f$). Optional.
      *      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
+     * * 18:The output state (in) (\f$h_{t-1}\f$).
+     *      A 2-D tensor of type T, of shape [batch_size, output_size].
+     * * 19:The cell state (in) (\f$C_{t-1}\f$).
+     *      A 2-D tensor of type T, 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.
-     * * 20:proj_clip.
-     *      A clipping threshold for the output from the projection layer, such
+     * * 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.
      *
      * Outputs:
-     * * 0: scratch_buffer.
-     *      A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
-     * * 1: output_state.
+     * * 0: The scratch buffer.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units * 4] with
+     *      CIFG, or [batch_size, num_units * 3] without CIFG.
+     * * 1: The output state (out) (\f$h_t\f$).
      *      A 2-D tensor of type T, of shape [batch_size, output_size].
-     * * 2: cell_state.
+     * * 2: The cell state (out) (\f$C_t\f$).
      *      A 2-D tensor of type T, of shape [batch_size, num_units].
-     * * 3: output.
+     * * 3: The output (\f$o_t\f$).
      *      A 2-D tensor of type T, of shape [batch_size, output_size]. This is
-     *      effectively the same as the current “output_state” value.
+     *      effectively the same as the current “output state (out)” value.
      */
     LSTM = 16,
 
@@ -606,36 +840,56 @@
      *
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are 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 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.
+     * Both explicit padding and implicit padding are supported.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * Inputs (explicit padding):
+     * * 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 stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 6: An INT32 value, specifying the stride when walking through input
+     *      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.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 2: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the filter width.
+     * * 5: An INT32 value, specifying the filter height.
+     * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
      */
     MAX_POOL_2D = 17,
 
     /**
-     * Multiplies two tensors, elment-wise.
+     * Multiplies two tensors, element-wise.
      *
-     * Takes two input tensors of identical type and compatible dimensions.  The output
+     * 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:
@@ -645,72 +899,85 @@
      * 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}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@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 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.
+     * * 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.
+     * Outputs:
+     * * 0: The product, a tensor of the same type as input0.
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+     *      condition must be satisfied: output_scale > input1_scale * input2_scale.
      */
     MUL = 18,
 
     /**
      * Computes rectified linear activation on the input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = max(0, input)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * 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.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     RELU = 19,
 
     /**
      * Computes rectified linear 1 activation on the input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = min(1.f, max(-1.f, input))
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * 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.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     RELU1 = 20,
 
     /**
      * Computes rectified linear 6 activation on the input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = min(6, max(0, input))
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * 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.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     RELU6 = 21,
 
@@ -720,36 +987,41 @@
      * 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 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.
+     * * 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.
+     * Outputs:
+     * * 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.
+     * Resized images must be distorted if their output aspect ratio is not the same as
+     * input aspect ratio.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * 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.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the output height of the output tensor.
+     * * 2: An INT32 value, specifying the output width of the output tensor.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
      */
     RESIZE_BILINEAR = 23,
 
@@ -768,7 +1040,7 @@
      * * “activation” is the function passed as the “fused_activation_function”
      *   argument (if not “NONE”).
      *
-     * Supported tensor types:
+     * Supported tensor types (Type T):
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Inputs:
@@ -784,21 +1056,18 @@
      *      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
+     * * 4: hidden state (in).
+     *      A 2-D tensor of type T, 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.
      *
-     * * 5: Hidden state.
+     * Outputs:
+     * * 0: hidden state (out).
      *      A 2-D tensor of type T, of shape [batch_size, num_units].
      *
-     * Outputs:
-     * * 0: output.
+     * * 1: output.
      *      A 2-D tensor of type T, of shape [batch_size, num_units]. This is
      *      effectively the same as the current state value.
      */
@@ -808,21 +1077,26 @@
      * 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:
+     * 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)}
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * 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.
+     * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+     * * 1: A FLOAT32 value, specifying the positive scaling factor for the exponent, beta.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM} type,
+     *      the scale must be 1.f / 256 and the zeroPoint must be 0.
      */
     SOFTMAX = 25,
 
@@ -839,18 +1113,20 @@
      * 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 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.
+     * * 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].
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
+     *      depth*block_size*block_size].
      */
     SPACE_TO_DEPTH = 26,
 
@@ -874,8 +1150,8 @@
      *
      * Specifically, for rank 1, this layer implements the operation:
      *
-     *    memory = push(conv1d(inputs, weights_feature, feature_dim, "VALID"));
-     *    outputs = activation(memory * weights_time + bias);
+     *     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
@@ -892,7 +1168,7 @@
      * Each rank adds a dimension to the weights matrices by means of stacking
      * the filters.
      *
-     * Supported tensor types:
+     * Supported tensor types (type T):
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Inputs:
@@ -907,20 +1183,17 @@
      *      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.
+     *      An optional 1-D tensor of type T, of shape [num_units].
+     * * 4: state (in).
+     *      A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+     * * 5: rank.
      *      The rank of the SVD approximation.
-     * * 5: fused_activation_function.
-     *      An (optional) ActivationFunctionType indicating the activation function.
+     * * 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.
+     * * 0: state (out).
      *      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].
@@ -930,17 +1203,20 @@
     /**
      * Computes hyperbolic tangent of input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = tanh(input)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     TANH = 28,
 
@@ -967,8 +1243,8 @@
  */
 enum OperandLifeTime : int32_t {
     /**
-     * The operand is internal to the model.  It's created by an operation
-     * and consumed by other operations.
+     * The operand is internal to the model. It's created by an operation and
+     * consumed by other operations.
      */
     TEMPORARY_VARIABLE,
 
@@ -1112,7 +1388,7 @@
     /**
      * Where to find the data for this operand.
      * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or NO_VALUE:
-     * - All the fields will be 0.
+     * - All the fields must be 0.
      * If the lifetime is CONSTANT_COPY:
      * - location.poolIndex is 0.
      * - location.offset is the offset in bytes into Model.operandValues.
@@ -1220,7 +1496,7 @@
      * 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.
+     * 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.
      */
diff --git a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
index 4f9d528..ed1fb94 100644
--- a/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
+++ b/neuralnetworks/1.0/vts/functional/GeneratedTestHarness.cpp
@@ -242,8 +242,8 @@
     // launch prepare model
     sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
     ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
+    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_1(
+        model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
     ASSERT_TRUE(prepareLaunchStatus.isOk());
     ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
 
diff --git a/neuralnetworks/1.1/IDevice.hal b/neuralnetworks/1.1/IDevice.hal
index d2c4843..1335bde 100644
--- a/neuralnetworks/1.1/IDevice.hal
+++ b/neuralnetworks/1.1/IDevice.hal
@@ -102,6 +102,8 @@
      * Multiple threads can call prepareModel on the same model concurrently.
      *
      * @param model The model to be prepared for execution.
+     * @param preference Indicates the intended execution behavior of a prepared
+     *                   model.
      * @param callback A callback object used to return the error status of
      *                 preparing the model for execution and the prepared model
      *                 if successful, nullptr otherwise. The callback object's
@@ -115,6 +117,7 @@
      *                - INVALID_ARGUMENT if one of the input arguments is
      *                  invalid
      */
-    prepareModel_1_1(Model model, IPreparedModelCallback callback)
+    prepareModel_1_1(Model model, ExecutionPreference preference,
+                     IPreparedModelCallback callback)
           generates (ErrorStatus status);
 };
diff --git a/neuralnetworks/1.1/types.hal b/neuralnetworks/1.1/types.hal
index 1d470d6..8290fbb 100644
--- a/neuralnetworks/1.1/types.hal
+++ b/neuralnetworks/1.1/types.hal
@@ -27,25 +27,24 @@
  */
 enum OperationType : @1.0::OperationType {
     /**
-     * BatchToSpace for N-D tensors.
+     * BatchToSpace for N-dimensional tensors.
      *
-     * This operation reshapes the "batch" dimension 0 into M + 1 dimensions of shape
+     * 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.
-     * The spatial dimensions of this intermediate result are then optionally cropped
-     * according to the amount to crop to produce the output.
+     *
      * This is the reverse of SpaceToBatch.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
-     * Supported tensor rank: up to 4
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
+     * 0: An n-D tensor, specifying the tensor to be reshaped
      * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
      *    input tensor. All values must be >= 1.
-     * 2: A 1-D Tensor of type TENSOR_INT32, the amount to crop for each spatial diemension of the
-     *    input tensor. All values must be >= 0.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -53,9 +52,9 @@
     BATCH_TO_SPACE_ND = 29,
 
     /**
-     * Divides the second tensor from the first tensor, element-wise.
+     * Element-wise division of two tensors.
      *
-     * Takes two input tensors of identical OperandType and compatible dimensions. The output
+     * Takes two input tensors of identical type and compatible dimensions. The output
      * is the result of dividing the first input tensor by the second, optionally
      * modified by an activation function.
      *
@@ -71,7 +70,9 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
@@ -88,15 +89,17 @@
     /**
      * Computes the mean of elements across dimensions of a tensor.
      *
-     * Reduces input tensor along the dimensions given in axis. 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.
+     * 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.
      *
-     * If axis has no entries, all dimensions are reduced, and a tensor with a single
-     * element is returned.
+     * If dimensions to reduce have no entries, all dimensions are reduced, and a tensor with
+     * a single element is returned.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
@@ -115,14 +118,18 @@
      *
      * This operation pads a tensor according to the specified paddings.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
-     * 1: A 2-D Tensor of type TENSOR_INT32. The paddings, before and after for each spatial dimension
-     *    of the input tensor.
+     * 0: An n-D tensor, specifying the tensor to be padded.
+     * 1: A 2-D Tensor of type 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 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.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -130,7 +137,7 @@
     PAD = 32,
 
     /**
-     * SpaceToBatch for N-D tensors.
+     * 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
@@ -139,16 +146,20 @@
      * batch position. Prior to division into blocks, the spatial dimensions of the input are
      * optionally zero padded according to paddings.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
-     * Supported tensor rank: up to 4
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4
      *
      * Inputs:
      * 0: An n-D tensor, specifying the input.
      * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
      *    input tensor. All values must be >= 1.
      * 2: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial diemension of the
-     *    input tensor. All values must be >= 0.
+     *    input tensor. All values must be >= 0. The shape of the tensor must be {rank(input0), 2}.
+     *    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.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -160,17 +171,20 @@
      *
      * Given a tensor input, this operation returns a tensor of the same type 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 axis.
+     * you can remove specific size 1 dimensions by specifying the axes (input1).
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
-     * 1: An 1-D Tensor of type TENSOR_INT32. The dimensions to squeeze. If None (the default),
-     *    squeezes all dimensions. If specified, only squeezes the dimensions listed. The dimension
-     *    index starts at 0. It is an error to squeeze a dimension that is not 1.
+     * 0: An n-D tensor, the tensor to be squeezed.
+     * 1: An optional 1-D tensor of type 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 type as input0. Contains the same data as input, but has one or more
@@ -181,23 +195,25 @@
     /**
      * Extracts a strided slice of a tensor.
      *
-     * 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
+     * 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 types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
+     * 0: An n-D tensor, specifying the tensor to be sliced.
      * 1: A 1-D Tensor of type TENSOR_INT32, the starts of the dimensions of the input
-     *    tensor to be sliced.
+     *    tensor to be sliced. The length must be of rank(input0).
      * 2: A 1-D Tensor of type TENSOR_INT32, the ends of the dimensions of the input
-     *    tensor to be sliced.
+     *    tensor to be sliced. The length must be of rank(input0).
      * 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input
-     *    tensor to be sliced.
+     *    tensor to be sliced. The length must be of rank(input0).
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -205,7 +221,7 @@
     STRIDED_SLICE = 35,
 
     /**
-     * Subtracts the second tensor from the first tensor, element-wise.
+     * Element-wise subtraction of two tensors.
      *
      * Takes two input tensors of identical type and compatible dimensions. The output
      * is the result of subtracting the second input tensor from the first one, optionally
@@ -223,7 +239,9 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
@@ -240,18 +258,20 @@
     /**
      * Transposes the input tensor, permuting the dimensions according to the perm tensor.
      *
-     * The returned tensor's dimension i must correspond to the input dimension perm[i].
+     * 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 types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
-     * 1: A 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the input
-     *    tensor.
+     * 0: An n-D tensor, specifying the tensor to be transposed.
+     * 1: An optional 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the
+     *    input tensor.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -362,3 +382,24 @@
      */
     bool relaxComputationFloat32toFloat16;
 };
+
+/**
+ * Execution preferences.
+ */
+enum ExecutionPreference : int32_t {
+    /**
+     * Prefer executing in a way that minimizes battery drain.
+     * This is desirable for compilations that will be executed often.
+     */
+    LOW_POWER = 0,
+    /**
+     * Prefer returning a single answer as fast as possible, even if this causes
+     * more power consumption.
+     */
+    FAST_SINGLE_ANSWER = 1,
+    /**
+     * Prefer maximizing the throughput of successive frames, for example when
+     * processing successive frames coming from the camera.
+     */
+    SUSTAINED_SPEED = 2,
+};
diff --git a/neuralnetworks/1.1/vts/functional/ValidateModel.cpp b/neuralnetworks/1.1/vts/functional/ValidateModel.cpp
index 7a20e26..3aa55f8 100644
--- a/neuralnetworks/1.1/vts/functional/ValidateModel.cpp
+++ b/neuralnetworks/1.1/vts/functional/ValidateModel.cpp
@@ -50,13 +50,13 @@
 }
 
 static void validatePrepareModel(const sp<IDevice>& device, const std::string& message,
-                                 const V1_1::Model& model) {
+                                 const V1_1::Model& model, ExecutionPreference preference) {
     SCOPED_TRACE(message + " [prepareModel_1_1]");
 
     sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
     ASSERT_NE(nullptr, preparedModelCallback.get());
     Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
+        device->prepareModel_1_1(model, preference, preparedModelCallback);
     ASSERT_TRUE(prepareLaunchStatus.isOk());
     ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
 
@@ -67,15 +67,24 @@
     ASSERT_EQ(nullptr, preparedModel.get());
 }
 
+static bool validExecutionPreference(ExecutionPreference preference) {
+    return preference == ExecutionPreference::LOW_POWER ||
+           preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
+           preference == ExecutionPreference::SUSTAINED_SPEED;
+}
+
 // Primary validation function. This function will take a valid model, apply a
 // mutation to it to invalidate the model, then pass it to interface calls that
 // use the model. Note that the model here is passed by value, and any mutation
 // to the model does not leave this function.
 static void validate(const sp<IDevice>& device, const std::string& message, V1_1::Model model,
-                     const std::function<void(Model*)>& mutation) {
+                     const std::function<void(Model*)>& mutation,
+                     ExecutionPreference preference = ExecutionPreference::FAST_SINGLE_ANSWER) {
     mutation(&model);
-    validateGetSupportedOperations(device, message, model);
-    validatePrepareModel(device, message, model);
+    if (validExecutionPreference(preference)) {
+        validateGetSupportedOperations(device, message, model);
+    }
+    validatePrepareModel(device, message, model, preference);
 }
 
 // Delete element from hidl_vec. hidl_vec doesn't support a "remove" operation,
@@ -486,6 +495,22 @@
     }
 }
 
+///////////////////////// VALIDATE EXECUTION PREFERENCE /////////////////////////
+
+static const int32_t invalidExecutionPreferences[] = {
+    static_cast<int32_t>(ExecutionPreference::LOW_POWER) - 1,        // lower bound
+    static_cast<int32_t>(ExecutionPreference::SUSTAINED_SPEED) + 1,  // upper bound
+};
+
+static void mutateExecutionPreferenceTest(const sp<IDevice>& device, const V1_1::Model& model) {
+    for (int32_t preference : invalidExecutionPreferences) {
+        const std::string message =
+            "mutateExecutionPreferenceTest: preference " + std::to_string(preference);
+        validate(device, message, model, [](Model*) {},
+                 static_cast<ExecutionPreference>(preference));
+    }
+}
+
 ////////////////////////// ENTRY POINT //////////////////////////////
 
 void ValidationTest::validateModel(const V1_1::Model& model) {
@@ -503,6 +528,7 @@
     removeOperationOutputTest(device, model);
     addOperationInputTest(device, model);
     addOperationOutputTest(device, model);
+    mutateExecutionPreferenceTest(device, model);
 }
 
 }  // namespace functional
diff --git a/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp b/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp
index bd96614..b42f561 100644
--- a/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp
+++ b/neuralnetworks/1.1/vts/functional/ValidateRequest.cpp
@@ -60,8 +60,8 @@
     // launch prepare model
     sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
     ASSERT_NE(nullptr, preparedModelCallback.get());
-    Return<ErrorStatus> prepareLaunchStatus =
-        device->prepareModel_1_1(model, preparedModelCallback);
+    Return<ErrorStatus> prepareLaunchStatus = device->prepareModel_1_1(
+        model, ExecutionPreference::FAST_SINGLE_ANSWER, preparedModelCallback);
     ASSERT_TRUE(prepareLaunchStatus.isOk());
     ASSERT_EQ(ErrorStatus::NONE, static_cast<ErrorStatus>(prepareLaunchStatus));
 
diff --git a/radio/1.2/vts/functional/radio_hidl_hal_api.cpp b/radio/1.2/vts/functional/radio_hidl_hal_api.cpp
index ee130f8..0febd38 100644
--- a/radio/1.2/vts/functional/radio_hidl_hal_api.cpp
+++ b/radio/1.2/vts/functional/radio_hidl_hal_api.cpp
@@ -30,10 +30,8 @@
         .geranBands = {GeranBands::BAND_450, GeranBands::BAND_480},
         .channels = {1,2}};
 
-    V1_2::NetworkScanRequest request = {
-        .type = ScanType::ONE_SHOT,
-        .interval = 60,
-        .specifiers = {specifier}};
+    ::android::hardware::radio::V1_2::NetworkScanRequest request = {
+        .type = ScanType::ONE_SHOT, .interval = 60, .specifiers = {specifier}};
 
     Return<void> res = radio_v1_2->startNetworkScan_1_2(serial, request);
     ASSERT_OK(res);
@@ -42,9 +40,9 @@
     EXPECT_EQ(serial, radioRsp_v1_2->rspInfo.serial);
 
     ALOGI("startNetworkScan, rspInfo.error = %s\n", toString(radioRsp_v1_2->rspInfo.error).c_str());
-    if (cardStatus.cardState == CardState::ABSENT) {
+    if (cardStatus.base.cardState == CardState::ABSENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error, {RadioError::SIM_ABSENT}));
-    } else if (cardStatus.cardState == CardState::PRESENT) {
+    } else if (cardStatus.base.cardState == CardState::PRESENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error, {RadioError::NONE}));
     }
 }
@@ -55,9 +53,8 @@
 TEST_F(RadioHidlTest_v1_2, startNetworkScan_InvalidArgument) {
     const int serial = GetRandomSerialNumber();
 
-    V1_2::NetworkScanRequest request = {
-        .type = ScanType::ONE_SHOT,
-        .interval = 60};
+    ::android::hardware::radio::V1_2::NetworkScanRequest request = {.type = ScanType::ONE_SHOT,
+                                                                    .interval = 60};
 
     Return<void> res = radio_v1_2->startNetworkScan_1_2(serial, request);
     ASSERT_OK(res);
@@ -67,10 +64,10 @@
 
     ALOGI("startNetworkScan_InvalidArgument, rspInfo.error = %s\n",
           toString(radioRsp_v1_2->rspInfo.error).c_str());
-    if (cardStatus.cardState == CardState::ABSENT) {
+    if (cardStatus.base.cardState == CardState::ABSENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error,
                                      {RadioError::SIM_ABSENT, RadioError::INVALID_ARGUMENTS}));
-    } else if (cardStatus.cardState == CardState::PRESENT) {
+    } else if (cardStatus.base.cardState == CardState::PRESENT) {
         ASSERT_TRUE(
             CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error, {RadioError::INVALID_ARGUMENTS}));
     }
@@ -87,7 +84,7 @@
         .geranBands = {GeranBands::BAND_450, GeranBands::BAND_480},
         .channels = {1,2}};
 
-    V1_2::NetworkScanRequest request = {
+    ::android::hardware::radio::V1_2::NetworkScanRequest request = {
         .type = ScanType::ONE_SHOT,
         .interval = 4,
         .specifiers = {specifier},
@@ -103,10 +100,10 @@
 
     ALOGI("startNetworkScan_InvalidInterval1, rspInfo.error = %s\n",
           toString(radioRsp_v1_2->rspInfo.error).c_str());
-    if (cardStatus.cardState == CardState::ABSENT) {
+    if (cardStatus.base.cardState == CardState::ABSENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error,
                                      {RadioError::SIM_ABSENT, RadioError::INVALID_ARGUMENTS}));
-    } else if (cardStatus.cardState == CardState::PRESENT) {
+    } else if (cardStatus.base.cardState == CardState::PRESENT) {
         ASSERT_TRUE(
             CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error, {RadioError::INVALID_ARGUMENTS}));
     }
@@ -123,7 +120,7 @@
         .geranBands = {GeranBands::BAND_450, GeranBands::BAND_480},
         .channels = {1,2}};
 
-    V1_2::NetworkScanRequest request = {
+    ::android::hardware::radio::V1_2::NetworkScanRequest request = {
         .type = ScanType::ONE_SHOT,
         .interval = 301,
         .specifiers = {specifier},
@@ -139,10 +136,10 @@
 
     ALOGI("startNetworkScan_InvalidInterval2, rspInfo.error = %s\n",
           toString(radioRsp_v1_2->rspInfo.error).c_str());
-    if (cardStatus.cardState == CardState::ABSENT) {
+    if (cardStatus.base.cardState == CardState::ABSENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error,
                                      {RadioError::SIM_ABSENT, RadioError::INVALID_ARGUMENTS}));
-    } else if (cardStatus.cardState == CardState::PRESENT) {
+    } else if (cardStatus.base.cardState == CardState::PRESENT) {
         ASSERT_TRUE(
             CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error, {RadioError::INVALID_ARGUMENTS}));
     }
@@ -159,7 +156,7 @@
         .geranBands = {GeranBands::BAND_450, GeranBands::BAND_480},
         .channels = {1,2}};
 
-    V1_2::NetworkScanRequest request = {
+    ::android::hardware::radio::V1_2::NetworkScanRequest request = {
         .type = ScanType::ONE_SHOT,
         .interval = 60,
         .specifiers = {specifier},
@@ -175,10 +172,10 @@
 
     ALOGI("startNetworkScan_InvalidMaxSearchTime1, rspInfo.error = %s\n",
           toString(radioRsp_v1_2->rspInfo.error).c_str());
-    if (cardStatus.cardState == CardState::ABSENT) {
+    if (cardStatus.base.cardState == CardState::ABSENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error,
                                      {RadioError::SIM_ABSENT, RadioError::INVALID_ARGUMENTS}));
-    } else if (cardStatus.cardState == CardState::PRESENT) {
+    } else if (cardStatus.base.cardState == CardState::PRESENT) {
         ASSERT_TRUE(
             CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error, {RadioError::INVALID_ARGUMENTS}));
     }
@@ -195,7 +192,7 @@
         .geranBands = {GeranBands::BAND_450, GeranBands::BAND_480},
         .channels = {1,2}};
 
-    V1_2::NetworkScanRequest request = {
+    ::android::hardware::radio::V1_2::NetworkScanRequest request = {
         .type = ScanType::ONE_SHOT,
         .interval = 60,
         .specifiers = {specifier},
@@ -211,10 +208,10 @@
 
     ALOGI("startNetworkScan_InvalidMaxSearchTime2, rspInfo.error = %s\n",
           toString(radioRsp_v1_2->rspInfo.error).c_str());
-    if (cardStatus.cardState == CardState::ABSENT) {
+    if (cardStatus.base.cardState == CardState::ABSENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error,
                                      {RadioError::SIM_ABSENT, RadioError::INVALID_ARGUMENTS}));
-    } else if (cardStatus.cardState == CardState::PRESENT) {
+    } else if (cardStatus.base.cardState == CardState::PRESENT) {
         ASSERT_TRUE(
             CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error, {RadioError::INVALID_ARGUMENTS}));
     }
@@ -231,7 +228,7 @@
         .geranBands = {GeranBands::BAND_450, GeranBands::BAND_480},
         .channels = {1,2}};
 
-    V1_2::NetworkScanRequest request = {
+    ::android::hardware::radio::V1_2::NetworkScanRequest request = {
         .type = ScanType::ONE_SHOT,
         .interval = 60,
         .specifiers = {specifier},
@@ -247,10 +244,10 @@
 
     ALOGI("startNetworkScan_InvalidPeriodicity1, rspInfo.error = %s\n",
           toString(radioRsp_v1_2->rspInfo.error).c_str());
-    if (cardStatus.cardState == CardState::ABSENT) {
+    if (cardStatus.base.cardState == CardState::ABSENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error,
                                      {RadioError::SIM_ABSENT, RadioError::INVALID_ARGUMENTS}));
-    } else if (cardStatus.cardState == CardState::PRESENT) {
+    } else if (cardStatus.base.cardState == CardState::PRESENT) {
         ASSERT_TRUE(
             CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error, {RadioError::INVALID_ARGUMENTS}));
     }
@@ -267,7 +264,7 @@
         .geranBands = {GeranBands::BAND_450, GeranBands::BAND_480},
         .channels = {1,2}};
 
-    V1_2::NetworkScanRequest request = {
+    ::android::hardware::radio::V1_2::NetworkScanRequest request = {
         .type = ScanType::ONE_SHOT,
         .interval = 60,
         .specifiers = {specifier},
@@ -283,10 +280,10 @@
 
     ALOGI("startNetworkScan_InvalidPeriodicity2, rspInfo.error = %s\n",
           toString(radioRsp_v1_2->rspInfo.error).c_str());
-    if (cardStatus.cardState == CardState::ABSENT) {
+    if (cardStatus.base.cardState == CardState::ABSENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error,
                                      {RadioError::SIM_ABSENT, RadioError::INVALID_ARGUMENTS}));
-    } else if (cardStatus.cardState == CardState::PRESENT) {
+    } else if (cardStatus.base.cardState == CardState::PRESENT) {
         ASSERT_TRUE(
             CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error, {RadioError::INVALID_ARGUMENTS}));
     }
@@ -303,7 +300,7 @@
         .geranBands = {GeranBands::BAND_450, GeranBands::BAND_480},
         .channels = {1,2}};
 
-    V1_2::NetworkScanRequest request = {
+    ::android::hardware::radio::V1_2::NetworkScanRequest request = {
         .type = ScanType::ONE_SHOT,
         .interval = 60,
         .specifiers = {specifier},
@@ -319,10 +316,10 @@
 
     ALOGI("startNetworkScan_InvalidArgument, rspInfo.error = %s\n",
           toString(radioRsp_v1_2->rspInfo.error).c_str());
-    if (cardStatus.cardState == CardState::ABSENT) {
+    if (cardStatus.base.cardState == CardState::ABSENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error,
                                      {RadioError::NONE, RadioError::SIM_ABSENT}));
-    } else if (cardStatus.cardState == CardState::PRESENT) {
+    } else if (cardStatus.base.cardState == CardState::PRESENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error, {RadioError::NONE}));
     }
 }
@@ -338,7 +335,7 @@
         .geranBands = {GeranBands::BAND_450, GeranBands::BAND_480},
         .channels = {1,2}};
 
-    V1_2::NetworkScanRequest request = {
+    ::android::hardware::radio::V1_2::NetworkScanRequest request = {
         .type = ScanType::ONE_SHOT,
         .interval = 60,
         .specifiers = {specifier},
@@ -355,10 +352,10 @@
 
     ALOGI("startNetworkScan_InvalidArgument, rspInfo.error = %s\n",
           toString(radioRsp_v1_2->rspInfo.error).c_str());
-    if (cardStatus.cardState == CardState::ABSENT) {
+    if (cardStatus.base.cardState == CardState::ABSENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error,
                                      {RadioError::NONE, RadioError::SIM_ABSENT}));
-    } else if (cardStatus.cardState == CardState::PRESENT) {
+    } else if (cardStatus.base.cardState == CardState::PRESENT) {
         ASSERT_TRUE(CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error, {RadioError::NONE}));
     }
 }
@@ -369,8 +366,8 @@
 TEST_F(RadioHidlTest_v1_2, setIndicationFilter_1_2) {
     const int serial = GetRandomSerialNumber();
 
-    Return<void> res =
-        radio_v1_2->setIndicationFilter_1_2(serial, static_cast<int>(IndicationFilter::ALL));
+    Return<void> res = radio_v1_2->setIndicationFilter_1_2(
+        serial, static_cast<int>(::android::hardware::radio::V1_2::IndicationFilter::ALL));
     ASSERT_OK(res);
     EXPECT_EQ(std::cv_status::no_timeout, wait());
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
@@ -390,7 +387,7 @@
     Return<void> res = radio_v1_2->setSignalStrengthReportingCriteria(
         serial, 5000,
         10,  // hysteresisDb too large given threshold list deltas
-        {-109, -103, -97, -89}, V1_2::AccessNetwork::GERAN);
+        {-109, -103, -97, -89}, ::android::hardware::radio::V1_2::AccessNetwork::GERAN);
     ASSERT_OK(res);
     EXPECT_EQ(std::cv_status::no_timeout, wait());
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
@@ -407,8 +404,8 @@
 TEST_F(RadioHidlTest_v1_2, setSignalStrengthReportingCriteria_EmptyParams) {
     const int serial = GetRandomSerialNumber();
 
-    Return<void> res = radio_v1_2->setSignalStrengthReportingCriteria(serial, 0, 0, {},
-                                                                      V1_2::AccessNetwork::GERAN);
+    Return<void> res = radio_v1_2->setSignalStrengthReportingCriteria(
+        serial, 0, 0, {}, ::android::hardware::radio::V1_2::AccessNetwork::GERAN);
     ASSERT_OK(res);
     EXPECT_EQ(std::cv_status::no_timeout, wait());
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
@@ -426,7 +423,8 @@
     const int serial = GetRandomSerialNumber();
 
     Return<void> res = radio_v1_2->setSignalStrengthReportingCriteria(
-        serial, 5000, 2, {-109, -103, -97, -89}, V1_2::AccessNetwork::GERAN);
+        serial, 5000, 2, {-109, -103, -97, -89},
+        ::android::hardware::radio::V1_2::AccessNetwork::GERAN);
     ASSERT_OK(res);
     EXPECT_EQ(std::cv_status::no_timeout, wait());
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
@@ -444,7 +442,8 @@
     const int serial = GetRandomSerialNumber();
 
     Return<void> res = radio_v1_2->setSignalStrengthReportingCriteria(
-        serial, 5000, 2, {-110, -97, -73, -49, -25}, V1_2::AccessNetwork::UTRAN);
+        serial, 5000, 2, {-110, -97, -73, -49, -25},
+        ::android::hardware::radio::V1_2::AccessNetwork::UTRAN);
     ASSERT_OK(res);
     EXPECT_EQ(std::cv_status::no_timeout, wait());
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
@@ -462,7 +461,8 @@
     const int serial = GetRandomSerialNumber();
 
     Return<void> res = radio_v1_2->setSignalStrengthReportingCriteria(
-        serial, 5000, 2, {-140, -128, -118, -108, -98, -44}, V1_2::AccessNetwork::EUTRAN);
+        serial, 5000, 2, {-140, -128, -118, -108, -98, -44},
+        ::android::hardware::radio::V1_2::AccessNetwork::EUTRAN);
     ASSERT_OK(res);
     EXPECT_EQ(std::cv_status::no_timeout, wait());
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
@@ -480,7 +480,8 @@
     const int serial = GetRandomSerialNumber();
 
     Return<void> res = radio_v1_2->setSignalStrengthReportingCriteria(
-        serial, 5000, 2, {-105, -90, -75, -65}, V1_2::AccessNetwork::CDMA2000);
+        serial, 5000, 2, {-105, -90, -75, -65},
+        ::android::hardware::radio::V1_2::AccessNetwork::CDMA2000);
     ASSERT_OK(res);
     EXPECT_EQ(std::cv_status::no_timeout, wait());
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
@@ -500,7 +501,8 @@
     Return<void> res = radio_v1_2->setLinkCapacityReportingCriteria(
         serial, 5000,
         5000,  // hysteresisDlKbps too big for thresholds delta
-        100, {1000, 5000, 10000, 20000}, {500, 1000, 5000, 10000}, V1_2::AccessNetwork::GERAN);
+        100, {1000, 5000, 10000, 20000}, {500, 1000, 5000, 10000},
+        ::android::hardware::radio::V1_2::AccessNetwork::GERAN);
     ASSERT_OK(res);
     EXPECT_EQ(std::cv_status::no_timeout, wait());
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
@@ -520,7 +522,8 @@
     Return<void> res = radio_v1_2->setLinkCapacityReportingCriteria(
         serial, 5000, 500,
         1000,  // hysteresisUlKbps too big for thresholds delta
-        {1000, 5000, 10000, 20000}, {500, 1000, 5000, 10000}, V1_2::AccessNetwork::GERAN);
+        {1000, 5000, 10000, 20000}, {500, 1000, 5000, 10000},
+        ::android::hardware::radio::V1_2::AccessNetwork::GERAN);
     ASSERT_OK(res);
     EXPECT_EQ(std::cv_status::no_timeout, wait());
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
@@ -537,8 +540,8 @@
 TEST_F(RadioHidlTest_v1_2, setLinkCapacityReportingCriteria_emptyParams) {
     const int serial = GetRandomSerialNumber();
 
-    Return<void> res = radio_v1_2->setLinkCapacityReportingCriteria(serial, 0, 0, 0, {}, {},
-                                                                    V1_2::AccessNetwork::GERAN);
+    Return<void> res = radio_v1_2->setLinkCapacityReportingCriteria(
+        serial, 0, 0, 0, {}, {}, ::android::hardware::radio::V1_2::AccessNetwork::GERAN);
     ASSERT_OK(res);
     EXPECT_EQ(std::cv_status::no_timeout, wait());
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
@@ -557,7 +560,7 @@
 
     Return<void> res = radio_v1_2->setLinkCapacityReportingCriteria(
         serial, 5000, 500, 100, {1000, 5000, 10000, 20000}, {500, 1000, 5000, 10000},
-        V1_2::AccessNetwork::GERAN);
+        ::android::hardware::radio::V1_2::AccessNetwork::GERAN);
     ASSERT_OK(res);
     EXPECT_EQ(std::cv_status::no_timeout, wait());
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
@@ -574,7 +577,8 @@
 TEST_F(RadioHidlTest_v1_2, setupDataCall_1_2) {
     const int serial = GetRandomSerialNumber();
 
-    V1_2::AccessNetwork accessNetwork = V1_2::AccessNetwork::EUTRAN;
+    ::android::hardware::radio::V1_2::AccessNetwork accessNetwork =
+        ::android::hardware::radio::V1_2::AccessNetwork::EUTRAN;
 
     DataProfileInfo dataProfileInfo;
     memset(&dataProfileInfo, 0, sizeof(dataProfileInfo));
@@ -600,7 +604,8 @@
     bool roamingAllowed = false;
     bool isRoaming = false;
 
-    V1_2::DataRequestReason reason = V1_2::DataRequestReason::NORMAL;
+    ::android::hardware::radio::V1_2::DataRequestReason reason =
+        ::android::hardware::radio::V1_2::DataRequestReason::NORMAL;
     std::vector<hidl_string> addresses = {""};
     std::vector<hidl_string> dnses = {""};
 
@@ -613,12 +618,12 @@
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
     EXPECT_EQ(serial, radioRsp_v1_2->rspInfo.serial);
 
-    if (cardStatus.cardState == CardState::ABSENT) {
+    if (cardStatus.base.cardState == CardState::ABSENT) {
         ASSERT_TRUE(CheckAnyOfErrors(
             radioRsp_v1_2->rspInfo.error,
             {RadioError::SIM_ABSENT, RadioError::RADIO_NOT_AVAILABLE, RadioError::INVALID_ARGUMENTS,
              RadioError::OP_NOT_ALLOWED_BEFORE_REG_TO_NW, RadioError::REQUEST_NOT_SUPPORTED}));
-    } else if (cardStatus.cardState == CardState::PRESENT) {
+    } else if (cardStatus.base.cardState == CardState::PRESENT) {
         ASSERT_TRUE(CheckAnyOfErrors(
             radioRsp_v1_2->rspInfo.error,
             {RadioError::NONE, RadioError::RADIO_NOT_AVAILABLE, RadioError::INVALID_ARGUMENTS,
@@ -632,7 +637,8 @@
 TEST_F(RadioHidlTest_v1_2, deactivateDataCall_1_2) {
     const int serial = GetRandomSerialNumber();
     int cid = 1;
-    V1_2::DataRequestReason reason = V1_2::DataRequestReason::NORMAL;
+    ::android::hardware::radio::V1_2::DataRequestReason reason =
+        ::android::hardware::radio::V1_2::DataRequestReason::NORMAL;
 
     Return<void> res = radio_v1_2->deactivateDataCall_1_2(serial, cid, reason);
     ASSERT_OK(res);
@@ -641,13 +647,13 @@
     EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
     EXPECT_EQ(serial, radioRsp_v1_2->rspInfo.serial);
 
-    if (cardStatus.cardState == CardState::ABSENT) {
+    if (cardStatus.base.cardState == CardState::ABSENT) {
         ASSERT_TRUE(CheckAnyOfErrors(
             radioRsp_v1_2->rspInfo.error,
             {RadioError::NONE, RadioError::RADIO_NOT_AVAILABLE, RadioError::INVALID_CALL_ID,
              RadioError::INVALID_STATE, RadioError::INVALID_ARGUMENTS,
              RadioError::REQUEST_NOT_SUPPORTED, RadioError::CANCELLED, RadioError::SIM_ABSENT}));
-    } else if (cardStatus.cardState == CardState::PRESENT) {
+    } else if (cardStatus.base.cardState == CardState::PRESENT) {
         ASSERT_TRUE(CheckAnyOfErrors(
             radioRsp_v1_2->rspInfo.error,
             {RadioError::NONE, RadioError::RADIO_NOT_AVAILABLE, RadioError::INVALID_CALL_ID,
@@ -710,3 +716,33 @@
         radioRsp_v1_2->rspInfo.error,
         {RadioError::NONE, RadioError::RADIO_NOT_AVAILABLE, RadioError::NOT_PROVISIONED}));
 }
+
+/*
+ * Test IRadio.getAvailableBandModes() for the response returned.
+ */
+TEST_F(RadioHidlTest_v1_2, getAvailableBandModes) {
+    int serial = GetRandomSerialNumber();
+
+    Return<void> res = radio_v1_2->getAvailableBandModes(serial);
+    ASSERT_OK(res);
+    EXPECT_EQ(std::cv_status::no_timeout, wait());
+    EXPECT_EQ(RadioResponseType::SOLICITED, radioRsp_v1_2->rspInfo.type);
+    EXPECT_EQ(serial, radioRsp_v1_2->rspInfo.serial);
+    ALOGI("getAvailableBandModes, rspInfo.error = %s\n",
+          toString(radioRsp_v1_2->rspInfo.error).c_str());
+    ASSERT_TRUE(
+        CheckAnyOfErrors(radioRsp_v1_2->rspInfo.error,
+                         {RadioError::NONE, RadioError::RADIO_NOT_AVAILABLE, RadioError::MODEM_ERR,
+                          RadioError::INTERNAL_ERR,
+                          // If REQUEST_NOT_SUPPORTED is returned, then it should also be returned
+                          // for setRandMode().
+                          RadioError::REQUEST_NOT_SUPPORTED}));
+    bool hasUnspecifiedBandMode = false;
+    if (radioRsp_v1_2->rspInfo.error == RadioError::NONE) {
+        for (const RadioBandMode& mode : radioRsp_v1_2->radioBandModes) {
+            // Automatic mode selection must be supported
+            if (mode == RadioBandMode::BAND_MODE_UNSPECIFIED) hasUnspecifiedBandMode = true;
+        }
+        ASSERT_TRUE(hasUnspecifiedBandMode);
+    }
+}
diff --git a/radio/1.2/vts/functional/radio_hidl_hal_test.cpp b/radio/1.2/vts/functional/radio_hidl_hal_test.cpp
index d74d077..edac1aa 100644
--- a/radio/1.2/vts/functional/radio_hidl_hal_test.cpp
+++ b/radio/1.2/vts/functional/radio_hidl_hal_test.cpp
@@ -17,14 +17,18 @@
 #include <radio_hidl_hal_utils_v1_2.h>
 
 void RadioHidlTest_v1_2::SetUp() {
-    radio_v1_2 = ::testing::VtsHalHidlTargetTestBase::getService<V1_2::IRadio>(
-        RadioHidlEnvironment::Instance()->getServiceName<V1_2::IRadio>(
-            hidl_string(RADIO_SERVICE_NAME)));
+    radio_v1_2 =
+        ::testing::VtsHalHidlTargetTestBase::getService<::android::hardware::radio::V1_2::IRadio>(
+            RadioHidlEnvironment::Instance()
+                ->getServiceName<::android::hardware::radio::V1_2::IRadio>(
+                    hidl_string(RADIO_SERVICE_NAME)));
     if (radio_v1_2 == NULL) {
         sleep(60);
-        radio_v1_2 = ::testing::VtsHalHidlTargetTestBase::getService<V1_2::IRadio>(
-            RadioHidlEnvironment::Instance()->getServiceName<V1_2::IRadio>(
-                hidl_string(RADIO_SERVICE_NAME)));
+        radio_v1_2 = ::testing::VtsHalHidlTargetTestBase::getService<
+            ::android::hardware::radio::V1_2::IRadio>(
+            RadioHidlEnvironment::Instance()
+                ->getServiceName<::android::hardware::radio::V1_2::IRadio>(
+                    hidl_string(RADIO_SERVICE_NAME)));
     }
     ASSERT_NE(nullptr, radio_v1_2.get());
 
@@ -71,4 +75,4 @@
     }
     count_--;
     return status;
-}
+}
\ No newline at end of file
diff --git a/radio/1.2/vts/functional/radio_hidl_hal_utils_v1_2.h b/radio/1.2/vts/functional/radio_hidl_hal_utils_v1_2.h
index c61913c..2d0ea29 100644
--- a/radio/1.2/vts/functional/radio_hidl_hal_utils_v1_2.h
+++ b/radio/1.2/vts/functional/radio_hidl_hal_utils_v1_2.h
@@ -22,14 +22,14 @@
 #include <condition_variable>
 #include <mutex>
 
-#include <android/hardware/radio/1.1/IRadioIndication.h>
-#include <android/hardware/radio/1.1/IRadioResponse.h>
 #include <android/hardware/radio/1.2/IRadio.h>
+#include <android/hardware/radio/1.2/IRadioIndication.h>
+#include <android/hardware/radio/1.2/IRadioResponse.h>
 #include <android/hardware/radio/1.2/types.h>
 
 #include "vts_test_util.h"
 
-using namespace ::android::hardware::radio;
+using namespace ::android::hardware::radio::V1_2;
 using namespace ::android::hardware::radio::V1_1;
 using namespace ::android::hardware::radio::V1_0;
 
@@ -44,21 +44,24 @@
 #define RADIO_SERVICE_NAME "slot1"
 
 class RadioHidlTest_v1_2;
-extern CardStatus cardStatus;
+extern ::android::hardware::radio::V1_2::CardStatus cardStatus;
 
 /* Callback class for radio response v1_2*/
-class RadioResponse_v1_2 : public V1_1::IRadioResponse {
+class RadioResponse_v1_2 : public ::android::hardware::radio::V1_2::IRadioResponse {
    protected:
     RadioHidlTest_v1_2& parent_v1_2;
 
    public:
+    hidl_vec<RadioBandMode> radioBandModes;
+
     RadioResponseInfo rspInfo;
 
     RadioResponse_v1_2(RadioHidlTest_v1_2& parent_v1_2);
     virtual ~RadioResponse_v1_2() = default;
 
-    Return<void> getIccCardStatusResponse(const RadioResponseInfo& info,
-                                          const CardStatus& cardStatus);
+    Return<void> getIccCardStatusResponse(
+        const RadioResponseInfo& info,
+        const ::android::hardware::radio::V1_0::CardStatus& cardStatus);
 
     Return<void> supplyIccPinForAppResponse(const RadioResponseInfo& info,
                                             int32_t remainingRetries);
@@ -81,8 +84,9 @@
     Return<void> supplyNetworkDepersonalizationResponse(const RadioResponseInfo& info,
                                                         int32_t remainingRetries);
 
-    Return<void> getCurrentCallsResponse(const RadioResponseInfo& info,
-                                         const ::android::hardware::hidl_vec<Call>& calls);
+    Return<void> getCurrentCallsResponse(
+        const RadioResponseInfo& info,
+        const ::android::hardware::hidl_vec<::android::hardware::radio::V1_0::Call>& calls);
 
     Return<void> dialResponse(const RadioResponseInfo& info);
 
@@ -104,14 +108,17 @@
     Return<void> getLastCallFailCauseResponse(const RadioResponseInfo& info,
                                               const LastCallFailCauseInfo& failCauseInfo);
 
-    Return<void> getSignalStrengthResponse(const RadioResponseInfo& info,
-                                           const SignalStrength& sigStrength);
+    Return<void> getSignalStrengthResponse(
+        const RadioResponseInfo& info,
+        const ::android::hardware::radio::V1_0::SignalStrength& sigStrength);
 
-    Return<void> getVoiceRegistrationStateResponse(const RadioResponseInfo& info,
-                                                   const VoiceRegStateResult& voiceRegResponse);
+    Return<void> getVoiceRegistrationStateResponse(
+        const RadioResponseInfo& info,
+        const ::android::hardware::radio::V1_0::VoiceRegStateResult& voiceRegResponse);
 
-    Return<void> getDataRegistrationStateResponse(const RadioResponseInfo& info,
-                                                  const DataRegStateResult& dataRegResponse);
+    Return<void> getDataRegistrationStateResponse(
+        const RadioResponseInfo& info,
+        const ::android::hardware::radio::V1_0::DataRegStateResult& dataRegResponse);
 
     Return<void> getOperatorResponse(const RadioResponseInfo& info,
                                      const ::android::hardware::hidl_string& longName,
@@ -310,8 +317,9 @@
     Return<void> getVoiceRadioTechnologyResponse(const RadioResponseInfo& info,
                                                  RadioTechnology rat);
 
-    Return<void> getCellInfoListResponse(const RadioResponseInfo& info,
-                                         const ::android::hardware::hidl_vec<CellInfo>& cellInfo);
+    Return<void> getCellInfoListResponse(
+        const RadioResponseInfo& info,
+        const ::android::hardware::hidl_vec<::android::hardware::radio::V1_0::CellInfo>& cellInfo);
 
     Return<void> setCellInfoListRateResponse(const RadioResponseInfo& info);
 
@@ -406,27 +414,33 @@
 
     Return<void> setLinkCapacityReportingCriteriaResponse(const RadioResponseInfo& info);
 
-    Return<void> getIccCardStatusResponse_1_2(const RadioResponseInfo& info,
-                                              const CardStatus& card_status);
+    Return<void> getIccCardStatusResponse_1_2(
+        const RadioResponseInfo& info,
+        const ::android::hardware::radio::V1_2::CardStatus& card_status);
 
-    Return<void> getCurrentCallsResponse_1_2(const RadioResponseInfo& info,
-                                             const ::android::hardware::hidl_vec<Call>& calls);
+    Return<void> getCurrentCallsResponse_1_2(
+        const RadioResponseInfo& info,
+        const ::android::hardware::hidl_vec<::android::hardware::radio::V1_2::Call>& calls);
 
-    Return<void> getSignalStrengthResponse_1_2(const RadioResponseInfo& info,
-                                               const SignalStrength& sig_strength);
+    Return<void> getSignalStrengthResponse_1_2(
+        const RadioResponseInfo& info,
+        const ::android::hardware::radio::V1_2::SignalStrength& sig_strength);
 
     Return<void> getCellInfoListResponse_1_2(
-        const RadioResponseInfo& info, const ::android::hardware::hidl_vec<CellInfo>& cellInfo);
+        const RadioResponseInfo& info,
+        const ::android::hardware::hidl_vec<::android::hardware::radio::V1_2::CellInfo>& cellInfo);
 
     Return<void> getVoiceRegistrationStateResponse_1_2(
-        const RadioResponseInfo& info, const V1_2::VoiceRegStateResult& voiceRegResponse);
+        const RadioResponseInfo& info,
+        const ::android::hardware::radio::V1_2::VoiceRegStateResult& voiceRegResponse);
 
     Return<void> getDataRegistrationStateResponse_1_2(
-        const RadioResponseInfo& info, const V1_2::DataRegStateResult& dataRegResponse);
+        const RadioResponseInfo& info,
+        const ::android::hardware::radio::V1_2::DataRegStateResult& dataRegResponse);
 };
 
 /* Callback class for radio indication */
-class RadioIndication_v1_2 : public V1_1::IRadioIndication {
+class RadioIndication_v1_2 : public ::android::hardware::radio::V1_2::IRadioIndication {
    protected:
     RadioHidlTest_v1_2& parent_v1_2;
 
@@ -435,26 +449,33 @@
     virtual ~RadioIndication_v1_2() = default;
 
     /* 1.2 Api */
-    Return<void> networkScanResult_1_2(RadioIndicationType type,
-                                       const V1_2::NetworkScanResult& result);
+    Return<void> networkScanResult_1_2(
+        RadioIndicationType type,
+        const ::android::hardware::radio::V1_2::NetworkScanResult& result);
 
-    Return<void> cellInfoList_1_2(RadioIndicationType type,
-                                  const ::android::hardware::hidl_vec<V1_2::CellInfo>& records);
+    Return<void> cellInfoList_1_2(
+        RadioIndicationType type,
+        const ::android::hardware::hidl_vec<::android::hardware::radio::V1_2::CellInfo>& records);
 
-    Return<void> currentLinkCapacityEstimate(RadioIndicationType type,
-                                             const V1_2::LinkCapacityEstimate& lce);
+    Return<void> currentLinkCapacityEstimate(
+        RadioIndicationType type,
+        const ::android::hardware::radio::V1_2::LinkCapacityEstimate& lce);
 
     Return<void> currentPhysicalChannelConfigs(
         RadioIndicationType type,
-        const ::android::hardware::hidl_vec<V1_2::PhysicalChannelConfig>& configs);
+        const ::android::hardware::hidl_vec<
+            ::android::hardware::radio::V1_2::PhysicalChannelConfig>& configs);
 
-    Return<void> currentSignalStrength_1_2(RadioIndicationType type,
-                                           const V1_2::SignalStrength& signalStrength);
+    Return<void> currentSignalStrength_1_2(
+        RadioIndicationType type,
+        const ::android::hardware::radio::V1_2::SignalStrength& signalStrength);
 
     /* 1.1 Api */
     Return<void> carrierInfoForImsiEncryption(RadioIndicationType info);
 
-    Return<void> networkScanResult(RadioIndicationType type, const NetworkScanResult& result);
+    Return<void> networkScanResult(
+        RadioIndicationType type,
+        const ::android::hardware::radio::V1_1::NetworkScanResult& result);
 
     Return<void> keepaliveStatus(RadioIndicationType type, const KeepaliveStatus& status);
 
@@ -480,8 +501,9 @@
                                   const ::android::hardware::hidl_string& nitzTime,
                                   uint64_t receivedTime);
 
-    Return<void> currentSignalStrength(RadioIndicationType type,
-                                       const SignalStrength& signalStrength);
+    Return<void> currentSignalStrength(
+        RadioIndicationType type,
+        const ::android::hardware::radio::V1_0::SignalStrength& signalStrength);
 
     Return<void> dataCallListChanged(
         RadioIndicationType type, const ::android::hardware::hidl_vec<SetupDataCallResult>& dcList);
@@ -539,8 +561,9 @@
 
     Return<void> voiceRadioTechChanged(RadioIndicationType type, RadioTechnology rat);
 
-    Return<void> cellInfoList(RadioIndicationType type,
-                              const ::android::hardware::hidl_vec<CellInfo>& records);
+    Return<void> cellInfoList(
+        RadioIndicationType type,
+        const ::android::hardware::hidl_vec<::android::hardware::radio::V1_0::CellInfo>& records);
 
     Return<void> imsNetworkStateChanged(RadioIndicationType type);
 
@@ -575,7 +598,9 @@
         static RadioHidlEnvironment* instance = new RadioHidlEnvironment;
         return instance;
     }
-    virtual void registerTestServices() override { registerTestService<V1_2::IRadio>(); }
+    virtual void registerTestServices() override {
+        registerTestService<::android::hardware::radio::V1_2::IRadio>();
+    }
 
    private:
     RadioHidlEnvironment() {}
@@ -598,11 +623,11 @@
     std::cv_status wait();
 
     /* radio service handle */
-    sp<V1_2::IRadio> radio_v1_2;
+    sp<::android::hardware::radio::V1_2::IRadio> radio_v1_2;
 
     /* radio response handle */
     sp<RadioResponse_v1_2> radioRsp_v1_2;
 
     /* radio indication handle */
     sp<RadioIndication_v1_2> radioInd_v1_2;
-};
+};
\ No newline at end of file
diff --git a/radio/1.2/vts/functional/radio_indication.cpp b/radio/1.2/vts/functional/radio_indication.cpp
index 57f5cb0..eba9dc0 100644
--- a/radio/1.2/vts/functional/radio_indication.cpp
+++ b/radio/1.2/vts/functional/radio_indication.cpp
@@ -20,29 +20,33 @@
 
 /* 1.2 Apis */
 Return<void> RadioIndication_v1_2::networkScanResult_1_2(
-    RadioIndicationType /*type*/, const V1_2::NetworkScanResult& /*result*/) {
+    RadioIndicationType /*type*/,
+    const ::android::hardware::radio::V1_2::NetworkScanResult& /*result*/) {
     return Void();
 }
 
 Return<void> RadioIndication_v1_2::cellInfoList_1_2(
     RadioIndicationType /*type*/,
-    const ::android::hardware::hidl_vec<V1_2::CellInfo>& /*records*/) {
+    const ::android::hardware::hidl_vec<::android::hardware::radio::V1_2::CellInfo>& /*records*/) {
     return Void();
 }
 
 Return<void> RadioIndication_v1_2::currentLinkCapacityEstimate(
-    RadioIndicationType /*type*/, const V1_2::LinkCapacityEstimate& /*lce*/) {
+    RadioIndicationType /*type*/,
+    const ::android::hardware::radio::V1_2::LinkCapacityEstimate& /*lce*/) {
     return Void();
 }
 
 Return<void> RadioIndication_v1_2::currentPhysicalChannelConfigs(
     RadioIndicationType /*type*/,
-    const ::android::hardware::hidl_vec<V1_2::PhysicalChannelConfig>& /*configs*/) {
+    const ::android::hardware::hidl_vec<
+        ::android::hardware::radio::V1_2::PhysicalChannelConfig>& /*configs*/) {
     return Void();
 }
 
 Return<void> RadioIndication_v1_2::currentSignalStrength_1_2(
-    RadioIndicationType /*type*/, const V1_2::SignalStrength& /*signalStrength*/) {
+    RadioIndicationType /*type*/,
+    const ::android::hardware::radio::V1_2::SignalStrength& /*signalStrength*/) {
     return Void();
 }
 
@@ -51,8 +55,9 @@
     return Void();
 }
 
-Return<void> RadioIndication_v1_2::networkScanResult(RadioIndicationType /*type*/,
-                                                     const NetworkScanResult& /*result*/) {
+Return<void> RadioIndication_v1_2::networkScanResult(
+    RadioIndicationType /*type*/,
+    const ::android::hardware::radio::V1_1::NetworkScanResult& /*result*/) {
     return Void();
 }
 
@@ -101,8 +106,9 @@
     return Void();
 }
 
-Return<void> RadioIndication_v1_2::currentSignalStrength(RadioIndicationType /*type*/,
-                                                         const SignalStrength& /*signalStrength*/) {
+Return<void> RadioIndication_v1_2::currentSignalStrength(
+    RadioIndicationType /*type*/,
+    const ::android::hardware::radio::V1_0::SignalStrength& /*signalStrength*/) {
     return Void();
 }
 
@@ -224,7 +230,8 @@
 }
 
 Return<void> RadioIndication_v1_2::cellInfoList(
-    RadioIndicationType /*type*/, const ::android::hardware::hidl_vec<CellInfo>& /*records*/) {
+    RadioIndicationType /*type*/,
+    const ::android::hardware::hidl_vec<::android::hardware::radio::V1_0::CellInfo>& /*records*/) {
     return Void();
 }
 
@@ -276,4 +283,4 @@
 Return<void> RadioIndication_v1_2::modemReset(RadioIndicationType /*type*/,
                                               const ::android::hardware::hidl_string& /*reason*/) {
     return Void();
-}
+}
\ No newline at end of file
diff --git a/radio/1.2/vts/functional/radio_response.cpp b/radio/1.2/vts/functional/radio_response.cpp
index 9195689..85ec3e0 100644
--- a/radio/1.2/vts/functional/radio_response.cpp
+++ b/radio/1.2/vts/functional/radio_response.cpp
@@ -16,13 +16,14 @@
 
 #include <radio_hidl_hal_utils_v1_2.h>
 
-CardStatus cardStatus;
+::android::hardware::radio::V1_2::CardStatus cardStatus;
 
 RadioResponse_v1_2::RadioResponse_v1_2(RadioHidlTest_v1_2& parent) : parent_v1_2(parent) {}
 
 /* 1.0 Apis */
-Return<void> RadioResponse_v1_2::getIccCardStatusResponse(const RadioResponseInfo& /*info*/,
-                                                          const CardStatus& /*card_status*/) {
+Return<void> RadioResponse_v1_2::getIccCardStatusResponse(
+    const RadioResponseInfo& /*info*/,
+    const ::android::hardware::radio::V1_0::CardStatus& /*card_status*/) {
     return Void();
 }
 
@@ -62,7 +63,8 @@
 }
 
 Return<void> RadioResponse_v1_2::getCurrentCallsResponse(
-    const RadioResponseInfo& /*info*/, const ::android::hardware::hidl_vec<Call>& /*calls*/) {
+    const RadioResponseInfo& /*info*/,
+    const ::android::hardware::hidl_vec<::android::hardware::radio::V1_0::Call>& /*calls*/) {
     return Void();
 }
 
@@ -107,18 +109,21 @@
     return Void();
 }
 
-Return<void> RadioResponse_v1_2::getSignalStrengthResponse(const RadioResponseInfo& /*info*/,
-                                                           const SignalStrength& /*sig_strength*/) {
+Return<void> RadioResponse_v1_2::getSignalStrengthResponse(
+    const RadioResponseInfo& /*info*/,
+    const ::android::hardware::radio::V1_0::SignalStrength& /*sig_strength*/) {
     return Void();
 }
 
 Return<void> RadioResponse_v1_2::getVoiceRegistrationStateResponse(
-    const RadioResponseInfo& /*info*/, const VoiceRegStateResult& /*voiceRegResponse*/) {
+    const RadioResponseInfo& /*info*/,
+    const ::android::hardware::radio::V1_0::VoiceRegStateResult& /*voiceRegResponse*/) {
     return Void();
 }
 
 Return<void> RadioResponse_v1_2::getDataRegistrationStateResponse(
-    const RadioResponseInfo& /*info*/, const DataRegStateResult& /*dataRegResponse*/) {
+    const RadioResponseInfo& /*info*/,
+    const ::android::hardware::radio::V1_0::DataRegStateResult& /*dataRegResponse*/) {
     return Void();
 }
 
@@ -312,8 +317,10 @@
 }
 
 Return<void> RadioResponse_v1_2::getAvailableBandModesResponse(
-    const RadioResponseInfo& /*info*/,
-    const ::android::hardware::hidl_vec<RadioBandMode>& /*bandModes*/) {
+    const RadioResponseInfo& info, const ::android::hardware::hidl_vec<RadioBandMode>& bandModes) {
+    rspInfo = info;
+    radioBandModes = bandModes;
+    parent_v1_2.notify();
     return Void();
 }
 
@@ -515,7 +522,7 @@
 
 Return<void> RadioResponse_v1_2::getCellInfoListResponse(
     const RadioResponseInfo& /*info*/,
-    const ::android::hardware::hidl_vec<CellInfo>& /*cellInfo*/) {
+    const ::android::hardware::hidl_vec<::android::hardware::radio::V1_0::CellInfo>& /*cellInfo*/) {
     return Void();
 }
 
@@ -704,8 +711,9 @@
     return Void();
 }
 
-Return<void> RadioResponse_v1_2::getIccCardStatusResponse_1_2(const RadioResponseInfo& info,
-                                                              const CardStatus& card_status) {
+Return<void> RadioResponse_v1_2::getIccCardStatusResponse_1_2(
+    const RadioResponseInfo& info,
+    const ::android::hardware::radio::V1_2::CardStatus& card_status) {
     rspInfo = info;
     cardStatus = card_status;
     parent_v1_2.notify();
@@ -713,32 +721,37 @@
 }
 
 Return<void> RadioResponse_v1_2::getCurrentCallsResponse_1_2(
-    const RadioResponseInfo& info, const ::android::hardware::hidl_vec<Call>& /*calls*/) {
+    const RadioResponseInfo& info,
+    const ::android::hardware::hidl_vec<::android::hardware::radio::V1_2::Call>& /*calls*/) {
     rspInfo = info;
     parent_v1_2.notify();
     return Void();
 }
 
 Return<void> RadioResponse_v1_2::getSignalStrengthResponse_1_2(
-    const RadioResponseInfo& info, const SignalStrength& /*sig_strength*/) {
+    const RadioResponseInfo& info,
+    const ::android::hardware::radio::V1_2::SignalStrength& /*sig_strength*/) {
     rspInfo = info;
     parent_v1_2.notify();
     return Void();
 }
 
 Return<void> RadioResponse_v1_2::getCellInfoListResponse_1_2(
-    const RadioResponseInfo& info, const ::android::hardware::hidl_vec<CellInfo>& /*cellInfo*/) {
+    const RadioResponseInfo& info,
+    const ::android::hardware::hidl_vec<::android::hardware::radio::V1_2::CellInfo>& /*cellInfo*/) {
     rspInfo = info;
     parent_v1_2.notify();
     return Void();
 }
 
 Return<void> RadioResponse_v1_2::getVoiceRegistrationStateResponse_1_2(
-    const RadioResponseInfo& /*info*/, const V1_2::VoiceRegStateResult& /*voiceRegResponse*/) {
+    const RadioResponseInfo& /*info*/,
+    const ::android::hardware::radio::V1_2::VoiceRegStateResult& /*voiceRegResponse*/) {
     return Void();
 }
 
 Return<void> RadioResponse_v1_2::getDataRegistrationStateResponse_1_2(
-    const RadioResponseInfo& /*info*/, const V1_2::DataRegStateResult& /*dataRegResponse*/) {
+    const RadioResponseInfo& /*info*/,
+    const ::android::hardware::radio::V1_2::DataRegStateResult& /*dataRegResponse*/) {
     return Void();
-}
+}
\ No newline at end of file
diff --git a/wifi/1.2/vts/functional/wifi_sta_iface_hidl_test.cpp b/wifi/1.2/vts/functional/wifi_sta_iface_hidl_test.cpp
index fd4a671..92f5d14 100644
--- a/wifi/1.2/vts/functional/wifi_sta_iface_hidl_test.cpp
+++ b/wifi/1.2/vts/functional/wifi_sta_iface_hidl_test.cpp
@@ -14,6 +14,9 @@
  * limitations under the License.
  */
 
+#include <numeric>
+#include <vector>
+
 #include <android-base/logging.h>
 
 #include <android/hardware/wifi/1.2/IWifiStaIface.h>
@@ -24,8 +27,9 @@
 #include "wifi_hidl_test_utils.h"
 
 using ::android::sp;
-using ::android::hardware::wifi::V1_2::IWifiStaIface;
+using ::android::hardware::wifi::V1_0::CommandId;
 using ::android::hardware::wifi::V1_0::WifiStatusCode;
+using ::android::hardware::wifi::V1_2::IWifiStaIface;
 
 /**
  * Fixture to use for all STA Iface HIDL interface tests.
@@ -40,6 +44,13 @@
     virtual void TearDown() override { stopWifi(); }
 
    protected:
+    bool isCapabilitySupported(IWifiStaIface::StaIfaceCapabilityMask cap_mask) {
+        const auto& status_and_caps =
+            HIDL_INVOKE(wifi_sta_iface_, getCapabilities);
+        EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_caps.first.code);
+        return (status_and_caps.second & cap_mask) != 0;
+    }
+
     sp<IWifiStaIface> wifi_sta_iface_;
 };
 
@@ -54,3 +65,45 @@
     EXPECT_EQ(WifiStatusCode::SUCCESS,
               HIDL_INVOKE(wifi_sta_iface_, setMacAddress, kMac).code);
 }
+
+/*
+ * ReadApfPacketFilterData:
+ * Ensures that we can read the APF working memory when supported.
+ *
+ * TODO: Test disabled because we can't even test reading and writing the APF
+ * memory while the interface is in disconnected state (b/73804303#comment25).
+ * There's a pending bug on VTS infra to add such support (b/32974062).
+ * TODO: We can't execute APF opcodes from this test because there's no way
+ * to loop test packets through the wifi firmware (b/73804303#comment29).
+ */
+TEST_F(WifiStaIfaceHidlTest, DISABLED_ReadApfPacketFilterData) {
+    if (!isCapabilitySupported(IWifiStaIface::StaIfaceCapabilityMask::APF)) {
+        // Disable test if APF packet filer is not supported.
+        LOG(WARNING) << "TEST SKIPPED: APF packet filtering not supported";
+        return;
+    }
+
+    const auto& status_and_caps =
+        HIDL_INVOKE(wifi_sta_iface_, getApfPacketFilterCapabilities);
+    EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_caps.first.code);
+    LOG(WARNING) << "StaApfPacketFilterCapabilities: version="
+                 << status_and_caps.second.version
+                 << " maxLength=" << status_and_caps.second.maxLength;
+
+    const CommandId kCmd = 0;  // Matches what WifiVendorHal.java uses.
+    const uint32_t kDataSize =
+        std::min(status_and_caps.second.maxLength, static_cast<uint32_t>(500));
+
+    // Create a buffer and fill it with some values.
+    std::vector<uint8_t> data(kDataSize);
+    std::iota(data.begin(), data.end(), 0);
+
+    EXPECT_EQ(
+        HIDL_INVOKE(wifi_sta_iface_, installApfPacketFilter, kCmd, data).code,
+        WifiStatusCode::SUCCESS);
+    const auto& status_and_data =
+        HIDL_INVOKE(wifi_sta_iface_, readApfPacketFilterData);
+    EXPECT_EQ(WifiStatusCode::SUCCESS, status_and_data.first.code);
+
+    EXPECT_EQ(status_and_data.second, data);
+}