diff --git a/current.txt b/current.txt
index 66dbf65..972fe16 100644
--- a/current.txt
+++ b/current.txt
@@ -574,8 +574,11 @@
 # ABI preserving changes to HALs during Android R
 b69a7615c508acf5c5201efd1bfa3262167874fc3594e2db5a3ff93addd8ac75 android.hardware.keymaster@4.0::IKeymasterDevice
 eb2fa0c883c2185d514be0b84c179b283753ef0c1b77b45b4f359bd23bba8b75 android.hardware.neuralnetworks@1.0::IPreparedModel
+f1109cbb10297b7429a11fab42afa912710b303c9bf20bd5cdb8bd57b9c84186 android.hardware.neuralnetworks@1.0::types
+9d8ee57c490ffeaa28f702eaea8d198cb510e4bbfb99e6cb5f63e73341057c7c android.hardware.neuralnetworks@1.1::types
 fb382e986c10b8fbb797a8546e8f9ea6d1107bfe6f3fb7e57f6bbbf1f807a906 android.hardware.neuralnetworks@1.2::IDevice
 40e71cd693de5b832325c5d8f081f2ff20a7ba2b89d401cee5b4b3eb0e241681 android.hardware.neuralnetworks@1.2::IPreparedModel
+71c0f7127335e5b74d1615d5e7f129831b43ffbae5318ad0924d7d8d8910a859 android.hardware.neuralnetworks@1.2::types
 a785a57447a81e9c130eef6904c3a5c256076c6a04588c40620ebd6fa2660d77 android.hardware.radio@1.2::types
 1a6e2bd289f22931c526b21916910f1d4c436b7acb9556e4243de4ce8e6cc2e4 android.hardware.soundtrigger@2.0::ISoundTriggerHwCallback
 fd65298e1e09e0e3c781ab18305920d757dbe55a3b459ce17814ec5cf6dfee99 android.hardware.wifi@1.0::IWifiP2pIface
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 02db063..ba9d068 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -25,25 +25,24 @@
  * 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
+ * Although we define many types, 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 {
     /** A 32 bit floating point scalar value. */
-    FLOAT32             = 0,
+    FLOAT32 = 0,
     /** A signed 32 bit integer scalar value. */
-    INT32               = 1,
+    INT32 = 1,
     /** An unsigned 32 bit integer scalar value. */
-    UINT32              = 2,
-
+    UINT32 = 2,
     /** A tensor of 32 bit floating point values. */
-    TENSOR_FLOAT32      = 3,
+    TENSOR_FLOAT32 = 3,
     /** A tensor of 32 bit integer values. */
-    TENSOR_INT32        = 4,
+    TENSOR_INT32 = 4,
     /**
-     * A tensor of 8 bit integers that represent real numbers.
+     * A tensor of 8 bit unsigned 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:
@@ -51,21 +50,21 @@
      * - zeroPoint: a 32 bit integer, in range [0, 255].
      *
      * The formula is:
-     * real_value = (integer_value - zeroPoint) * scale.
+     *   real_value = (integer_value - zeroPoint) * scale.
      */
     TENSOR_QUANT8_ASYMM = 5,
 
     /**
-     * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
-     * OEM operation and data types.
+     * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+     * alternative to OEM operation and data types.
      *
      * OEM specific scalar value.
      */
     OEM                 = 10000,
 
     /**
-     * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
-     * OEM operation and data types.
+     * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+     * alternative to OEM operation and data types.
      *
      * A tensor of OEM specific values.
      */
@@ -78,7 +77,6 @@
  * The type of an operation in a model.
  */
 enum OperationType : int32_t {
-
     /**
      * Adds two tensors, element-wise.
      *
@@ -110,14 +108,16 @@
      * * 0: A tensor.
      * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
      *      as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scales and zeroPoint can be different from input0 scale and zeroPoint.
      * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
      *      {@link FusedActivationFunc} values. Specifies the activation to
      *      invoke on the result.
      *
      * Outputs:
      * * 0: The sum, a tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     ADD = 0,
 
@@ -187,8 +187,8 @@
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, out_height, out_width, depth].
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     AVERAGE_POOL_2D = 1,
 
@@ -206,22 +206,23 @@
      *
      * Inputs:
      * * 0 ~ n-1: The list of n input tensors, of shape
-     *            [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of
-     *            {@link OperandType::TENSOR_QUANT8_ASYMM}, all input tensors
-     *            must have the same scale and zeroPoint.
+     *            [D0, D1, ..., Daxis(i), ..., Dm].
+     *            All input tensors of
+     *            {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *            must have the same scale and zeroPoint as the output tensor.
      * * n: An {@link OperandType::INT32} scalar, specifying the
      *      concatenation axis.
      *
      * Outputs:
      * * 0: The output, a tensor of the same {@link OperandType} as the input
      *      tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor, the scale and zeroPoint
+     *      values must be the same as the input tensors'.
      */
     CONCATENATION = 2,
 
     /**
-     * Performs an 2-D convolution operation.
+     * Performs a 2-D convolution operation.
      *
      * The CONV_2D op sweeps a 2-D filter that can mix channels together over a
      * batch of images, applying the filter to each window of each image of the
@@ -238,11 +239,17 @@
      *             filter[channel, di, dj, k]
      *         ) + bias[channel]
      *
-     * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT32}
-     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor {@link OperandType} configurations:
+     * * 32 bit floating point:
+     * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
      *
-     * Supported tensor rank: 4, with "NHWC" data layout.
+     * * Quantized:
+     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+     * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+     * * * input.scale * filter.scale).
+     *
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+     * and Channels) data layout.
      *
      * Both explicit padding and implicit padding are supported.
      *
@@ -252,12 +259,12 @@
      * * 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}, the bias
-     *      should also be of {@link OperandType::TENSOR_FLOAT32}. For input
-     *      tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias
-     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
-     *      0 and bias_scale == input_scale * filter_scale.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32}
+     *      the bias must be of the same
+     *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+     *      of 0 and bias_scale == input_scale * filter_scale.
      * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
      *      the left, in the ‘width’ dimension.
      * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -281,11 +288,11 @@
      *      [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}, the bias should
-     *      also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
-     *      of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
-     *      of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
-     *      bias_scale == input_scale * filter_scale.
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32}
+     *      the bias must be of the same
+     *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+     *      of 0 and bias_scale == input_scale * filter_scale.
      * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
      *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -299,11 +306,9 @@
      *
      * 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}, the following condition
-     *      must be satisfied: output_scale > input_scale * filter_scale.
-     *
-     * Available since API level 27.
+     *      [batches, out_height, out_width, depth_out].
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the following condition must be satisfied: output_scale > input_scale * filter_scale
      */
     CONV_2D = 3,
 
@@ -329,11 +334,17 @@
      *             filter[1, di, dj, k * channel_multiplier + q]
      *         ) + bias[k * channel_multiplier + q]
      *
-     * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT32}
-     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor {@link OperandType} configurations:
+     * * 32 bit floating point:
+     * * * {@link OperandType::TENSOR_FLOAT32} for input, filter, output, and bias.
      *
-     * Supported tensor rank: 4, with "NHWC" data layout.
+     * * Quantized:
+     * * * {@link OperandType::TENSOR_QUANT8_ASYMM} for input, filter, and output.
+     * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
+     * * * input.scale * filter.scale).
+     *
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+     * and Channels) data layout.
      *
      * Both explicit padding and implicit padding are supported.
      *
@@ -343,11 +354,11 @@
      * * 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}, the bias should
-     *      also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
-     *      of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
-     *      of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
-     *      bias_scale == input_scale * filter_scale.
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32}
+     *      the bias must be of the same
+     *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+     *      of 0 and bias_scale == input_scale * filter_scale.
      * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
      *      the left, in the ‘width’ dimension.
      * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -372,11 +383,11 @@
      * * 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}, the bias should
-     *      also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
-     *      of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
-     *      of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
-     *      bias_scale == input_scale * filter_scale.
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32}
+     *      the bias must be of the same
+     *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
+     *      of 0 and bias_scale == input_scale * filter_scale.
      * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
      *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -392,11 +403,10 @@
      *
      * 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}, the following condition
-     *      must be satisfied: output_scale > input_scale * filter_scale.
-     *
-     * Available since API level 27.
+     *      [batches, out_height, out_width, depth_out]. For
+     *      output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the following condition must be satisfied:
+     *      output_scale > input_scale * filter_scale
      */
     DEPTHWISE_CONV_2D = 4,
 
@@ -419,7 +429,8 @@
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
-     * Supported tensor rank: 4, with "NHWC" data layout.
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+     * and Channels) data layout.
      *
      * Inputs:
      * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -431,8 +442,8 @@
      * Outputs:
      * * 0: The output 4-D tensor, of shape [batch, height*block_size,
      *      width*block_size, depth/(block_size*block_size)].
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     DEPTH_TO_SPACE = 5,
 
@@ -443,19 +454,19 @@
      *
      *     output = (input - zeroPoint) * scale.
      *
-     * Supported tensor {@link OperandType}:
+     * Supported input tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
+     * Supported output tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT32}.
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * * 0: A tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}.
+     * * 0: A tensor.
      *
      * Outputs:
-     * * 0: The output tensor of same shape as input0, but with
-     *      {@link OperandType::TENSOR_FLOAT32}.
-     *
-     * Available since API level 27.
+     * * 0: A tensor with the same shape as input0.
      */
     DEQUANTIZE = 6,
 
@@ -479,6 +490,13 @@
      * If a value in Lookups is out of bounds, the operation must fail
      * and an error must be reported.
      *
+     * Supported value tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported value tensor rank: from 2
+     *
      * Inputs:
      * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32}.
      *      The values are indices into the first dimension of Values.
@@ -489,8 +507,8 @@
      * * 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.
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input1.
      */
     EMBEDDING_LOOKUP = 7,
 
@@ -508,8 +526,6 @@
      * Outputs:
      * * 0: The output tensor, of the same {@link OperandType} and dimensions as
      *      the input tensor.
-     *
-     * Available since API level 27.
      */
     FLOOR = 8,
 
@@ -549,12 +565,9 @@
      *      invoke on the result.
      *
      * Outputs:
-     * * 0: The output tensor, of shape [batch_size, num_units]. For output
-     *      tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
-     *      condition must be satisfied:
-     *      output_scale > input_scale * filter_scale.
-     *
-     * Available since API level 27.
+     * * 0: The output tensor, of shape [batch_size, num_units]. For
+     *      output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
+     *      condition must be satisfied: output_scale > input_scale * filter_scale.
      */
     FULLY_CONNECTED = 9,
 
@@ -585,6 +598,13 @@
      * must be selected. If no entry in Keys has 123456, a slice of zeroes
      * must be concatenated.
      *
+     * Supported value tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported value tensor rank: from 2
+     *
      * Inputs:
      * * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with
      *      shape [ k ].
@@ -598,13 +618,13 @@
      *
      * Outputs:
      * * 0: Output. A tensor with shape [ k …].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input2.
      * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
      *      hits (True) or not (False).
      *      Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0
      *      and scale 1.0f.
      *      A non-zero byte represents True, a hit. A zero indicates otherwise.
-     *
-     * Available since API level 27.
      */
     HASHTABLE_LOOKUP = 10,
 
@@ -617,9 +637,6 @@
      *         input[batch, row, col, channel] /
      *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
      *
-     * For input tensor with more dimensions, independently normalizes each 1-D
-     * slice along dimension dim.
-     *
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
@@ -627,13 +644,10 @@
      * Height, Width, and Channels).
      *
      * Inputs:
-     * * 0: A 4-D tensor, of shape [batches, height, width, depth].
+     * * 0: A 4-D tensor, specifying the tensor to be normalized.
      *
      * Outputs:
-     * * 0: The output 4-D tensor, of the same shape as input
-     *      [batches, height, width, depth].
-     *
-     * Available since API level 27.
+     * * 0: A tensor of the same {@link OperandType} and same shape as input0.
      */
     L2_NORMALIZATION = 11,
 
@@ -652,7 +666,8 @@
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
-     * Supported tensor rank: 4, with "NHWC" data layout.
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+     * and Channels) data layout.
      *
      * Both explicit padding and implicit padding are supported.
      *
@@ -700,8 +715,6 @@
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, out_height, out_width, depth].
-     *
-     * Available since API level 27.
      */
     L2_POOL_2D = 12,
 
@@ -729,17 +742,18 @@
      *      the input.
      * * 1: An {@link OperandType::INT32} scalar, specifying the radius of
      *      the normalization window.
-     * * 2: An {@link OperandType::FLOAT32} scalar, specifying the bias, must
-     *      not be zero.
-     * * 3: An {@link OperandType::FLOAT32} scalar, specifying the scale
-     *      factor, alpha.
-     * * 4: An {@link OperandType::FLOAT32} scalar, specifying the exponent,
-     *      beta.
+     * * 2: A scalar, specifying the bias, must not be zero.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
+     *      value must be of {@link OperandType::FLOAT32}.
+     * * 3: A scalar, specifying the scale factor, alpha.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
+     *      alpha value must be of {@link OperandType::FLOAT32}.
+     * * 4: A scalar, specifying the exponent, beta.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the beta
+     *      value must be of {@link OperandType::FLOAT32}.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 27.
      */
     LOCAL_RESPONSE_NORMALIZATION = 13,
 
@@ -763,45 +777,53 @@
      * * 0: The output tensor of same shape as input0.
      *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the scale must be 1.f / 256 and the zeroPoint must be 0.
-     *
-     * Available since API level 27.
      */
     LOGISTIC = 14,
 
     /**
      * Projects an input to a bit vector via locality senstive hashing.
      *
+     * Supported input tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_INT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported input tensor rank: from 1
+     *
      * Inputs:
      * * 0: Hash functions. Dim.size == 2, DataType: Float.
-     *            Tensor[0].Dim[0]: Number of hash functions.
-     *            Tensor[0].Dim[1]: Number of seeds per hash functions.
-     *            Tensor[0].Dim[1] <= 32 in sparse case.
+     *      Tensor[0].Dim[0]: Number of hash functions.
+     *      Tensor[0].Dim[1]: Number of projected output bits generated by each
+     *      hash function.
+     *      If the projection type is Sparse:
+     *      Tensor[0].Dim[1] + ceil(log2(Tensor[0].Dim[0])) <= 32
      *
      * * 1: Input. Dim.size >= 1, no restriction on DataType.
      * * 2: Weight. Optional. Dim.size == 1, DataType: Float.
-     *     If not set, each input element is considered to have the same weight
-     *     of 1.0.
-     *     Tensor[1].Dim[0] == Tensor[2].Dim[0]
+     *      If not set, each input element is considered to have the same weight
+     *      of 1.0.
+     *      Tensor[1].Dim[0] == Tensor[2].Dim[0]
      * * 3: Type:
-     *        Sparse: Value LSHProjectionType_SPARSE(=1).
+     *        Sparse:
+     *          Value LSHProjectionType_SPARSE(=1).
      *          Computed bit vector is considered to be sparse.
      *          Each output element is an int32 made up of multiple bits
      *          computed from hash functions.
      *
-     *        Dense: Value LSHProjectionType_DENSE(=2).
+     *        Dense:
+     *          Value LSHProjectionType_DENSE(=2).
      *          Computed bit vector is considered to be dense. Each output
      *          element represents a bit and can take the value of either
      *          0 or 1.
      *
      * Outputs:
-     * * 0: If the projection type is sparse:
-     *        Output.Dim == { Tensor[0].Dim[0] }
-     *        A tensor of int32 that represents hash signatures.
-     *      If the projection type is Dense:
-     *        Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
-     *        A flattened tensor that represents projected bit vectors.
+     * * 0: If the projection type is Sparse:
+     *      Output.Dim == { Tensor[0].Dim[0] }
+     *      A tensor of int32 that represents hash signatures.
      *
-     * Available since API level 27.
+     *      If the projection type is Dense:
+     *      Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
+     *      A flattened tensor that represents projected bit vectors.
      */
     LSH_PROJECTION = 15,
 
@@ -901,71 +923,54 @@
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
+     * All input and output tensors must be of the same type.
+     *
      * Inputs:
      * * 0: The input (\f$x_t\f$).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, input_size], where “batch_size” corresponds to the
-     *      batching dimension, and “input_size” is the size of the input.
+     *      A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+     *      corresponds to the batching dimension, and “input_size” is the size
+     *      of the input.
      * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units, input_size], where “num_units” corresponds to the
-     *      number of cell units.
+     *      A 2-D tensor of shape [num_units, input_size], where “num_units”
+     *      corresponds to the number of cell units.
      * * 2: The input-to-forget weights (\f$W_{xf}\f$).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units, input_size].
+     *      A 2-D tensor of shape [num_units, input_size].
      * * 3: The input-to-cell weights (\f$W_{xc}\f$).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units, input_size].
+     *      A 2-D tensor of shape [num_units, input_size].
      * * 4: The input-to-output weights (\f$W_{xo}\f$).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units, input_size].
+     *      A 2-D tensor of shape [num_units, input_size].
      * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, 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.
+     *      A 2-D tensor of shape [num_units, output_size], where “output_size”
+     *      corresponds to either the number of cell units (i.e., “num_units”),
+     *      or the second dimension of the “projection_weights”, if defined.
      * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units, output_size].
+     *      A 2-D tensor of shape [num_units, output_size].
      * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units, output_size].
+     *      A 2-D tensor of shape [num_units, output_size].
      * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units, output_size].
+     *      A 2-D tensor of shape [num_units, output_size].
      * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
-     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units].
+     *      A 1-D tensor of shape [num_units].
      * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
-     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units].
+     *      A 1-D tensor of shape [num_units].
      * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
-     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units].
+     *      A 1-D tensor of shape [num_units].
      * * 12:The input gate bias (\f$b_i\f$). Optional.
-     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units].
+     *      A 1-D tensor of shape [num_units].
      * * 13:The forget gate bias (\f$b_f\f$).
-     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units].
+     *      A 1-D tensor of shape [num_units].
      * * 14:The cell bias (\f$b_c\f$).
-     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units].
+     *      A 1-D tensor of shape [num_units].
      * * 15:The output gate bias (\f$b_o\f$).
-     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units].
+     *      A 1-D tensor of shape [num_units].
      * * 16:The projection weights (\f$W_{proj}\f$). Optional.
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [output_size, num_units].
+     *      A 2-D tensor of shape [output_size, num_units].
      * * 17:The projection bias (\f$b_{proj}\f$). Optional.
-     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [output_size].
+     *      A 1-D tensor of shape [output_size].
      * * 18:The output state (in) (\f$h_{t-1}\f$).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, output_size].
+     *      A 2-D tensor of shape [batch_size, output_size].
      * * 19:The cell state (in) (\f$C_{t-1}\f$).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, num_units].
+     *      A 2-D tensor of shape [batch_size, num_units].
      * * 20:The activation function (\f$g\f$).
      *      A value indicating the activation function:
      *      <ul>
@@ -984,21 +989,15 @@
      *
      * Outputs:
      * * 0: The scratch buffer.
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, num_units * 3] with CIFG, or
+     *      A 2-D tensor of shape [batch_size, num_units * 3] with CIFG, or
      *      [batch_size, num_units * 4] without CIFG.
      * * 1: The output state (out) (\f$h_t\f$).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, output_size].
+     *      A 2-D tensor of shape [batch_size, output_size].
      * * 2: The cell state (out) (\f$C_t\f$).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, num_units].
+     *      A 2-D tensor of shape [batch_size, num_units].
      * * 3: The output (\f$o_t\f$).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, output_size]. This is effectively the same as the
-     *      current “output state (out)” value.
-     *
-     * Available since API level 27.
+     *      A 2-D tensor of shape [batch_size, output_size]. This is effectively
+     *      the same as the current “output state (out)” value.
      */
     LSTM = 16,
 
@@ -1019,7 +1018,8 @@
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
-     * Supported tensor rank: 4, with "NHWC" data layout.
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+     * and Channels) data layout.
      *
      * Both explicit padding and implicit padding are supported.
      *
@@ -1067,8 +1067,8 @@
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, out_height, out_width, depth].
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     MAX_POOL_2D = 17,
 
@@ -1106,8 +1106,6 @@
      *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the following condition must be satisfied:
      *      output_scale > input1_scale * input2_scale.
-     *
-     * Available since API level 27.
      */
     MUL = 18,
 
@@ -1129,8 +1127,8 @@
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     RELU = 19,
 
@@ -1151,9 +1149,9 @@
      * * 0: A tensor, specifying the input.
      *
      * Outputs:
-     * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 27.
+     * * 0: The output tensor of the same shape as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     RELU1 = 20,
 
@@ -1175,8 +1173,8 @@
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     RELU6 = 21,
 
@@ -1205,8 +1203,8 @@
      *
      * Outputs:
      * * 0: The output tensor, of shape specified by the input shape.
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     RESHAPE = 22,
 
@@ -1220,9 +1218,10 @@
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
-     * Supported tensor rank: 4, with "NHWC" data layout.
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+     * and Channels) data layout.
      *
-     * Inputs:
+     * Inputs (resizing by shape):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
      *      the input.
      * * 1: An {@link OperandType::INT32} scalar, specifying the output
@@ -1233,8 +1232,6 @@
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, new_height, new_width, depth].
-     *
-     * Available since API level 27.
      */
     RESIZE_BILINEAR = 23,
 
@@ -1257,25 +1254,23 @@
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
+     * The input tensors must all be the same type.
+     *
      * Inputs:
      * * 0: input.
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32} of shape
-     *      [batch_size, input_size], where “batch_size” corresponds to the
-     *      batching dimension, and “input_size” is the size of the input.
+     *      A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+     *      corresponds to the batching dimension, and “input_size” is the size
+     *      of the input.
      * * 1: weights.
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units, input_size], where “num_units” corresponds to the
-     *      number of units.
+     *      A 2-D tensor of shape [num_units, input_size], where “num_units”
+     *      corresponds to the number of units.
      * * 2: recurrent_weights.
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units, num_units], with columns corresponding to the weights
-     *      from each unit.
+     *      A 2-D tensor of shape [num_units, num_units], with columns
+     *      corresponding to the weights from each unit.
      * * 3: bias.
-     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units].
+     *      A 1-D tensor of shape [num_units].
      * * 4: hidden state (in).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, num_units].
+     *      A 2-D tensor of shape [batch_size, num_units].
      * * 5: fused_activation_function.
      *      An optional {@link FusedActivationFunc} value indicating the
      *      activation function. If “NONE” is specified then it results in a
@@ -1283,15 +1278,11 @@
      *
      * Outputs:
      * * 0: hidden state (out).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, num_units].
+     *      A 2-D tensor of shape [batch_size, num_units].
      *
      * * 1: output.
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, num_units]. This is effectively the same as the
-     *      current state value.
-     *
-     * Available since API level 27.
+     *      A 2-D tensor of shape [batch_size, num_units]. This is effectively
+     *      the same as the current state value.
      */
     RNN = 24,
 
@@ -1306,6 +1297,9 @@
      *         exp((input[batch, i] - max(input[batch, :])) * beta) /
      *         sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
      *
+     * For input tensor with rank other than 2, the activation will be applied
+     * independently on each 1-D slice along specified dimension.
+     *
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
@@ -1314,15 +1308,15 @@
      *
      * Inputs:
      * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
-     * * 1: An {@link OperandType::FLOAT32} scalar, specifying the positive
-     *      scaling factor for the exponent, beta.
+     * * 1: A scalar, specifying the positive scaling factor for the exponent,
+     *      beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of
+     *      {@link OperandType::FLOAT32}.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
      *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the scale must be 1.f / 256 and the zeroPoint must be 0.
-     *
-     * Available since API level 27.
      */
     SOFTMAX = 25,
 
@@ -1344,7 +1338,8 @@
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
-     * Supported tensor rank: 4, with "NHWC" data layout.
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+     * and Channels) data layout.
      *
      * Inputs:
      * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -1356,8 +1351,8 @@
      * Outputs:
      * * 0: The output 4-D tensor, of shape [batches, height/block_size,
      *      width/block_size, depth_in*block_size*block_size].
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     SPACE_TO_DEPTH = 26,
 
@@ -1403,25 +1398,23 @@
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
+     * All input tensors must be the same type.
+     *
      * Inputs:
      * * 0: input.
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, input_size], where “batch_size” corresponds to the
-     *      batching dimension, and “input_size” is the size of the input.
+     *      A 2-D tensor of shape [batch_size, input_size], where “batch_size”
+     *      corresponds to the batching dimension, and “input_size” is the size
+     *      of the input.
      * * 1: weights_feature.
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units, input_size], where “num_units” corresponds to the
-     *      number of units.
+     *      A 2-D tensor of shape [num_units, input_size], where “num_units”
+     *      corresponds to the number of units.
      * * 2: weights_time.
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [num_units, memory_size], where “memory_size” corresponds to the
-     *      fixed-size of the memory.
+     *      A 2-D tensor of shape [num_units, memory_size], where “memory_size”
+     *      corresponds to the fixed-size of the memory.
      * * 3: bias.
-     *      An optional 1-D tensor of {@link OperandType::TENSOR_FLOAT32},
-     *      of shape [num_units].
+     *      An optional 1-D tensor of shape [num_units].
      * * 4: state (in).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
-     *      [batch_size, (memory_size - 1) * num_units * rank].
+     *      A 2-D tensor of shape [batch_size, (memory_size - 1) * num_units * rank].
      * * 5: rank.
      *      The rank of the SVD approximation.
      * * 6: fused_activation_function.
@@ -1431,13 +1424,11 @@
      *
      * Outputs:
      * * 0: state (out).
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      A 2-D tensor of the same {@link OperandType} as the inputs, with shape
      *      [batch_size, (memory_size - 1) * num_units * rank].
      * * 1: output.
-     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      A 2-D tensor of the same {@link OperandType} as the inputs, with shape
      *      [batch_size, num_units].
-     *
-     * Available since API level 27.
      */
     SVDF = 27,
 
@@ -1458,8 +1449,6 @@
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 27.
      */
     TANH = 28,
 
diff --git a/neuralnetworks/1.0/types.t b/neuralnetworks/1.0/types.t
new file mode 100644
index 0000000..d7b26aa
--- /dev/null
+++ b/neuralnetworks/1.0/types.t
@@ -0,0 +1,431 @@
+%% template file for generating types.hal.
+%% see frameworks/ml/nn/tools/api/README.md.
+/*
+ * Copyright (C) 2017 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.neuralnetworks@1.0;
+
+%insert Operand_1.0_Comment
+enum OperandType : int32_t {
+%insert Operand_1.0
+
+    /**
+     * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+     * alternative to OEM operation and data types.
+     *
+     * OEM specific scalar value.
+     */
+    OEM                 = 10000,
+
+    /**
+     * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+     * alternative to OEM operation and data types.
+     *
+     * A tensor of OEM specific values.
+     */
+    TENSOR_OEM_BYTE     = 10001,
+};
+
+%insert Operation_1.0_Comment
+enum OperationType : int32_t {
+%insert Operation_1.0
+
+    /**
+     * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
+     * OEM operation and data types.
+     *
+     * This operation is OEM specific. It should only be used for OEM
+     * applications.
+     */
+    OEM_OPERATION = 10000,
+};
+
+/**
+ * Fused activation function types.
+ */
+enum FusedActivationFunc : int32_t {
+    NONE  = 0,
+    RELU  = 1,
+    RELU1 = 2,
+    RELU6 = 3,
+};
+
+/**
+ * How an operand is used.
+ */
+enum OperandLifeTime : int32_t {
+    /**
+     * The operand is internal to the model. It's created by an operation and
+     * consumed by other operations. It must be an output operand of
+     * exactly one operation.
+     */
+    TEMPORARY_VARIABLE,
+
+    /**
+     * The operand is an input of the model. It must not be an output
+     * operand of any operation.
+     *
+     * An operand can't be both input and output of a model.
+     */
+    MODEL_INPUT,
+
+    /**
+     * The operand is an output of the model. It must be an output
+     * operand of exactly one operation.
+     *
+     * An operand can't be both input and output of a model.
+     */
+    MODEL_OUTPUT,
+
+    /**
+     * The operand is a constant found in Model.operandValues. It must
+     * not be an output operand of any operation.
+     */
+    CONSTANT_COPY,
+
+    /**
+     * The operand is a constant that was specified via a Memory
+     * object. It must not be an output operand of any operation.
+     */
+    CONSTANT_REFERENCE,
+
+    /**
+     * The operand does not have a value. This is valid only for optional
+     * arguments of operations.
+     */
+    NO_VALUE,
+};
+
+/**
+ * Status of a device.
+ */
+enum DeviceStatus : int32_t {
+    AVAILABLE,
+    BUSY,
+    OFFLINE,
+    UNKNOWN,
+};
+
+/**
+ * Performance information for the reference workload.
+ *
+ * Used by a driver to report its performance characteristics.
+ */
+struct PerformanceInfo {
+    /**
+     * Ratio of the time taken by the driver to execute the
+     * workload compared to the time the CPU would take for the
+     * same workload. A lower number is better.
+     */
+    float execTime;
+
+    /**
+     * Ratio of the energy used by the driver compared to what
+     * the CPU would use for doing the same workload. A lower number
+     * is better.
+     */
+    float powerUsage;
+};
+
+/**
+ * The capabilities of a driver.
+ */
+struct Capabilities {
+    /**
+     * Driver performance when operating on float32 data.
+     */
+    PerformanceInfo float32Performance;
+
+    /**
+     * Driver performance when operating on asymmetric 8-bit quantized data.
+     */
+    PerformanceInfo quantized8Performance;
+};
+
+/**
+ * Describes the location of a data object.
+ */
+struct DataLocation {
+    /**
+     * The index of the memory pool where this location is found.
+     */
+    uint32_t poolIndex;
+
+    /**
+     * Offset in bytes from the start of the pool.
+     */
+    uint32_t offset;
+
+    /**
+     * The length of the data in bytes.
+     */
+    uint32_t length;
+};
+
+/**
+ * Describes one operand of the model's graph.
+ */
+struct Operand {
+    /**
+     * Data type of the operand.
+     */
+    OperandType type;
+
+    /**
+     * Dimensions of the operand.
+     *
+     * For a scalar operand, dimensions.size() must be 0.
+     *
+     * For a tensor operand, dimensions.size() must be at least 1;
+     * however, any of the dimensions may be unspecified.
+     *
+     * A tensor operand with all dimensions specified has "fully
+     * specified" dimensions. Whenever possible (i.e., whenever the
+     * dimensions are known at model construction time), a tensor
+     * operand should have (but is not required to have) fully
+     * specified dimensions, in order to enable the best possible
+     * performance.
+     *
+     * If a tensor operand's dimensions are not fully specified, the
+     * dimensions of the operand are deduced from the operand
+     * dimensions and values of the operation for which that operand
+     * is an output.
+     *
+     * In the following situations, a tensor operand's dimensions must
+     * be fully specified:
+     *
+     *     . The operand has lifetime CONSTANT_COPY or
+     *       CONSTANT_REFERENCE.
+     *
+     *     . The operand has lifetime MODEL_INPUT or MODEL_OUTPUT. Fully
+     *       specified dimensions must either be present in the
+     *       Operand or they must be provided in the corresponding
+     *       RequestArgument.
+     *       EXCEPTION: If the input or output is optional and omitted
+     *       (by setting the hasNoValue field of the corresponding
+     *       RequestArgument to true) then it need not have fully
+     *       specified dimensions.
+     *
+     * A tensor operand with some number of unspecified dimensions is
+     * represented by setting each unspecified dimension to 0.
+     */
+    vec<uint32_t> dimensions;
+
+    /**
+     * The number of times this operand appears as an operation input.
+     *
+     * (For example, if this operand appears once in one operation's
+     * input list, and three times in another operation's input list,
+     * then numberOfConsumers = 4.)
+     */
+    uint32_t numberOfConsumers;
+
+    /**
+     * Quantized scale of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
+     * TENSOR_INT32.
+     */
+    float scale;
+
+    /**
+     * Quantized zero-point offset of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+     */
+    int32_t zeroPoint;
+
+    /**
+     * How the operand is used.
+     */
+    OperandLifeTime lifetime;
+
+    /**
+     * Where to find the data for this operand.
+     * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
+     * NO_VALUE:
+     * - 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.
+     * - location.length is set.
+     * If the lifetime is CONSTANT_REFERENCE:
+     * - location.poolIndex is set.
+     * - location.offset is the offset in bytes into the specified pool.
+     * - location.length is set.
+     */
+    DataLocation location;
+};
+
+/**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+    /**
+     * The operation type.
+     */
+    OperationType type;
+
+    /**
+     * Describes the table that contains the indexes of the inputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
+    vec<uint32_t> inputs;
+
+    /**
+     * Describes the table that contains the indexes of the outputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
+    vec<uint32_t> outputs;
+};
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * might not be known is the shape of the input tensors.
+ */
+struct Model {
+    /**
+     * All operands included in the model.
+     */
+    vec<Operand> operands;
+
+    /**
+     * All operations included in the model.
+     *
+     * The operations are sorted into execution order. Every operand
+     * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
+     * written before it is read.
+     */
+    vec<Operation> operations;
+
+    /**
+     * Input indexes of the model. There must be at least one.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
+    vec<uint32_t> inputIndexes;
+
+    /**
+     * Output indexes of the model. There must be at least one.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
+    vec<uint32_t> outputIndexes;
+
+    /**
+     * A byte buffer containing operand data that were copied into the model.
+     *
+     * An operand's value must be located here if and only if Operand::lifetime
+     * equals OperandLifeTime::CONSTANT_COPY.
+     */
+    vec<uint8_t> operandValues;
+
+    /**
+     * A collection of shared memory pools containing operand values.
+     *
+     * An operand's value must be located here if and only if Operand::lifetime
+     * equals OperandLifeTime::CONSTANT_REFERENCE.
+     */
+    vec<memory> pools;
+};
+
+/**
+ * Metadata information specifying the location of the input or output data and
+ * any updates to the input or output operand.
+ */
+struct RequestArgument {
+    /**
+     * If true, the argument does not have a value. This can be used for
+     * operations that take optional arguments. If true, the fields of location
+     * are set to 0 and the dimensions vector is left empty.
+     */
+    bool hasNoValue;
+
+    /**
+     * The location within one of the memory pools passed in the Request.
+     */
+    DataLocation location;
+
+    /**
+     * Updated dimension information.
+     *
+     * If dimensions.size() > 0, dimension information was provided
+     * along with the argument. This can be the case for models that
+     * accept inputs of varying size. This can't change the rank, just
+     * the value of the dimensions that were unspecified in the
+     * model. If dimensions.size() > 0, then all dimensions must be
+     * specified here; and any dimension that was specified in the
+     * model must have the same value here.
+     *
+     * If the dimensions in the model are not fully specified, then
+     * they must be fully specified here, unless hasNoValue is set to
+     * true. If the dimensions in the model are fully specified, then
+     * either dimensions.size() may be 0, or the dimensions in the
+     * model must be identical to the dimensions here.
+     */
+    vec<uint32_t> dimensions;
+};
+
+/**
+ * Inputs to be sent to and outputs to be retrieved from a prepared model.
+ *
+ * A Request serves two primary tasks:
+ * 1) Provides the input and output data to be used when executing the model.
+ * 2) Specifies any updates to the input operand metadata that were left
+ *    unspecified at model preparation time.
+ *
+ * An output must not overlap with any other output, with an input, or
+ * with an operand of lifetime CONSTANT_REFERENCE.
+ */
+struct Request {
+    /**
+     * Input data and information to be used in the execution of a prepared
+     * model.
+     *
+     * The index of the input corresponds to the index in Model.inputIndexes.
+     *   E.g., input[i] corresponds to Model.inputIndexes[i].
+     */
+    vec<RequestArgument> inputs;
+
+    /**
+     * Output data and information to be used in the execution of a prepared
+     * model.
+     *
+     * The index of the output corresponds to the index in Model.outputIndexes.
+     *   E.g., output[i] corresponds to Model.outputIndexes[i].
+     */
+    vec<RequestArgument> outputs;
+
+    /**
+     * A collection of shared memory pools containing operand data for both the
+     * inputs and the outputs to a model.
+     */
+    vec<memory> pools;
+};
+
+/**
+ * Return status of a function.
+ */
+enum ErrorStatus : int32_t {
+    NONE,
+    DEVICE_UNAVAILABLE,
+    GENERAL_FAILURE,
+    OUTPUT_INSUFFICIENT_SIZE,
+    INVALID_ARGUMENT,
+};
diff --git a/neuralnetworks/1.1/types.hal b/neuralnetworks/1.1/types.hal
index 73705bb..3d78fb6 100644
--- a/neuralnetworks/1.1/types.hal
+++ b/neuralnetworks/1.1/types.hal
@@ -26,7 +26,6 @@
  * The type of an operation in a model.
  */
 enum OperationType : @1.0::OperationType {
-
     /**
      * BatchToSpace for N-dimensional tensors.
      *
@@ -41,7 +40,8 @@
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
-     * Supported tensor rank: 4
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+     * and Channels) data layout.
      *
      * Inputs:
      * * 0: An n-D tensor, specifying the tensor to be reshaped
@@ -51,8 +51,8 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     BATCH_TO_SPACE_ND = 29,
 
@@ -91,8 +91,6 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 28.
      */
     DIV = 30,
 
@@ -126,8 +124,8 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be same as input0.
      */
     MEAN = 31,
 
@@ -138,7 +136,8 @@
      *
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
-     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (the pad value is undefined)
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *   (the pad value is undefined)
      *
      * Supported tensor rank: up to 4
      *
@@ -160,11 +159,8 @@
      *      of the padding:
      *          output0.dimension[i] =
      *              padding[i, 0] + input0.dimension[i] + padding[i, 1]
-     *
-     *      NOTE: The pad value for {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM}
-     *      is undefined.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     PAD = 32,
 
@@ -182,8 +178,10 @@
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *   (the pad value is undefined)
      *
-     * Supported tensor rank: 4
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+     * and Channels) data layout.
      *
      * Inputs:
      * * 0: An n-D tensor, specifying the input.
@@ -201,8 +199,8 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     SPACE_TO_BATCH_ND = 33,
 
@@ -232,8 +230,8 @@
      * * 0: A tensor of the same {@link OperandType} as input0. Contains the
      *      same data as input, but has one or more dimensions of size 1
      *      removed.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     SQUEEZE = 34,
 
@@ -278,8 +276,8 @@
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k),
      *      where k is the number of bits set in shrink_axis_mask.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     STRIDED_SLICE = 35,
 
@@ -318,8 +316,6 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 28.
      */
     SUB = 36,
 
@@ -345,11 +341,10 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     TRANSPOSE = 37,
-
 };
 
 /**
diff --git a/neuralnetworks/1.1/types.t b/neuralnetworks/1.1/types.t
new file mode 100644
index 0000000..75ac2e7
--- /dev/null
+++ b/neuralnetworks/1.1/types.t
@@ -0,0 +1,158 @@
+%% template file for generating types.hal.
+%% see frameworks/ml/nn/tools/api/README.md.
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.neuralnetworks@1.1;
+
+import @1.0::Operand;
+import @1.0::OperationType;
+import @1.0::PerformanceInfo;
+
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
+enum OperationType : @1.0::OperationType {
+%insert Operation_1.1
+};
+
+/**
+ * The capabilities of a driver.
+ */
+struct Capabilities {
+    /**
+     * Driver performance when operating on float32 data.
+     */
+    PerformanceInfo float32Performance;
+
+    /**
+     * Driver performance when operating on asymmetric 8-bit quantized data.
+     */
+    PerformanceInfo quantized8Performance;
+
+    /**
+     * Driver performance when operating on float32 data but performing
+     * calculations with range and/or precision as low as that of the IEEE
+     * 754 16-bit floating-point format.
+     */
+    PerformanceInfo relaxedFloat32toFloat16Performance;
+};
+
+/**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+    /**
+     * The operation type.
+     */
+    OperationType type;
+
+    /**
+     * Describes the table that contains the indexes of the inputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
+    vec<uint32_t> inputs;
+
+    /**
+     * Describes the table that contains the indexes of the outputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
+    vec<uint32_t> outputs;
+};
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * may not be known is the shape of the input tensors.
+ */
+struct Model {
+    /**
+     * All operands included in the model.
+     */
+    vec<Operand> operands;
+
+    /**
+     * All operations included in the model.
+     *
+     * The operations are sorted into execution order. Every operand
+     * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
+     * written before it is read.
+     */
+    vec<Operation> operations;
+
+    /**
+     * Input indexes of the model. There must be at least one.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
+    vec<uint32_t> inputIndexes;
+
+    /**
+     * Output indexes of the model. There must be at least one.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
+    vec<uint32_t> outputIndexes;
+
+    /**
+     * A byte buffer containing operand data that were copied into the model.
+     *
+     * An operand's value must be located here if and only if Operand::lifetime
+     * equals OperandLifeTime::CONSTANT_COPY.
+     */
+    vec<uint8_t> operandValues;
+
+    /**
+     * A collection of shared memory pools containing operand values.
+     *
+     * An operand's value must be located here if and only if Operand::lifetime
+     * equals OperandLifeTime::CONSTANT_REFERENCE.
+     */
+    vec<memory> pools;
+
+    /**
+     * 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
+     * precision as low as that of the IEEE 754 16-bit floating-point format.
+     * 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
+     * range and precision of the IEEE 754 32-bit floating-point format.
+     */
+    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/1.2/types.hal b/neuralnetworks/1.2/types.hal
index f368ce2..837ced5 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -43,8 +43,6 @@
      *
      * Values of this operand type are either true or false. A zero value
      * represents false; any other value represents true.
-     *
-     * Available since API level 29.
      */
     BOOL = 6,
     /**
@@ -55,14 +53,10 @@
      * realValue = integerValue * scale.
      *
      * scale is a 32 bit floating point with value greater than zero.
-     *
-     * Available since API level 29.
      */
     TENSOR_QUANT16_SYMM = 7,
     /**
      * A tensor of IEEE 754 16 bit floating point values.
-     *
-     * Available since API level 29.
      */
     TENSOR_FLOAT16 = 8,
     /**
@@ -70,14 +64,10 @@
      *
      * Values of this operand type are either true or false. A zero value
      * represents false; any other value represents true.
-     *
-     * Available since API level 29.
      */
     TENSOR_BOOL8 = 9,
     /**
      * An IEEE 754 16 bit floating point scalar value.
-     *
-     * Available since API level 29.
      */
     FLOAT16 = 10,
     /**
@@ -90,14 +80,13 @@
      * - scales: an array of positive 32 bit floating point values.
      * The size of the scales array must be equal to dimensions[channelDim].
      *
+     *{@link SymmPerChannelQuantParams} must hold the parameters for an Operand of this type.
      * The channel dimension of this tensor must not be unknown (dimensions[channelDim] != 0).
      *
      * The formula is:
      * realValue[..., C, ...] =
      *     integerValue[..., C, ...] * scales[C]
      * where C is an index in the Channel dimension.
-     *
-     * Available since API level 29.
      */
     TENSOR_QUANT8_SYMM_PER_CHANNEL = 11,
     /**
@@ -110,8 +99,6 @@
      *
      * The formula is:
      * real_value = (integer_value - zeroPoint) * scale.
-     *
-     * Available since API level 29.
      */
     TENSOR_QUANT16_ASYMM = 12,
     /**
@@ -122,20 +109,19 @@
      * realValue = integerValue * scale.
      *
      * scale is a 32 bit floating point with value greater than zero.
-     *
-     * Available since API level 29.
      */
     TENSOR_QUANT8_SYMM = 13,
+
     /*
-     * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
-     * OEM operation and data types.
+     * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+     * alternative to OEM operation and data types.
      *
      * OEM specific scalar value.
      * OEM                 = 10000,
      */
     /*
-     * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
-     * OEM operation and data types.
+     * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+     * alternative to OEM operation and data types.
      *
      * A tensor of OEM specific values.
      * TENSOR_OEM_BYTE     = 10001,
@@ -166,6 +152,7 @@
  * The type of an operation in a model.
  */
 enum OperationType : int32_t {
+
     /**
      * Adds two tensors, element-wise.
      *
@@ -187,12 +174,12 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Since API level 29, generic zero-sized input tensor is supported. Zero
+     * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
      * dimension is only compatible with 0 or 1. The size of the output
      * dimension is zero if either of corresponding input dimension is zero.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -202,14 +189,16 @@
      * * 0: A tensor.
      * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
      *      as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scales and zeroPoint can be different from input0 scale and zeroPoint.
      * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
      *      {@link FusedActivationFunc} values. Specifies the activation to
      *      invoke on the result.
      *
      * Outputs:
      * * 0: The sum, a tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     ADD = @1.1::OperationType:ADD,
 
@@ -227,7 +216,7 @@
      *         ) / sum(1)
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -235,13 +224,14 @@
      * With the default data layout NHWC, the data is stored in the order of:
      * [batch, height, width, channels]. Alternatively, the data layout could
      * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
      *
      * Both explicit padding and implicit padding are supported.
      *
      * Inputs (explicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input. Since API level 29, zero batches is supported for this
-     *      tensor.
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
      *      the left, in the ‘width’ dimension.
      * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -263,12 +253,12 @@
      *      invoke on the result.
      * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
      *       Set to true to specify NCHW data layout for input0 and output0.
-     *       Available since API level 29.
+     *       Available since HAL version 1.2.
      *
      * Inputs (implicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input. Since API level 29, zero batches is supported for this
-     *      tensor.
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
      *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -285,13 +275,13 @@
      *      invoke on the result.
      * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
      *      Set to true to specify NCHW data layout for input0 and output0.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, out_height, out_width, depth].
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     AVERAGE_POOL_2D = @1.1::OperationType:AVERAGE_POOL_2D,
 
@@ -302,33 +292,34 @@
      * dimensions except the dimension along the concatenation axis.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
-     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (full support since API
-     *   level 29, see the input section)
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *   (full support since HAL version 1.2, see the input section)
      *
      * Supported tensor rank: up to 4
      *
      * Inputs:
      * * 0 ~ n-1: The list of n input tensors, of shape
      *            [D0, D1, ..., Daxis(i), ..., Dm].
-     *            Before API level 29, all input tensors of
+     *            Before HAL version 1.2, all input tensors of
      *            {@link OperandType::TENSOR_QUANT8_ASYMM}
      *            must have the same scale and zeroPoint as the output tensor.
-     *            Since API level 29, zero-sized tensors are supported.
+     *            Since HAL version 1.2, zero-sized tensors are supported.
      * * n: An {@link OperandType::INT32} scalar, specifying the
      *      concatenation axis.
      *
      * Outputs:
      * * 0: The output, a tensor of the same {@link OperandType} as the input
      *      tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
-     *
-     * Available since API level 27.
+     *      Since HAL version 1.2, for a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint values can be different from
+     *      input tensors. Before HAL version 1.2 they have to be the same as for the input tensors.
      */
     CONCATENATION = @1.1::OperationType:CONCATENATION,
 
     /**
-     * Performs an 2-D convolution operation.
+     * Performs a 2-D convolution operation.
      *
      * The CONV_2D op sweeps a 2-D filter that can mix channels together over a
      * batch of images, applying the filter to each window of each image of the
@@ -354,7 +345,7 @@
      * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
      * * * input.scale * filter.scale).
      *
-     * Available since API level 29:
+     * Available since HAL version 1.2:
      * * 16 bit floating point:
      * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
      *
@@ -368,27 +359,29 @@
      * With the default data layout NHWC, the data is stored in the order of:
      * [batch, height, width, channels]. Alternatively, the data layout could
      * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
      *
      * Both explicit padding and implicit padding are supported.
      *
      * Inputs (explicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
-     *      specifying the input. Since API level 29, zero batches is supported
-     *      for this tensor.
+     *      specifying the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
      * * 1: A 4-D tensor, of shape
      *      [depth_out, filter_height, filter_width, depth_in], specifying the
-     *      filter. For tensor of type
-     *      {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
-     *      dimension (extraParams.channelQuant.channelDim) must be set to 0.
+     *      filter.
+     *      For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+     *      the channel dimension (SymmPerChannelQuantParams::channelDim)
+     *      must be set to 0.
      * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
-     *      tensor of type {@link OperandType::TENSOR_FLOAT32} or
-     *      {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32}
+     *      or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
      *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
-     *      of 0 and bias_scale == input_scale * filter_scale. For filter tensor
-     *      of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
-     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
-     *      0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+     *      of 0 and bias_scale == input_scale * filter_scale.
+     *      For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+     *      and bias_scale of 0. The actual scale of each value 'i' is equal to
      *      bias_scale[i] = input_scale * filter_scale[i].
      * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
      *      the left, in the ‘width’ dimension.
@@ -407,36 +400,37 @@
      *      invoke on the result.
      * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
      *      Set to true to specify NCHW data layout for input0 and output0.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      * * 11: An optional {@link OperandType::INT32} scalar, specifying the dilation
      *      factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
      *      cells between each filter element on width dimension. If this input is set,
      *      input 12 (dilation factor for height) must be specified as well.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      * * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
      *      factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
      *      cells between each filter element on height dimension. If this input is set,
      *      input 11 (dilation factor for width) must be specified as well.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
      * Inputs (implicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
-     *      specifying the input. Since API level 29, zero batches is supported
-     *      for this tensor.
+     *      specifying the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
      * * 1: A 4-D tensor, of shape
      *      [depth_out, filter_height, filter_width, depth_in], specifying the
-     *      filter. For tensor of type
-     *      {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
-     *      dimension (extraParams.channelQuant.channelDim) must be set to 0.
+     *      filter.
+     *      For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+     *      the channel dimension (SymmPerChannelQuantParams::channelDim)
+     *      must be set to 0.
      * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
-     *      tensor of type {@link OperandType::TENSOR_FLOAT32} or
-     *      {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32}
+     *      or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
      *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
-     *      of 0 and bias_scale == input_scale * filter_scale. For filter tensor
-     *      of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
-     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
-     *      0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+     *      of 0 and bias_scale == input_scale * filter_scale.
+     *      For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+     *      and bias_scale of 0. The actual scale of each value 'i' is equal to
      *      bias_scale[i] = input_scale * filter_scale[i].
      * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
      *      padding scheme, has to be one of the
@@ -450,26 +444,23 @@
      *      invoke on the result.
      * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
      *      Set to true to specify NCHW data layout for input0 and output0.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      * * 8: An optional {@link OperandType::INT32} scalar, specifying the dilation
      *      factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
      *      cells between each filter element on width dimension. If this input is set,
      *      input 9 (dilation factor for height) must be specified as well.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      * * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
      *      factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
      *      cells between each filter element on height dimension. If this input is set,
      *      input 8 (dilation factor for width) must be specified as well.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
      * Outputs:
      * * 0: The output 4-D tensor, of shape
-     *      [batches, out_height, out_width, depth_out]. Before API level 29,
-     *      for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the
-     *      following condition must be satisfied:
-     *      output_scale > input_scale * filter_scale
-     *
-     * Available since API level 27.
+     *      [batches, out_height, out_width, depth_out].
+     *      Before HAL version 1.2, for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the following condition must be satisfied: output_scale > input_scale * filter_scale
      */
     CONV_2D = @1.1::OperationType:CONV_2D,
 
@@ -504,7 +495,7 @@
      * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
      * * * input.scale * filter.scale).
      *
-     * Available since API level 29:
+     * Available since HAL version 1.2:
      * * 16 bit floating point:
      * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
      *
@@ -518,6 +509,7 @@
      * With the default data layout NHWC, the data is stored in the order of:
      * [batch, height, width, channels]. Alternatively, the data layout could
      * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
      *
      * Both explicit padding and implicit padding are supported.
      *
@@ -525,18 +517,19 @@
      * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
      *      specifying the input.
      * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
-     *      specifying the filter. For tensor of type
-     *      {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
-     *      dimension (extraParams.channelQuant.channelDim) must be set to 3.
+     *      specifying the filter.
+     *      For tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
+     *      the channel dimension (SymmPerChannelQuantParams::channelDim)
+     *      must be set to 3.
      * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
-     *      tensor of type {@link OperandType::TENSOR_FLOAT32} or
-     *      {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32}
+     *      or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
      *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
-     *      of 0 and bias_scale == input_scale * filter_scale. For filter tensor
-     *      of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
-     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
-     *      0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+     *      of 0 and bias_scale == input_scale * filter_scale.
+     *      For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+     *      and bias_scale of 0. The actual scale of each value 'i' is equal to
      *      bias_scale[i] = input_scale * filter_scale[i].
      * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
      *      the left, in the ‘width’ dimension.
@@ -557,17 +550,17 @@
      *       invoke on the result.
      * * 11: An optional {@link OperandType::BOOL} scalar, default to false.
      *       Set to true to specify NCHW data layout for input0 and output0.
-     *       Available since API level 29.
+     *       Available since HAL version 1.2.
      * * 12: An optional {@link OperandType::INT32} scalar, specifying the dilation
      *      factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
      *      cells between each filter element on width dimension. If this input is set,
      *      input 13 (dilation factor for height) must be specified as well.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      * * 13: An optional {@link OperandType::INT32} scalar, specifying the dilation
      *      factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
      *      cells between each filter element on height dimension. If this input is set,
      *      input 12 (dilation factor for width) must be specified as well.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
      * Inputs (implicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -575,14 +568,14 @@
      * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
      *      specifying the filter.
      * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
-     *      tensor of type {@link OperandType::TENSOR_FLOAT32} or
-     *      {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
+     *      tensor of type {@link OperandType::TENSOR_FLOAT32}
+     *      or {@link OperandType::TENSOR_FLOAT16} the bias must be of the same
      *      type. For filter tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint
-     *      of 0 and bias_scale == input_scale * filter_scale. For filter tensor
-     *      of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}, the bias
-     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
-     *      0 and bias_scale of 0. The actual scale of each value 'i' is equal to
+     *      of 0 and bias_scale == input_scale * filter_scale.
+     *      For filter tensor of {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL},
+     *      the bias should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0
+     *      and bias_scale of 0. The actual scale of each value 'i' is equal to
      *      bias_scale[i] = input_scale * filter_scale[i].
      * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
      *      padding scheme, has to be one of the
@@ -598,27 +591,24 @@
      *      invoke on the result.
      * * 8: An optional {@link OperandType::BOOL} scalar, default to false.
      *      Set to true to specify NCHW data layout for input0 and output0.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      * * 9: An optional {@link OperandType::INT32} scalar, specifying the dilation
      *      factor for width. Defaults to 1. If set to k > 1, there will be k-1 skipped
      *      cells between each filter element on width dimension. If this input is set,
      *      input 10 (dilation factor for height) must be specified as well.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      * * 10: An optional {@link OperandType::INT32} scalar, specifying the dilation
      *      factor for height. Defaults to 1. If set to k > 1, there will be k-1 skipped
      *      cells between each filter element on height dimension. If this input is set,
      *      input 9 (dilation factor for width) must be specified as well.
-     *      Available since API level 29.
-
+     *      Available since HAL version 1.2.
      *
      * Outputs:
      * * 0: The output 4-D tensor, of shape
-     *      [batches, out_height, out_width, depth_out]. Before API level 29,
-     *      for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the
-     *      following condition must be satisfied:
+     *      [batches, out_height, out_width, depth_out]. Before HAL version 1.2, for
+     *      output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the following condition must be satisfied:
      *      output_scale > input_scale * filter_scale
-     *
-     * Available since API level 27.
      */
     DEPTHWISE_CONV_2D = @1.1::OperationType:DEPTHWISE_CONV_2D,
 
@@ -638,7 +628,7 @@
      * be divisible by block_size * block_size
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -646,6 +636,7 @@
      * With the default data layout NHWC, the data is stored in the order of:
      * [batch, height, width, channels]. Alternatively, the data layout could
      * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
      *
      * Inputs:
      * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -655,13 +646,13 @@
      *      of the input depth.
      * * 2: An optional {@link OperandType::BOOL} scalar, default to false.
      *      Set to true to specify NCHW data layout for input0 and output0.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
      * Outputs:
      * * 0: The output 4-D tensor, of shape [batch, height*block_size,
      *      width*block_size, depth/(block_size*block_size)].
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     DEPTH_TO_SPACE = @1.1::OperationType:DEPTH_TO_SPACE,
 
@@ -674,22 +665,21 @@
      *
      * Supported input tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
-     * * {@link OperandType::TENSOR_QUANT8_SYMM} (since API level 29)
-     * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since API level 29)
+     * * {@link OperandType::TENSOR_QUANT8_SYMM} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} (since HAL version 1.2)
      *
      * Supported output tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}.
      *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * * 0: A tensor. Since API level 29, this tensor may be zero-sized.
+     * * 0: A tensor.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
      *
      * Outputs:
      * * 0: A tensor with the same shape as input0.
-     *
-     * Available since API level 27.
      */
     DEQUANTIZE = @1.1::OperationType:DEQUANTIZE,
 
@@ -730,8 +720,8 @@
      * * 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.
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input1.
      */
     EMBEDDING_LOOKUP = @1.1::OperationType:EMBEDDING_LOOKUP,
 
@@ -739,7 +729,7 @@
      * Computes element-wise floor() on the input tensor.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Supported tensor rank: up to 4
@@ -750,8 +740,6 @@
      * Outputs:
      * * 0: The output tensor, of the same {@link OperandType} and dimensions as
      *      the input tensor.
-     *
-     * Available since API level 27.
      */
     FLOOR = @1.1::OperationType:FLOOR,
 
@@ -764,7 +752,7 @@
      *     outputs = activation(inputs * weights’ + bias)
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -777,8 +765,8 @@
      *      [batch_size, input_size], where "input_size" corresponds to the
      *      number of inputs to the layer, matching the second dimension of
      *      weights, and "batch_size" is calculated by dividing the number of
-     *      elements by "input_size". Since API level 29, zero batch_size is
-     *      supported for this tensor.
+     *      elements by "input_size".
+     *      Since HAL version 1.2, zero batch_size is supported for this tensor.
      * * 1: A 2-D tensor, specifying the weights, of shape
      *      [num_units, input_size], where "num_units" corresponds to the number
      *      of output nodes.
@@ -793,12 +781,9 @@
      *      invoke on the result.
      *
      * Outputs:
-     * * 0: The output tensor, of shape [batch_size, num_units]. Before API
-     *      level 29, For output tensor of {@link
-     *      OperandType::TENSOR_QUANT8_ASYMM}, the following condition must be
-     *      satisfied: output_scale > input_scale * filter_scale.
-     *
-     * Available since API level 27.
+     * * 0: The output tensor, of shape [batch_size, num_units]. Before HAL version 1.2, for
+     *      output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
+     *      condition must be satisfied: output_scale > input_scale * filter_scale.
      */
     FULLY_CONNECTED = @1.1::OperationType:FULLY_CONNECTED,
 
@@ -849,13 +834,13 @@
      *
      * Outputs:
      * * 0: Output. A tensor with shape [ k …].
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input2.
      * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
      *      hits (True) or not (False).
      *      Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0
      *      and scale 1.0f.
      *      A non-zero byte represents True, a hit. A zero indicates otherwise.
-     *
-     * Available since API level 27.
      */
     HASHTABLE_LOOKUP = @1.1::OperationType:HASHTABLE_LOOKUP,
 
@@ -872,12 +857,12 @@
      * 1-D slice along dimension dim.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
-     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
      *
      * Supported tensor rank: up to 4
-     * Tensors with rank less than 4 are only supported since API level 29.
+     * Tensors with rank less than 4 are only supported since HAL version 1.2.
      *
      * Inputs:
      * * 0: An n-D tensor, specifying the tensor to be normalized.
@@ -885,14 +870,12 @@
      *      specifying the dimension normalization would be performed on.
      *      Negative index is used to specify axis from the end (e.g. -1 for
      *      the last axis). Must be in the range [-n, n).
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} and same shape as input0.
      *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the scale must be 1.f / 128 and the zeroPoint must be 128.
-     *
-     * Available since API level 27.
      */
     L2_NORMALIZATION = @1.1::OperationType:L2_NORMALIZATION,
 
@@ -909,20 +892,21 @@
      *              sum(1))
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
      * With the default data layout NHWC, the data is stored in the order of:
      * [batch, height, width, channels]. Alternatively, the data layout could
      * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
      *
      * Both explicit padding and implicit padding are supported.
      *
      * Inputs (explicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input. Since API level 29, zero batches is supported for this
-     *      tensor.
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
      *      the left, in the ‘width’ dimension.
      * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -944,12 +928,12 @@
      *      invoke on the result.
      * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
      *       Set to true to specify NCHW data layout for input0 and output0.
-     *       Available since API level 29.
+     *       Available since HAL version 1.2.
      *
      * Inputs (implicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input. Since API level 29, zero batches is supported for this
-     *      tensor.
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
      *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -966,13 +950,11 @@
      *      invoke on the result.
      * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
      *      Set to true to specify NCHW data layout for input0 and output0.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, out_height, out_width, depth].
-     *
-     * Available since API level 27.
      */
     L2_POOL_2D = @1.1::OperationType:L2_POOL_2D,
 
@@ -994,11 +976,11 @@
      * 1-D slice along specified dimension.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Supported tensor rank: up to 4
-     * Tensors with rank less than 4 are only supported since API level 29.
+     * Tensors with rank less than 4 are only supported since HAL version 1.2.
      *
      * Inputs:
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
@@ -1011,10 +993,10 @@
      *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
      *      value must be of {@link OperandType::FLOAT32}.
      * * 3: A scalar, specifying the scale factor, alpha.
-     *      For input tensor of {@link OperandType::TENSOR_FLOAT16}, the alpha
-     *      value must be of {@link OperandType::FLOAT16}.
-     *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the alpha
-     *      value must be of {@link OperandType::FLOAT32}.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT16}, the
+     *      alpha value must be of {@link OperandType::FLOAT16}.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the
+     *      alpha value must be of {@link OperandType::FLOAT32}.
      * * 4: A scalar, specifying the exponent, beta.
      *      For input tensor of {@link OperandType::TENSOR_FLOAT16}, the beta
      *      value must be of {@link OperandType::FLOAT16}.
@@ -1024,12 +1006,10 @@
      *      specifying the dimension normalization would be performed on.
      *      Negative index is used to specify axis from the end (e.g. -1 for
      *      the last axis). Must be in the range [-n, n).
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 27.
      */
     LOCAL_RESPONSE_NORMALIZATION = @1.1::OperationType:LOCAL_RESPONSE_NORMALIZATION,
 
@@ -1041,22 +1021,20 @@
      *     output = 1 / (1 + exp(-input))
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * * 0: A tensor, specifying the input. Since API level 29, this tensor may
-     *      be zero-sized.
+     * * 0: A tensor, specifying the input.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
      *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the scale must be 1.f / 256 and the zeroPoint must be 0.
-     *
-     * Available since API level 27.
      */
     LOGISTIC = @1.1::OperationType:LOGISTIC,
 
@@ -1064,7 +1042,7 @@
      * Projects an input to a bit vector via locality senstive hashing.
      *
      * Supported input tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_INT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
@@ -1086,7 +1064,7 @@
      *      Tensor[1].Dim[0] == Tensor[2].Dim[0]
      * * 3: Type:
      *        Sparse:
-     *          Value LSHProjectionType_SPARSE(=3) (since API level 29).
+     *          Value LSHProjectionType_SPARSE(=3) (since HAL version 1.2).
      *          Computed bit vector is considered to be sparse.
      *          Each output element is an int32 made up of multiple bits
      *          computed from hash functions.
@@ -1107,14 +1085,12 @@
      * Outputs:
      * * 0: If the projection type is Sparse:
      *      Output.Dim == { Tensor[0].Dim[0] }
-     *      A tensor of int32 that represents hash signatures,
+     *      A tensor of int32 that represents hash signatures.
      *
      *      If the projection type is Dense:
      *      Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] }
      *      A flattened tensor that represents projected bit vectors.
-     *
-     * Available since API level 27.
-     * The offset value for sparse projections was added in API level 29.
+     * The offset value for sparse projections was added in HAL version 1.2.
      */
     LSH_PROJECTION = @1.1::OperationType:LSH_PROJECTION,
 
@@ -1170,7 +1146,7 @@
      *   matrix, each element of which is the product of the corresponding
      *   elements of the input matrices.
      *
-     * Since API level 29 LSTM supports layer normalization.
+     * Since HAL version 1.2 LSTM supports layer normalization.
      * In case layer normalization is used, the inputs to internal activation
      * functions (sigmoid and \f$g\f$) are normalized, rescaled and recentered
      * following an approach from section 3.1 from
@@ -1197,7 +1173,7 @@
      * * 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.
-     * * (API level >= 29) The four layer normalization weights either all have
+     * * (HAL version 1.2 or later) The four layer normalization weights either all have
      *   values or none of them have values. Additionally, if CIFG is used,
      *   input layer normalization weights tensor is omitted and the other layer
      *   normalization weights either all have values or none of them have
@@ -1228,7 +1204,7 @@
      * Jimmy Ba et al. "Layer Normalization"
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * All input and output tensors must be of the same type.
@@ -1291,24 +1267,24 @@
      * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such
      *      that values are bound within [-cell_clip, cell_clip]. If set to 0.0
      *      then clipping is disabled.
-     *      Until API level 29 this scalar must be of type {@link
-     *      FLOAT32}. Since API level 29, if all the input
+     *      Until HAL version 1.2 this scalar must be of type {@link
+     *      OperandType::FLOAT32}. Since HAL version 1.2, if all the input
      *      tensors have type {@link OperandType::TENSOR_FLOAT32}, this
      *      scalar must be of the type {@link OperandType::FLOAT32},
      *      otherwise if all the input tensors have the type {@link
-     *      TENSOR_FLOAT16}, this scalar must be of type {@link
-     *      FLOAT16}.
+     *      OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link
+     *      OperandType::FLOAT16}.
      * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the
      *      projection layer, such that values are bound within
      *      [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
-     *      Until API level 29 this scalar must be of type {@link
-     *      FLOAT32}. Since API level 29, if all the input
+     *      Until HAL version 1.2 this scalar must be of type {@link
+     *      OperandType::FLOAT32}. Since HAL version 1.2, if all the input
      *      tensors have type {@link OperandType::TENSOR_FLOAT32}, this
      *      scalar must be of the type {@link OperandType::FLOAT32},
      *      otherwise if all the input tensors have the type {@link
-     *      TENSOR_FLOAT16}, this scalar must be of type {@link
-     *      FLOAT16}.
-     * Since API level 29 there are additional inputs to this op:
+     *      OperandType::TENSOR_FLOAT16}, this scalar must be of type {@link
+     *      OperandType::FLOAT16}.
+     * Since HAL version 1.2 there are additional inputs to this op:
      * * 23:The input layer normalization weights.
      *      A 1-D tensor of shape [num_units]. Used to rescale normalized inputs
      *      to activation at input gate.
@@ -1333,8 +1309,6 @@
      * * 3: The output (\f$o_t\f$).
      *      A 2-D tensor of shape [batch_size, output_size]. This is effectively
      *      the same as the current “output state (out)” value.
-     *
-     * Available since API level 27.
      */
     LSTM = @1.1::OperationType:LSTM,
 
@@ -1352,7 +1326,7 @@
      *         )
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -1360,13 +1334,14 @@
      * With the default data layout NHWC, the data is stored in the order of:
      * [batch, height, width, channels]. Alternatively, the data layout could
      * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
      *
      * Both explicit padding and implicit padding are supported.
      *
      * Inputs (explicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input. Since API level 29, zero batches is supported for this
-     *      tensor.
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
      *      the left, in the ‘width’ dimension.
      * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
@@ -1388,12 +1363,12 @@
      *      invoke on the result.
      * * 10: An optional {@link OperandType::BOOL} scalar, default to false.
      *       Set to true to specify NCHW data layout for input0 and output0.
-     *       Available since API level 29.
+     *       Available since HAL version 1.2.
      *
      * Inputs (implicit padding):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input. Since API level 29, zero batches is supported for this
-     *      tensor.
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
      *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
@@ -1410,13 +1385,13 @@
      *      invoke on the result.
      * * 7: An optional {@link OperandType::BOOL} scalar, default to false.
      *      Set to true to specify NCHW data layout for input0 and output0.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, out_height, out_width, depth].
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     MAX_POOL_2D = @1.1::OperationType:MAX_POOL_2D,
 
@@ -1435,15 +1410,15 @@
      * of the input operands. It starts with the trailing dimensions, and works
      * its way forward.
      *
-     * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
-     * * {@link OperandType::TENSOR_FLOAT32}
-     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
-     *
-     * Since API level 29, generic zero-sized input tensor is supported. Zero
+     * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
      * dimension is only compatible with 0 or 1. The size of the output
      * dimension is zero if either of corresponding input dimension is zero.
      *
+     * Supported tensor {@link OperandType}:
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
@@ -1459,8 +1434,6 @@
      *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the following condition must be satisfied:
      *      output_scale > input1_scale * input2_scale.
-     *
-     * Available since API level 27.
      */
     MUL = @1.1::OperationType:MUL,
 
@@ -1472,20 +1445,20 @@
      *     output = max(0, input)
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * * 0: A tensor, specifying the input. Since API level 29, this tensor may
-     *      be zero-sized.
+     * * 0: A tensor, specifying the input.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     RELU = @1.1::OperationType:RELU,
 
@@ -1497,20 +1470,20 @@
      *     output = min(1.f, max(-1.f, input))
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * * 0: A tensor, specifying the input. Since API level 29, this tensor may
-     *      be zero-sized.
+     * * 0: A tensor, specifying the input.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
      *
      * Outputs:
-     * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 27.
+     * * 0: The output tensor of the same shape as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     RELU1 = @1.1::OperationType:RELU1,
 
@@ -1522,20 +1495,20 @@
      *     output = min(6, max(0, input))
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * * 0: A tensor, specifying the input. Since API level 29, this tensor may
-     *      be zero-sized.
+     * * 0: A tensor, specifying the input.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     RELU6 = @1.1::OperationType:RELU6,
 
@@ -1546,7 +1519,7 @@
      * tensor, but with a newly specified shape.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -1565,8 +1538,8 @@
      *
      * Outputs:
      * * 0: The output tensor, of shape specified by the input shape.
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     RESHAPE = @1.1::OperationType:RESHAPE,
 
@@ -1578,30 +1551,31 @@
      * same as corner pixels of input.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
-     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
      *
      * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
      * With the default data layout NHWC, the data is stored in the order of:
      * [batch, height, width, channels]. Alternatively, the data layout could
      * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
      *
      * Both resizing by shape and resizing by scale are supported.
      *
      * Inputs (resizing by shape):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
-     *      the input. Since API level 29, zero batches is supported for this
-     *      tensor.
+     *      the input.
+     *      Since HAL version 1.2, zero batches is supported for this tensor.
      * * 1: An {@link OperandType::INT32} scalar, specifying the output
      *      width of the output tensor.
      * * 2: An {@link OperandType::INT32} scalar, specifying the output
      *      height of the output tensor.
      * * 3: An optional {@link OperandType::BOOL} scalar, default to false.
      *      Set to true to specify NCHW data layout for input0 and output0.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
-     * Inputs (resizing by scale, since API level 29):
+     * Inputs (resizing by scale, since HAL version 1.2):
      * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
      *      the input. Zero batches is supported for this tensor.
      * * 1: A scalar, specifying width_scale, the scaling factor of the width
@@ -1622,8 +1596,8 @@
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, new_height, new_width, depth].
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     RESIZE_BILINEAR = @1.1::OperationType:RESIZE_BILINEAR,
 
@@ -1644,7 +1618,7 @@
      *   argument (if not “NONE”).
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * The input tensors must all be the same type.
@@ -1676,8 +1650,6 @@
      * * 1: output.
      *      A 2-D tensor of shape [batch_size, num_units]. This is effectively
      *      the same as the current state value.
-     *
-     * Available since API level 27.
      */
     RNN = @1.1::OperationType:RNN,
 
@@ -1696,34 +1668,32 @@
      * independently on each 1-D slice along specified dimension.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: up to 4.
-     * Tensors with rank other than 2 or 4 are only supported since API level 29.
+     * Tensors with rank other than 2 or 4 are only supported since HAL version 1.2.
      *
      * Inputs:
-     * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped. Since
-     *      API level 29, this tensor may be zero-sized.
+     * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
      * * 1: A scalar, specifying the positive scaling factor for the exponent,
      *      beta. If input0 is of {@link OperandType::TENSOR_FLOAT32} or
      *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the scalar must be of
-     *      {@link OperandType::FLOAT32}. If input0 is of {@link
-     *      OperandType::TENSOR_FLOAT16}, then the scalar must be of {@link
-     *      OperandType::FLOAT16}.
+     *      {@link OperandType::FLOAT32}.
+     *      If input0 is of {@link OperandType::TENSOR_FLOAT16}, then the
+     *      scalar must be of {@link OperandType::FLOAT16}.
      * * 2: An optional {@link OperandType::INT32} scalar, default to -1,
      *      specifying the dimension the activation would be performed on.
      *      Negative index is used to specify axis from the end (e.g. -1 for
      *      the last axis). Must be in the range [-n, n).
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
      *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the scale must be 1.f / 256 and the zeroPoint must be 0.
-     *
-     * Available since API level 27.
      */
     SOFTMAX = @1.1::OperationType:SOFTMAX,
 
@@ -1742,7 +1712,7 @@
      * The input tensor's height and width must be divisible by block_size.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -1750,6 +1720,7 @@
      * With the default data layout NHWC, the data is stored in the order of:
      * [batch, height, width, channels]. Alternatively, the data layout could
      * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
      *
      * Inputs:
      * * 0: A 4-D tensor, of shape [batches, height, width, depth_in],
@@ -1759,13 +1730,13 @@
      *      input height and width.
      * * 2: An optional {@link OperandType::BOOL} scalar, default to false.
      *      Set to true to specify NCHW data layout for input0 and output0.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
      * Outputs:
      * * 0: The output 4-D tensor, of shape [batches, height/block_size,
      *      width/block_size, depth_in*block_size*block_size].
-     *
-     * Available since API level 27.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     SPACE_TO_DEPTH = @1.1::OperationType:SPACE_TO_DEPTH,
 
@@ -1809,7 +1780,7 @@
      * the filters.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * All input tensors must be the same type.
@@ -1843,8 +1814,6 @@
      * * 1: output.
      *      A 2-D tensor of the same {@link OperandType} as the inputs, with shape
      *      [batch_size, num_units].
-     *
-     * Available since API level 27.
      */
     SVDF = @1.1::OperationType:SVDF,
 
@@ -1856,22 +1825,20 @@
      *     output = tanh(input)
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
-     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
      *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * * 0: A tensor, specifying the input. Since API level 29, this tensor may
-     *      be zero-sized.
+     * * 0: A tensor, specifying the input.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
      *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the scale must be 1.f / 128 and the zeroPoint must be 128.
-     *
-     * Available since API level 27.
      */
     TANH = @1.1::OperationType:TANH,
 
@@ -1886,7 +1853,7 @@
      * This is the reverse of SpaceToBatch.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -1894,6 +1861,7 @@
      * With the default data layout NHWC, the data is stored in the order of:
      * [batch, height, width, channels]. Alternatively, the data layout could
      * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
      *
      * Inputs:
      * * 0: An n-D tensor, specifying the tensor to be reshaped
@@ -1906,8 +1874,8 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     BATCH_TO_SPACE_ND = @1.1::OperationType:BATCH_TO_SPACE_ND,
 
@@ -1931,12 +1899,12 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Since API level 29, generic zero-sized input tensor is supported. Zero
+     * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
      * dimension is only compatible with 0 or 1. The size of the output
      * dimension is zero if either of corresponding input dimension is zero.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Supported tensor rank: up to 4
@@ -1951,8 +1919,6 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 28.
      */
     DIV = @1.1::OperationType:DIV,
 
@@ -1965,7 +1931,7 @@
      * length 1.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -1987,21 +1953,21 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be same as input0.
      */
     MEAN = @1.1::OperationType:MEAN,
 
     /**
-     * Pads a tensor with zeros.
+     * Pads a tensor.
      *
      * This operation pads a tensor according to the specified paddings.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
-     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (full support since API
-     *   level 29, see the output section)
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *   (full support since HAL version 1.2, see the output section)
      *
      * Supported tensor rank: up to 4
      *
@@ -2023,12 +1989,12 @@
      *      of the padding:
      *          output0.dimension[i] =
      *              padding[i, 0] + input0.dimension[i] + padding[i, 1]
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      *
-     *      NOTE: Before API level 29, the pad value for
-     *      {@link ANEURALNETWORKS_TENSOR_QUANT8_ASYMM} is undefined.
-     *      Since API level 29, the pad value is always the logical zero.
-     *
-     * Available since API level 28.
+     *      NOTE: Before HAL version 1.2, the pad value for
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined.
+     *      Since HAL version 1.2, the pad value is always the logical zero.
      */
     PAD = @1.1::OperationType:PAD,
 
@@ -2044,14 +2010,16 @@
      * dimensions of the input are optionally zero padded according to paddings.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *   (full support since HAL version 1.2, see the output section)
      *
      * Supported tensor rank: 4, with "NHWC" or "NCHW" data layout.
      * With the default data layout NHWC, the data is stored in the order of:
      * [batch, height, width, channels]. Alternatively, the data layout could
      * be NCHW, the data storage order of: [batch, channels, height, width].
+     * NCHW is supported since HAL version 1.2.
      *
      * Inputs:
      * * 0: An n-D tensor, specifying the input.
@@ -2068,12 +2036,16 @@
      *      end of dimension i.
      * * 3: An optional {@link OperandType::BOOL} scalar, default to false.
      *      Set to true to specify NCHW data layout for input0 and output0.
-     *      Available since API level 29.
+     *      Available since HAL version 1.2.
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      *
-     * Available since API level 28.
+     *      NOTE: Before HAL version 1.2, the pad value for
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM} is undefined.
+     *      Since HAL version 1.2, the pad value is always the logical zero.
      */
     SPACE_TO_BATCH_ND = @1.1::OperationType:SPACE_TO_BATCH_ND,
 
@@ -2086,7 +2058,7 @@
      * dimensions by specifying the axes (input1).
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -2104,8 +2076,8 @@
      * * 0: A tensor of the same {@link OperandType} as input0. Contains the
      *      same data as input, but has one or more dimensions of size 1
      *      removed.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     SQUEEZE = @1.1::OperationType:SQUEEZE,
 
@@ -2119,7 +2091,7 @@
      * reverse slice.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -2151,8 +2123,8 @@
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0 and rank (n - k),
      *      where k is the number of bits set in shrink_axis_mask.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     STRIDED_SLICE = @1.1::OperationType:STRIDED_SLICE,
 
@@ -2176,14 +2148,14 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Since API level 29, generic zero-sized input tensor is supported. Zero
+     * Since HAL version 1.2, generic zero-sized input tensor is supported. Zero
      * dimension is only compatible with 0 or 1. The size of the output
      * dimension is zero if either of corresponding input dimension is zero.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
-     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since API level 29)
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM} (since HAL version 1.2)
      *
      * Supported tensor rank: up to 4
      *
@@ -2197,8 +2169,8 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     SUB = @1.1::OperationType:SUB,
 
@@ -2212,7 +2184,7 @@
      * regular matrix transpose on 2-D input Tensors.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16} (since HAL version 1.2)
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -2220,14 +2192,14 @@
      *
      * Inputs:
      * * 0: An n-D tensor, specifying the tensor to be transposed.
-     *      Since API level 29, this tensor may be zero-sized.
+     *      Since HAL version 1.2, this tensor may be zero-sized.
      * * 1: An optional 1-D Tensor of {@link OperandType::TENSOR_INT32},
      *      the permutation of the dimensions of the input tensor.
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 28.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     TRANSPOSE = @1.1::OperationType:TRANSPOSE,
 
@@ -2245,8 +2217,6 @@
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 29.
      */
     ABS = 38,
 
@@ -2269,8 +2239,6 @@
      *
      * Outputs:
      * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
-     *
-     * Available since API level 29.
      */
     // There is no underscore in ARG_MAX to avoid name conflict with
     // the macro defined in libc/kernel/uapi/linux/limits.h.
@@ -2295,8 +2263,6 @@
      *
      * Outputs:
      * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
-     *
-     * Available since API level 29.
      */
     ARGMIN = 40,  // See ARGMAX for naming discussion.
 
@@ -2341,8 +2307,8 @@
      * * 0: A tensor of the same {@link OperandType} as input0, with shape
      *      [num_rois, num_classes * 4], specifying the coordinates of each
      *      output bounding box for each class, with format [x1, y1, x2, y2].
-     *
-     * Available since API level 29.
+     *      For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+     *      scale must be 0.125 and the zero point must be 0.
      */
     AXIS_ALIGNED_BBOX_TRANSFORM = 41,
 
@@ -2482,17 +2448,15 @@
      *       then clipping is disabled.
      *       If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
      *       this scalar must be of the type {@link OperandType::FLOAT32},
-     *       otherwise if all the input tensors have the type {@link
-     *       TENSOR_FLOAT16}, this scalar must be of type {@link
-     *       FLOAT16}.
+     *       otherwise if all the input tensors have the type {@link OperandType::TENSOR_FLOAT16},
+     *       this scalar must be of type {@link OperandType::FLOAT16}.
      * * 50: The clipping threshold for the output from the
      *       projection layer, such that values are bound within
      *       [-proj_clip, proj_clip]. If set to 0.0 then clipping is disabled.
      *       If all the input tensors have type {@link OperandType::TENSOR_FLOAT32},
      *       this scalar must be of the type {@link OperandType::FLOAT32},
-     *       otherwise if all the input tensors have the type {@link
-     *       TENSOR_FLOAT16}, this scalar must be of type {@link
-     *       FLOAT16}.
+     *       otherwise if all the input tensors have the type {@link OperandType::TENSOR_FLOAT16},
+     *       this scalar must be of type {@link OperandType::FLOAT16}.
      * * 51: merge_outputs
      *       An {@link OperandType::BOOL} scalar specifying if the outputs
      *       from forward and backward cells should be merged.
@@ -2539,8 +2503,6 @@
      *      A 3-D tensor of shape:
      *        If time-major: [max_time, batch_size, bw_output_size]
      *        If batch-major: [batch_size, max_time, bw_output_size]
-     *
-     * Available since API level 29.
      */
     BIDIRECTIONAL_SEQUENCE_LSTM = 42,
 
@@ -2658,8 +2620,6 @@
      *      (timeMajor). If it is set to true, then the shape is set to
      *      [maxTime, batchSize, bwNumUnits], otherwise the shape is set to
      *      [batchSize, maxTime, bwNumUnits].
-     *
-     * Available since API level 29.
      */
     BIDIRECTIONAL_SEQUENCE_RNN = 43,
 
@@ -2737,8 +2697,6 @@
      * * 3: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
      *      [num_output_rois], specifying the batch index of each box. Boxes
      *      with the same batch index are grouped together.
-     *
-     * Available since API level 29.
      */
     BOX_WITH_NMS_LIMIT = 44,
 
@@ -2762,8 +2720,6 @@
      *
      * Outputs:
      * * 0: A tensor with the same shape as input0.
-     *
-     * Available since API level 29.
      */
     CAST = 45,
 
@@ -2800,8 +2756,8 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} and same shape as input0.
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     CHANNEL_SHUFFLE = 46,
 
@@ -2856,14 +2812,14 @@
      * * 11: A scalar, score_threshold. Boxes with scores lower than the
      *       threshold are filtered before sending to the NMS algorithm. The
      *       scalar must be of {@link OperandType::FLOAT16} if input0 is of
-     *       {@link OperandType::TENSOR_FLOAT16} and of {@link
-     *       OperandType::FLOAT32} if input0 is of {@link
-     *       OperandType::TENSOR_FLOAT32}.
+     *       {@link OperandType::TENSOR_FLOAT16} and of
+     *       {@link OperandType::FLOAT32} if input0 is of
+     *       {@link OperandType::TENSOR_FLOAT32}.
      * * 12: A scalar, specifying the IoU threshold for hard NMS. The scalar
-     *       must be of {@link OperandType::FLOAT16} if input0 is of {@link
-     *       OperandType::TENSOR_FLOAT16} and of {@link
-     *       OperandType::FLOAT32} if input0 is of {@link
-     *       OperandType::TENSOR_FLOAT32}.
+     *       must be of {@link OperandType::FLOAT16} if input0 is of
+     *       {@link OperandType::TENSOR_FLOAT16} and of
+     *       {@link OperandType::FLOAT32} if input0 is of
+     *       {@link OperandType::TENSOR_FLOAT32}.
      * * 13: An {@link OperandType::BOOL} scalar, set to true to include
      *       background class in the list of label map for the output, set
      *       to false to not include the background. When the background
@@ -2882,8 +2838,6 @@
      *      output detection.
      * * 3: An 1-D {@link OperandType::TENSOR_INT32} tensor, of shape [batches],
      *      specifying the number of valid output detections for each batch.
-     *
-     * Available since API level 29.
      */
     DETECTION_POSTPROCESSING = 47,
 
@@ -2908,8 +2862,6 @@
      *
      * Outputs:
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
-     *
-     * Available since API level 29.
      */
     EQUAL = 48,
 
@@ -2927,8 +2879,6 @@
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 29.
      */
     EXP = 49,
 
@@ -2956,8 +2906,8 @@
      * Outputs:
      * * 0: An (n + 1)-D tensor with the same {@link OperandType} and data as
      *      input0.
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     EXPAND_DIMS = 50,
 
@@ -2994,8 +2944,8 @@
      *
      * Outputs:
      * * 0: An (n + k - 1)-D tensor with the same {@link OperandType} as input0.
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     GATHER = 51,
 
@@ -3074,8 +3024,6 @@
      * * 2: A 1-D {@link OperandType::TENSOR_INT32} tensor, of shape
      *      [num_output_rois], specifying the batch index of each box. Boxes
      *      with the same batch index are grouped together.
-     *
-     * Available since API level 29.
      */
     GENERATE_PROPOSALS = 52,
 
@@ -3100,8 +3048,6 @@
      *
      * Outputs:
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
-     *
-     * Available since API level 29.
      */
     GREATER = 53,
     /**
@@ -3125,8 +3071,6 @@
      *
      * Outputs:
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
-     *
-     * Available since API level 29.
      */
     GREATER_EQUAL = 54,
 
@@ -3191,7 +3135,8 @@
      *      [depth_out, filter_height, filter_width, depth_group], specifying
      *      the filter, where depth_out must be divisible by num_groups.  For
      *      tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
-     *      the channel dimension must be set to 0.
+     *      the channel dimension (channelDim at
+     *      {@link SymmPerChannelQuantParams}) must be set to 0.
      * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
      *      tensor of type {@link OperandType::TENSOR_FLOAT32} or
      *      {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
@@ -3229,7 +3174,8 @@
      *      [depth_out, filter_height, filter_width, depth_group], specifying
      *      the filter, where depth_out must be divisible by num_groups.  For
      *      tensor of type {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL}
-     *      the channel dimension must be set to 0.
+     *      the channel dimension (SymmPerChannelQuantParams::channelDim)
+     *      must be set to 0.
      * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
      *      tensor of type {@link OperandType::TENSOR_FLOAT32} or
      *      {@link OperandType::TENSOR_FLOAT16}, the bias must be of the same
@@ -3258,8 +3204,8 @@
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, out_height, out_width, depth_out].
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     GROUPED_CONV_2D = 55,
 
@@ -3300,12 +3246,14 @@
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0, with shape
      *      [num_boxes, num_keypoints], specifying score of the keypoints.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from input0 scale and zeroPoint.
      * * 1: A tensor of the same {@link OperandType} as input1, with shape
      *      [num_boxes, num_keypoints, 2], specifying the location of
      *      the keypoints, the second dimension is organized as
      *      [keypoint_x, keypoint_y].
-     *
-     * Available since API level 29.
+     *      For type of {@link OperandType::TENSOR_QUANT16_ASYMM}, the
+     *      scale must be 0.125 and the zero point must be 0.
      */
     HEATMAP_MAX_KEYPOINT = 56,
 
@@ -3339,26 +3287,24 @@
      * * 0: An n-D tensor, specifying the tensor to be normalized.
      * * 1: A scalar, specifying gamma, the scale applied to the normalized
      *      tensor. The scalar must be of {@link OperandType::FLOAT16} if
-     *      input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link
-     *      OperandType::FLOAT32} if input0 is of {@link
-     *      OperandType::TENSOR_FLOAT32}.
+     *      input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} if input0 is of
+     *      {@link OperandType::TENSOR_FLOAT32}.
      * * 2: A scalar, specifying beta, the offset applied to the normalized
      *      tensor. The scalar must be of {@link OperandType::FLOAT16} if
-     *      input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link
-     *      OperandType::FLOAT32} if input0 is of {@link
-     *      OperandType::TENSOR_FLOAT32}.
+     *      input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} if input0 is of
+     *      {@link OperandType::TENSOR_FLOAT32}.
      * * 3: A scalar, specifying epsilon, the small value added to variance to
      *      avoid dividing by zero. The scalar must be of {@link OperandType::FLOAT16} if
-     *      input0 is of {@link OperandType::TENSOR_FLOAT16} and of {@link
-     *      OperandType::FLOAT32} if input0 is of {@link
-     *      OperandType::TENSOR_FLOAT32}.
+     *      input0 is of {@link OperandType::TENSOR_FLOAT16} and of
+     *      {@link OperandType::FLOAT32} if input0 is of
+     *      {@link OperandType::TENSOR_FLOAT32}.
      * * 4: An {@link OperandType::BOOL} scalar, set to true to specify
      *      NCHW data layout for input0 and output0. Set to false for NHWC.
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} and same shape as input0.
-     *
-     * Available since API level 29.
      */
     INSTANCE_NORMALIZATION = 57,
 
@@ -3383,8 +3329,6 @@
      *
      * Outputs:
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
-     *
-     * Available since API level 29.
      */
     LESS = 58,
 
@@ -3409,8 +3353,6 @@
      *
      * Outputs:
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
-     *
-     * Available since API level 29.
      */
     LESS_EQUAL = 59,
 
@@ -3428,8 +3370,6 @@
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 29.
      */
     LOG = 60,
 
@@ -3450,8 +3390,6 @@
      *
      * Outputs:
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
-     *
-     * Available since API level 29.
      */
     LOGICAL_AND = 61,
 
@@ -3468,8 +3406,6 @@
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 29.
      */
     LOGICAL_NOT = 62,
 
@@ -3490,8 +3426,6 @@
      *
      * Outputs:
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
-     *
-     * Available since API level 29.
      */
     LOGICAL_OR = 63,
 
@@ -3523,8 +3457,6 @@
      * Outputs:
      * * 0: The output tensor of the same {@link OperandType} and shape as
      *      input0.
-     *
-     * Available since API level 29.
      */
     LOG_SOFTMAX = 64,
 
@@ -3543,11 +3475,13 @@
      * * 0: A tensor.
      * * 1: A tensor of the same {@link OperandType} and compatible dimensions
      *      with input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scales and zeroPoint can be different from input0 scale and zeroPoint.
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     MAXIMUM = 65,
 
@@ -3566,11 +3500,13 @@
      * * 0: A tensor.
      * * 1: A tensor of the same {@link OperandType} and compatible dimensions
      *      with input0.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scales and zeroPoint can be different from input0 scale and zeroPoint.
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     MINIMUM = 66,
 
@@ -3589,8 +3525,6 @@
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 29.
      */
     NEG = 67,
 
@@ -3615,8 +3549,6 @@
      *
      * Outputs:
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
-     *
-     * Available since API level 29.
      */
     NOT_EQUAL = 68,
 
@@ -3657,8 +3589,8 @@
      *      of the padding:
      *          output0.dimension[i] =
      *              padding[i, 0] + input0.dimension[i] + padding[i, 1]
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     PAD_V2 = 69,
 
@@ -3689,8 +3621,6 @@
      *
      * Outputs:
      * * 0: An output tensor.
-     *
-     * Available since API level 29.
      */
     POW = 70,
 
@@ -3728,8 +3658,8 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be diffent from the input0 scale and zeroPoint.
      */
     PRELU = 71,
 
@@ -3752,8 +3682,6 @@
      * Outputs:
      * * 0: The output tensor of same shape as input0, but with
      *      {@link OperandType::TENSOR_QUANT8_ASYMM}.
-     *
-     * Available since API level 29.
      */
     QUANTIZE = 72,
 
@@ -3879,8 +3807,6 @@
      * Outputs:
      * * 0: A 2-D {@link OperandType::TENSOR_INT32} tensor with shape
      *      [batches, samples], containing the drawn samples.
-     *
-     * Available since API level 29.
      */
     RANDOM_MULTINOMIAL = 74,
 
@@ -3906,8 +3832,6 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 29.
      */
     REDUCE_ALL = 75,
 
@@ -3933,8 +3857,6 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 29.
      */
     REDUCE_ANY = 76,
 
@@ -3962,8 +3884,8 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     REDUCE_MAX = 77,
 
@@ -3991,8 +3913,8 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     REDUCE_MIN = 78,
 
@@ -4018,8 +3940,6 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 29.
      */
     REDUCE_PROD = 79,
 
@@ -4045,8 +3965,6 @@
      *
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0.
-     *
-     * Available since API level 29.
      */
     REDUCE_SUM = 80,
 
@@ -4064,7 +3982,7 @@
      * interpolation.
      *
      * Supported tensor {@link OperandType}:
-     * * {@link OperandType::TENSOR_FLOAT16} (since API level 29)
+     * * {@link OperandType::TENSOR_FLOAT16}
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -4105,8 +4023,8 @@
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0. The output
      *      shape is [num_rois, out_height, out_width, depth].
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from the input0 scale and zeroPoint.
      */
     ROI_ALIGN = 81,
 
@@ -4156,8 +4074,8 @@
      * Outputs:
      * * 0: A tensor of the same {@link OperandType} as input0. The output
      *      shape is [num_rois, out_height, out_width, depth].
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     ROI_POOLING = 82,
 
@@ -4175,8 +4093,6 @@
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 29.
      */
     RSQRT = 83,
 
@@ -4201,9 +4117,13 @@
      *      true) or input2 (if false).
      * * 1: An input tensor of the same shape as input0.
      * * 2: An input tensor of the same shape and type as input1.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scales and zeroPoint can be different from input1 scale and zeroPoint.
      *
      * Outputs:
      * * 0: A tensor of the same type and shape as input1 and input2.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      *
      */
     SELECT = 84,
@@ -4222,8 +4142,6 @@
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 29.
      */
     SIN = 85,
 
@@ -4235,7 +4153,6 @@
      * for each dimension. The size is specified as a 1-D tensor containing
      * either size of a slice along corresponding dimension or -1. In the latter
      * case, all the remaining elements in dimension are included in the slice.
-     * Slice size in each dimension cannot be zero.
      *
      * A sum of begin offset and a size of a slice must not exceed size of a
      * corresponding dimension.
@@ -4257,8 +4174,8 @@
      *
      * Outputs:
      * * 0: An n-D tensor of the same type as the input containing the slice.
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      its scale and zeroPoint has to be same as the input0 scale and zeroPoint.
      */
     SLICE = 86,
 
@@ -4282,8 +4199,8 @@
      *
      * Outputs:
      * * 0 ~ (num_splits - 1): Resulting subtensors.
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     SPLIT = 87,
 
@@ -4301,8 +4218,6 @@
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *
-     * Available since API level 29.
      */
     SQRT = 88,
 
@@ -4330,8 +4245,8 @@
      *
      * Outputs:
      * * 0: A tiled tensor of the same {@link OperandType} and rank as `input`.
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     TILE = 89,
 
@@ -4357,10 +4272,10 @@
      * Outputs:
      * * 0: An n-D tensor of the same type as the input, containing the k
      *      largest elements along each last dimensional slice.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      * * 1: An n-D tensor of type {@link OperandType::TENSOR_INT32}
      *      containing the indices of values within the last dimension of input.
-     *
-     * Available since API level 29.
      */
     TOPK_V2 = 90,
 
@@ -4374,7 +4289,7 @@
      * The output dimensions are functions of the filter dimensions, stride, and
      * padding.
      *
-     * Supported tensor {@link OperandCode} configurations:
+     * Supported tensor {@link OperandType} configurations:
      * * 16 bit floating point:
      * * * {@link OperandType::TENSOR_FLOAT16} for input, filter, output, and bias.
      *
@@ -4406,7 +4321,7 @@
      *      [depth_out, filter_height, filter_width, depth_in], specifying the
      *      filter. For tensor of type
      *      {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
-     *      dimension (extraParams.channelQuant.channelDim) must be set to 0.
+     *      dimension (SymmPerChannelQuantParams::channelDim) must be set to 0.
      * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
      *      tensor of type {@link OperandType::TENSOR_FLOAT32} or
      *      {@link OperandType::TENSOR_FLOAT16}, the bias should be of the
@@ -4443,7 +4358,7 @@
      *      [depth_out, filter_height, filter_width, depth_in], specifying the
      *      filter. For tensor of type
      *      {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} the channel
-     *      dimension (extraParams.channelQuant.channelDim) must be set to 0.
+     *      dimension (SymmPerChannelQuantParams::channelDim) must be set to 0.
      * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
      *      tensor of type {@link OperandType::TENSOR_FLOAT32} or
      *      {@link OperandType::TENSOR_FLOAT16}, the bias should be of the
@@ -4473,8 +4388,8 @@
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, out_height, out_width, depth_out].
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     TRANSPOSE_CONV_2D = 91,
 
@@ -4584,8 +4499,6 @@
      *      A 3-D tensor of shape:
      *        If time-major: [max_time, batch_size, output_size]
      *        If batch-major: [batch_size, max_time, output_size]
-     *
-     * Available since API level 29.
      */
     UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
 
@@ -4641,8 +4554,6 @@
      *      it is set to 1, then the output has a shape [maxTime, batchSize,
      *      numUnits], otherwise the output has a shape [batchSize, maxTime,
      *      numUnits].
-     *
-     * Available since API level 29.
      */
     UNIDIRECTIONAL_SEQUENCE_RNN = 93,
 
@@ -4696,8 +4607,8 @@
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, new_height, new_width, depth].
-     *
-     * Available since API level 29.
+     *      For a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
+     *      the scale and zeroPoint must be the same as input0.
      */
     RESIZE_NEAREST_NEIGHBOR = 94,
 
diff --git a/neuralnetworks/1.2/types.t b/neuralnetworks/1.2/types.t
new file mode 100644
index 0000000..cab330d
--- /dev/null
+++ b/neuralnetworks/1.2/types.t
@@ -0,0 +1,745 @@
+%% template file for generating types.hal.
+%% see frameworks/ml/nn/tools/api/README.md.
+/*
+ * Copyright (C) 2018 The Android Open Source Project
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package android.hardware.neuralnetworks@1.2;
+
+import @1.0::DataLocation;
+import @1.0::ErrorStatus;
+import @1.0::OperandLifeTime;
+import @1.0::OperandType;
+import @1.0::PerformanceInfo;
+import @1.1::OperationType;
+
+import android.hidl.safe_union@1.0::Monostate;
+
+enum Constant : uint32_t {
+    /**
+     * The byte size of the cache token.
+     */
+    BYTE_SIZE_OF_CACHE_TOKEN = 32,
+
+    /**
+     * The maximum number of files for each type of cache in compilation caching.
+     */
+    MAX_NUMBER_OF_CACHE_FILES = 32,
+};
+
+enum OperandType : @1.0::OperandType {
+%insert Operand_1.2
+
+    /*
+     * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+     * alternative to OEM operation and data types.
+     *
+     * OEM specific scalar value.
+     * OEM                 = 10000,
+     */
+    /*
+     * DEPRECATED. Since HAL version 1.2, extensions are the preferred
+     * alternative to OEM operation and data types.
+     *
+     * A tensor of OEM specific values.
+     * TENSOR_OEM_BYTE     = 10001,
+     */
+    /* ADDING A NEW FUNDAMENTAL TYPE REQUIRES UPDATING THE VALUE OF
+     * OperandTypeRange::FUNDAMENTAL_MAX.
+     */
+    /* ADDING A NEW OEM TYPE REQUIRES UPDATING THE VALUE OF
+     * OperandTypeRange::OEM_MAX.
+     */
+};
+
+/**
+ * The range of operand values in the OperandType enum.
+ */
+enum OperandTypeRange : uint32_t {
+    BASE_MIN        = 0,
+    FUNDAMENTAL_MIN = 0,
+%insert Operand_1.2_MAX
+    OEM_MIN         = 10000,
+    OEM_MAX         = 10001,
+    BASE_MAX        = 0xFFFF,
+};
+
+/**
+ * Operation types.
+ *
+ * The type of an operation in a model.
+ */
+enum OperationType : int32_t {
+
+%insert Operation_1.0
+
+%insert Operation_1.1
+
+%insert Operation_1.2
+
+    /**
+     * DEPRECATED. Since NNAPI 1.2, extensions are the preferred alternative to
+     * OEM operation and data types.
+     *
+     * This operation is OEM specific. It should only be used for OEM
+     * applications.
+     */
+    OEM_OPERATION = @1.1::OperationType:OEM_OPERATION,
+    /* ADDING A NEW FUNDAMENTAL OPERATION REQUIRES UPDATING THE VALUE OF
+     * OperationTypeRange::FUNDAMENTAL_MAX.
+     */
+    /* ADDING A NEW OEM OPERATION REQUIRES UPDATING THE VALUE OF
+     * OperationTypeRange::OEM_MAX.
+     */
+};
+
+/**
+ * The range of values in the OperationType enum.
+ */
+enum OperationTypeRange : uint32_t {
+    BASE_MIN        = 0,
+    FUNDAMENTAL_MIN = 0,
+%insert Operation_1.2_MAX
+    OEM_MIN         = 10000,
+    OEM_MAX         = 10000,
+    BASE_MAX        = 0xFFFF,
+};
+
+/**
+ * Device types.
+ *
+ * The type of NNAPI device.
+ */
+enum DeviceType : int32_t {
+    // Leaving 0 unused as it means unknown type in NDK NNAPI. There is no
+    // HAL equivalent of unknown type and a 1.2 HAL implementation must belong
+    // to one of the categories below.
+    /** The device does not fall into any category below. */
+    OTHER             = 1,
+    /** The device runs NNAPI models on single or multi-core CPU. */
+    CPU               = 2,
+    /** The device can run NNAPI models and also accelerate graphics APIs such
+      * as OpenGL ES and Vulkan. */
+    GPU               = 3,
+    /** Dedicated accelerator for Machine Learning workloads. */
+    ACCELERATOR       = 4,
+};
+
+/**
+ * The capabilities of a driver.
+ *
+ * Performance of an operation comes from the type of its first operand.
+ * This represents performance for non extension operand types.
+ */
+struct Capabilities {
+    /**
+     * Driver performance when operating on float32 data but performing
+     * calculations with range and/or precision as low as that of the IEEE
+     * 754 16-bit floating-point format.
+     */
+    PerformanceInfo relaxedFloat32toFloat16PerformanceScalar;
+    PerformanceInfo relaxedFloat32toFloat16PerformanceTensor;
+
+    /**
+     * Driver performance when operating on a particular data type.
+     * In the case of float32 data, this is used when the calculations
+     * are not relaxed.
+     */
+    struct OperandPerformance {
+        OperandType type;
+        PerformanceInfo info;
+    };
+
+    /**
+     * Performance by operand type. Must be sorted by OperandType.
+     * If a particular OperandType is not present in operandPerformance,
+     * its performance is treated as { .execTime = FLT_MAX, .powerUsage = FLT_MAX }.
+     */
+    vec<OperandPerformance> operandPerformance;
+};
+
+/**
+ * Describes one operation of the model's graph.
+ */
+struct Operation {
+    /**
+     * The operation type.
+     *
+     * Besides the values listed in {@link OperationType}, any value above
+     * {@link OperationTypeRange::BASE_MAX} is possible and should be interpreted
+     * as an extension type according to {@link Model::extensionNameToPrefix}.
+     */
+    OperationType type;
+
+    /**
+     * Describes the table that contains the indexes of the inputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
+    vec<uint32_t> inputs;
+
+    /**
+     * Describes the table that contains the indexes of the outputs of the
+     * operation. The offset is the index in the operandIndexes table.
+     */
+    vec<uint32_t> outputs;
+};
+
+/**
+ * Parameters for TENSOR_QUANT8_SYMM_PER_CHANNEL operand.
+ */
+struct SymmPerChannelQuantParams {
+    /** Array of scaling values for each channel. Each value must be greater than zero. */
+    vec<float> scales;
+    /** Index of the channel dimension */
+    uint32_t channelDim;
+};
+
+/**
+ * Describes one operand of the model's graph.
+ */
+struct Operand {
+    /**
+     * The data type.
+     *
+     * Besides the values listed in {@link OperandType}, any value above
+     * {@link OperandTypeRange::BASE_MAX} is possible and should be interpreted
+     * as an extension type according to {@link Model::extensionNameToPrefix}.
+     */
+    OperandType type;
+
+    /**
+     * Dimensions of the operand.
+     *
+     * For a scalar operand, dimensions.size() must be 0.
+     *
+     * A tensor operand with all dimensions specified has "fully
+     * specified" dimensions. Whenever possible (i.e., whenever the
+     * dimensions are known at model construction time), a tensor
+     * operand should have (but is not required to have) fully
+     * specified dimensions, in order to enable the best possible
+     * performance.
+     *
+     * If a tensor operand's dimensions are not fully specified, the
+     * dimensions of the operand are deduced from the operand
+     * dimensions and values of the operation for which that operand
+     * is an output.
+     *
+     * In the following situations, a tensor operand's dimensions must
+     * be fully specified:
+     *
+     *     . The operand has lifetime CONSTANT_COPY or
+     *       CONSTANT_REFERENCE.
+     *
+     *     . The operand has lifetime MODEL_INPUT. Fully
+     *       specified dimensions must either be present in the
+     *       Operand or they must be provided in the corresponding
+     *       RequestArgument.
+     *       EXCEPTION: If the input is optional and omitted
+     *       (by setting the hasNoValue field of the corresponding
+     *       RequestArgument to true) then it need not have fully
+     *       specified dimensions.
+     *
+     * A tensor operand with some number of unspecified dimensions is
+     * represented by setting each unspecified dimension to 0.
+     *
+     * A tensor operand with unspecified rank is represented by providing
+     * an empty dimensions vector.
+     */
+    vec<uint32_t> dimensions;
+
+    /**
+     * The number of times this operand appears as an operation input.
+     *
+     * (For example, if this operand appears once in one operation's
+     * input list, and three times in another operation's input list,
+     * then numberOfConsumers = 4.)
+     */
+    uint32_t numberOfConsumers;
+
+    /**
+     * Quantized scale of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM or
+     * TENSOR_INT32.
+     */
+    float scale;
+
+    /**
+     * Quantized zero-point offset of the operand.
+     *
+     * Only applicable if the operand is of type TENSOR_QUANT8_ASYMM.
+     */
+    int32_t zeroPoint;
+
+    /**
+     * How the operand is used.
+     */
+    OperandLifeTime lifetime;
+
+    /**
+     * Where to find the data for this operand.
+     * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
+     * NO_VALUE:
+     * - 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.
+     * - location.length is set.
+     * If the lifetime is CONSTANT_REFERENCE:
+     * - location.poolIndex is set.
+     * - location.offset is the offset in bytes into the specified pool.
+     * - location.length is set.
+     */
+    DataLocation location;
+
+    /**
+     * Additional parameters specific to a particular operand type.
+     */
+    safe_union ExtraParams {
+       /**
+        * No additional parameters.
+        */
+       Monostate none;
+
+       /**
+        * Symmetric per-channel quantization parameters.
+        *
+        * Only applicable to operands of type TENSOR_QUANT8_SYMM_PER_CHANNEL.
+        */
+       SymmPerChannelQuantParams channelQuant;
+
+       /**
+        * Extension operand parameters.
+        *
+        * The framework treats this as an opaque data blob.
+        * The format is up to individual extensions.
+        */
+       vec<uint8_t> extension;
+    } extraParams;
+};
+
+/**
+ * A Neural Network Model.
+ *
+ * This includes not only the execution graph, but also constant data such as
+ * weights or scalars added at construction time. The only information that
+ * may not be known is the shape of the input tensors.
+ */
+struct Model {
+    /**
+     * All operands included in the model.
+     */
+    vec<Operand> operands;
+
+    /**
+     * All operations included in the model.
+     *
+     * The operations are sorted into execution order. Every operand
+     * with lifetime MODEL_OUTPUT or TEMPORARY_VARIABLE must be
+     * written before it is read.
+     */
+    vec<Operation> operations;
+
+    /**
+     * Input indexes of the model. There must be at least one.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
+    vec<uint32_t> inputIndexes;
+
+    /**
+     * Output indexes of the model. There must be at least one.
+     *
+     * Each value corresponds to the index of the operand in "operands".
+     */
+    vec<uint32_t> outputIndexes;
+
+    /**
+     * A byte buffer containing operand data that were copied into the model.
+     *
+     * An operand's value must be located here if and only if Operand::lifetime
+     * equals OperandLifeTime::CONSTANT_COPY.
+     */
+    vec<uint8_t> operandValues;
+
+    /**
+     * A collection of shared memory pools containing operand values.
+     *
+     * An operand's value must be located here if and only if Operand::lifetime
+     * equals OperandLifeTime::CONSTANT_REFERENCE.
+     */
+    vec<memory> pools;
+
+    /**
+     * 'true' indicates TENSOR_FLOAT32 may be calculated with range and/or
+     * precision as low as that of the IEEE 754 16-bit floating-point format.
+     * 'false' indicates TENSOR_FLOAT32 must be calculated using at least the
+     * range and precision of the IEEE 754 32-bit floating-point format.
+     */
+    bool relaxComputationFloat32toFloat16;
+
+    /**
+     * The mapping between extension names and prefixes of operand and
+     * operation type values.
+     *
+     * An operand or operation whose numeric type value is above
+     * {@link OperandTypeRange::BASE_MAX} or
+     * {@link OperationTypeRange::BASE_MAX} respectively should be interpreted
+     * as an extension operand. The low
+     * {@link Model::ExtensionTypeEncoding::LOW_BITS_TYPE} bits of the value
+     * correspond to the type ID within the extension and the high
+     * {@link Model::ExtensionTypeEncoding::HIGH_BITS_PREFIX} bits encode
+     * the "prefix", which maps uniquely to the extension name.
+     *
+     * For example, if a model contains an operation whose value is
+     * 0xAAAABBBB and extensionNameToPrefix contains an entry with
+     * prefix=0xAAAA and name="vendor.test.test_extension", then
+     * the operation should be interpreted as the operation 0xBBBB
+     * of the extension named vendor.test.test_extension.
+     *
+     * This is a one-to-one correspondence. That is, there must be at most one
+     * prefix corresponding to each extension name and at most one extension
+     * name corresponding to each prefix.
+     */
+    vec<ExtensionNameAndPrefix> extensionNameToPrefix;
+
+    /**
+     * A correspondence between an extension name and a prefix of operand and
+     * operation type values.
+     */
+    struct ExtensionNameAndPrefix {
+        /**
+         * The extension name.
+         *
+         * See {@link Extension::name} for the format specification.
+         */
+        string name;
+
+        /**
+         * The unique extension identifier within the model.
+         *
+         * See {@link Model::extensionNameToPrefix}.
+         */
+        uint16_t prefix;
+    };
+
+    /**
+     * Numeric values of extension operand and operation types have the
+     * following structure:
+     * - 16 high bits represent the "prefix", which corresponds uniquely to the
+     *   extension name.
+     * - 16 low bits represent the type ID within the extension.
+     */
+    enum ExtensionTypeEncoding : uint8_t {
+        HIGH_BITS_PREFIX = 16,
+        LOW_BITS_TYPE = 16,
+    };
+};
+
+/**
+ * Describes the shape information of an output operand after execution.
+ */
+struct OutputShape {
+    /**
+     * Dimensions of the operand.
+     */
+    vec<uint32_t> dimensions;
+
+    /**
+     * Whether the provided buffer size is sufficient for the output.
+     */
+    bool isSufficient;
+};
+
+/**
+ * Specifies whether or not to measure timing information during execution.
+ */
+enum MeasureTiming : int32_t {
+    NO  = 0,
+    YES = 1,
+};
+
+/**
+
+ * Timing information measured during execution. Each time is a duration from
+ * the beginning of some task to the end of that task, including time when that
+ * task is not active (for example, preempted by some other task, or
+ * waiting for some resource to become available).
+ *
+ * Times are measured in microseconds.
+ * When a time is not available, it must be reported as UINT64_MAX.
+ */
+struct Timing {
+    /** Execution time on device (not driver, which runs on host processor). */
+    uint64_t timeOnDevice;
+    /** Execution time in driver (including time on device). */
+    uint64_t timeInDriver;
+};
+
+/**
+ * FmqRequestDatum is a single element of a serialized representation of an
+ * execution request (a {@link @1.0::Request} object and a {@link MeasureTiming}
+ * value) which is sent across FastMessageQueue.
+ *
+ * The serialized representation for a particular execution is referred to later
+ * in these descriptions as a 'packet'.
+ *
+ * FastMessageQueue can only pass HIDL-defined types that do not involve nested
+ * buffers, handles, or interfaces.
+ *
+ * The request is serialized as follows:
+ * 1) 'packetInformation'
+ * 2) For each input operand:
+ *    2.1) 'inputOperandInformation'
+ *    2.2) For each dimension element of the operand:
+ *         2.2.1) 'inputOperandDimensionValue'
+ * 3) For each output operand:
+ *    3.1) 'outputOperandInformation'
+ *    3.2) For each dimension element of the operand:
+ *         3.2.1) 'outputOperandDimensionValue'
+ * 4) For each pool:
+ *    4.1) 'poolIdentifier'
+ * 5) 'measureTiming'
+ */
+safe_union FmqRequestDatum {
+    /**
+     * Type to describe the high-level layout of the packet.
+     */
+    struct PacketInformation {
+        /**
+         * How many elements the packet contains, including the
+         * "packetInformation" datum.
+         */
+        uint32_t packetSize;
+
+        /**
+         * Number of input operands.
+         */
+        uint32_t numberOfInputOperands;
+
+        /**
+         * Number of output operands.
+         */
+        uint32_t numberOfOutputOperands;
+
+        /**
+         * Number of pool identifiers.
+         */
+        uint32_t numberOfPools;
+    };
+
+    /**
+     * Type representing the information for each operand.
+     */
+    struct OperandInformation {
+        /**
+         * If true, the argument does not have a value. This can be used for
+         * operations that take optional arguments. If true, the fields of
+         * 'location' are set to 0, 'numberOfDimensions' is set to 0,  and the
+         * dimensions information is omitted from the serialization.
+         */
+        bool hasNoValue;
+
+        /**
+         * The location within one of the memory pools passed in the Request.
+         */
+        DataLocation location;
+
+        /**
+         * Number of subsequent elements that belong to the dimensions vector.
+         */
+        uint32_t numberOfDimensions;
+    };
+
+    /**
+     * packetInformation is the first element of the packet and describes the
+     * remainder of the packet.
+     */
+    PacketInformation packetInformation;
+
+    /**
+     * Information for each input operand.
+     */
+    OperandInformation inputOperandInformation;
+
+    /**
+     * Element of the dimensions vector.
+     */
+    uint32_t inputOperandDimensionValue;
+
+    /**
+     * Information for each output operand.
+     */
+    OperandInformation outputOperandInformation;
+
+    /**
+     * Element of the dimensions vector.
+     */
+    uint32_t outputOperandDimensionValue;
+
+    /**
+     * Unique identifier for a pool.
+     *
+     * A {@link @1.0::Request} passes across one or more pools of shared memory
+     * for the inputs and outputs of an execution. However, these memory pools
+     * are not able to be sent across FastMessageQueue directly. Instead, the
+     * producing side of the FMQ represents each different pool with a unique
+     * identifier, and sends this identifier across the FMQ. Whenever the
+     * consuming side of the FMQ needs the memory corresponding to this unique
+     * identifier, it can pass the identifier to
+     * {@link IBurstCallback::getMemories} to retreive the memory. Although this
+     * HIDL Binder call is expensive compared to communication across FMQ, it is
+     * only needed in the cases when the consumer does not recognize the unique
+     * identifier.
+     */
+    int32_t poolIdentifier;
+
+    /**
+     * Specifies whether or not to measure duration of the execution. The
+     * duration runs from the time the driver dequeues the request from a
+     * FastMessageQueue to the time the driver enqueues results to a
+     * FastMessageQueue.
+     */
+    MeasureTiming measureTiming;
+};
+
+/**
+ * FmqResultDatum is a single element of a serialized representation of the
+ * values returned from an execution ({@link @1.0::ErrorStatus},
+ * vec<{@link OutputShape}>, and {@link Timing}) which is returned via
+ * FastMessageQueue.
+ *
+ * The serialized representation for a particular execution is referred to later
+ * in these descriptions as a 'packet'.
+ *
+ * FastMessageQueue can only pass HIDL-defined types that do not involve nested
+ * buffers, handles, or interfaces.
+ *
+ * The execution return values ({@link @1.0::ErrorStatus} and
+ * vec<{@link OutputShape}>) are serialized as follows:
+ * 1) 'packetInformation'
+ * 2) For each returned operand:
+ *    2.1) 'operandInformation'
+ *    2.2) For each dimension element of the operand:
+ *         2.2.1) 'operandDimensionValue'
+ * 3) 'executionTiming'
+ */
+safe_union FmqResultDatum {
+    /**
+     * Type to describe the high-level layout of the packet.
+     */
+    struct PacketInformation {
+        /**
+         * How many elements the packet contains, including the
+         * "packetInformation" datum.
+         */
+        uint32_t packetSize;
+
+        /**
+         * Status of the execution.
+         */
+        ErrorStatus errorStatus;
+
+        /**
+         * Number of returned operands.
+         */
+        uint32_t numberOfOperands;
+    };
+
+    /**
+     * Type representing the information for each operand.
+     */
+    struct OperandInformation {
+        /**
+         * Indicates whether the operand's output buffer is large enough to
+         * store the operand's result data.
+         */
+        bool isSufficient;
+
+        /**
+         * Number of subsequent elements that belong to the dimensions vector.
+         */
+        uint32_t numberOfDimensions;
+    };
+
+    /**
+     * packetInformation is the first element of the packet and describes the
+     * remainder of the packet. It additionally includes the status of the
+     * execution.
+     */
+    PacketInformation packetInformation;
+
+    /**
+     * Information for each returned operand.
+     */
+    OperandInformation operandInformation;
+
+    /**
+     * Element of the dimensions vector.
+     */
+    uint32_t operandDimensionValue;
+
+    /**
+     * Duration of execution. Unless measurement was requested and execution
+     * succeeds, all times must be reported as UINT64_MAX. A driver may choose
+     * to report any time as UINT64_MAX, indicating that measurement is not
+     * available.
+     */
+    Timing executionTiming;
+};
+
+/**
+ * Information about an extension.
+ */
+struct Extension {
+    /**
+     * The extension name.
+     *
+     * The name must consist of lowercase latin letters, numbers, periods, and
+     * underscore signs. The name must contain at least one period.
+     *
+     * The name must start with the reverse domain name of the vendor.
+     *
+     * Example: com.google.test_extension
+     */
+    string name;
+
+    /**
+     * Information about an extension operand type.
+     */
+    struct OperandTypeInformation {
+        /**
+         * The extension operand type.
+         */
+        uint16_t type;
+
+        /**
+         * Indicates whether the extension operand type represents a tensor or
+         * a scalar.
+         */
+        bool isTensor;
+
+        /**
+         * The byte size of the operand (if scalar) or of a single element (if
+         * tensor).
+         */
+        uint32_t byteSize;
+    };
+
+    /**
+     * Information about operand types defined by the extension.
+     */
+    vec<OperandTypeInformation> operandTypes;
+};