diff --git a/current.txt b/current.txt
index 88b067d..cc93f31 100644
--- a/current.txt
+++ b/current.txt
@@ -838,5 +838,7 @@
 
 # ABI preserving changes to HALs during Android T
 62ace52d9c3ff1f60f94118557a2aaf0b953513e59dcd34d5f94ae28d4c7e780 android.hardware.fastboot@1.0::IFastboot
+ca62a2a95d173ed323309e5e00f653ad3cceec82a6e5e4976a249cb5aafe2515 android.hardware.neuralnetworks@1.2::types
+fa76bced6b1b71c40fc706c508a9011284c57f57831cd0cf5f45653ed4ea463e android.hardware.neuralnetworks@1.3::types
 
 # There should be no more HIDL HALs - please use AIDL instead.
diff --git a/neuralnetworks/1.2/types.hal b/neuralnetworks/1.2/types.hal
index f5b6ead..ccb0f30 100644
--- a/neuralnetworks/1.2/types.hal
+++ b/neuralnetworks/1.2/types.hal
@@ -80,7 +80,7 @@
      * - 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.
+     * {@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:
diff --git a/neuralnetworks/1.3/types.hal b/neuralnetworks/1.3/types.hal
index a26b858..9c3bbf7 100644
--- a/neuralnetworks/1.3/types.hal
+++ b/neuralnetworks/1.3/types.hal
@@ -41,7 +41,6 @@
      * real_value = (integer_value - zeroPoint) * scale.
      */
     TENSOR_QUANT8_ASYMM_SIGNED = 14,
-
     /**
      * A reference to a subgraph.
      *
@@ -5230,7 +5229,7 @@
      * The output is calculated using the following formula:
      *
      *     h-swish(x) = x * max(0, min(6, (x + 3))) / 6
-
+     *
      * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT16}
      * * {@link OperandType::TENSOR_FLOAT32}
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl
index 12edc0f..dfda1d2 100644
--- a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperandType.aidl
@@ -24,35 +24,30 @@
  * Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors
  * with at least one dimension). Types not prefaced by TENSOR_* represent
  * scalar values and must have no dimensions.
+ *
+ * Although 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}.
  */
 @VintfStability
 @Backing(type="int")
 enum OperandType {
-    /**
-     * A 32 bit floating point scalar value.
-     */
+    /** A 32 bit floating point scalar value. */
     FLOAT32 = 0,
-    /**
-     * A signed 32 bit integer scalar value.
-     */
+    /** A signed 32 bit integer scalar value. */
     INT32 = 1,
-    /**
-     * An unsigned 32 bit integer scalar value.
-     */
+    /** An unsigned 32 bit integer scalar value. */
     UINT32 = 2,
-    /**
-     * A tensor of 32 bit floating point values.
-     */
+    /** A tensor of 32 bit floating point values. */
     TENSOR_FLOAT32 = 3,
-    /**
-     * A tensor of 32 bit integer values.
-     */
+    /** A tensor of 32 bit integer values. */
     TENSOR_INT32 = 4,
     /**
      * 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:
+     * Attached to this tensor are two numbers that can be used to convert the
+     * 8 bit integer to the real value and vice versa. These two numbers are:
      * - scale: a 32 bit floating point value greater than zero.
      * - zeroPoint: a 32 bit integer, in range [0, 255].
      *
@@ -63,15 +58,15 @@
     /**
      * An 8 bit boolean scalar value.
      *
-     * Values of this operand type are either true or false. A zero value represents false; any
-     * other value represents true.
+     * Values of this operand type are either true or false. A zero value
+     * represents false; any other value represents true.
      */
     BOOL = 6,
     /**
      * A tensor of 16 bit signed integers that represent real numbers.
      *
-     * Attached to this tensor is a number representing real value scale that is used to convert the
-     * 16 bit number to a real value in the following way:
+     * Attached to this tensor is a number representing real value scale that is
+     * used to convert the 16 bit number to a real value in the following way:
      * realValue = integerValue * scale.
      *
      * scale is a 32 bit floating point with value greater than zero.
@@ -84,8 +79,8 @@
     /**
      * A tensor of 8 bit boolean values.
      *
-     * Values of this operand type are either true or false. A zero value represents false; any
-     * other value represents true.
+     * Values of this operand type are either true or false. A zero value
+     * represents false; any other value represents true.
      */
     TENSOR_BOOL8 = 9,
     /**
@@ -95,8 +90,9 @@
     /**
      * A tensor of 8 bit signed integers that represent real numbers.
      *
-     * This tensor is associated with additional fields that can be used to convert the 8 bit signed
-     * integer to the real value and vice versa. These fields are:
+     * This tensor is associated with additional fields that can
+     * be used to convert the 8 bit signed integer to the real value and vice versa.
+     * These fields are:
      * - channelDim: a 32 bit unsigned integer indicating channel dimension.
      * - scales: an array of positive 32 bit floating point values.
      * The size of the scales array must be equal to dimensions[channelDim].
@@ -113,8 +109,8 @@
     /**
      * A tensor of 16 bit unsigned integers that represent real numbers.
      *
-     * Attached to this tensor are two numbers that can be used to convert the 16 bit integer to the
-     * real value and vice versa. These two numbers are:
+     * Attached to this tensor are two numbers that can be used to convert the
+     * 16 bit integer to the real value and vice versa. These two numbers are:
      * - scale: a 32 bit floating point value greater than zero.
      * - zeroPoint: a 32 bit integer, in range [0, 65535].
      *
@@ -125,8 +121,8 @@
     /**
      * A tensor of 8 bit signed integers that represent real numbers.
      *
-     * Attached to this tensor is a number representing real value scale that is used to convert the
-     * 8 bit number to a real value in the following way:
+     * Attached to this tensor is a number representing real value scale that is
+     * used to convert the 8 bit number to a real value in the following way:
      * realValue = integerValue * scale.
      *
      * scale is a 32 bit floating point with value greater than zero.
@@ -135,8 +131,8 @@
     /**
      * A tensor of 8 bit signed 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:
+     * Attached to this tensor are two numbers that can be used to convert the
+     * 8 bit integer to the real value and vice versa. These two numbers are:
      * - scale: a 32 bit floating point value greater than zero.
      * - zeroPoint: a 32 bit integer, in range [-128, 127].
      *
diff --git a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl
index e7fb90d..8585bda 100644
--- a/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl
+++ b/neuralnetworks/aidl/android/hardware/neuralnetworks/OperationType.aidl
@@ -78,6 +78,7 @@
      *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     ADD = 0,
+
     /**
      * Performs a 2-D average pooling operation.
      *
@@ -162,6 +163,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     AVERAGE_POOL_2D = 1,
+
     /**
      * Concatenates the input tensors along the given dimension.
      *
@@ -195,11 +197,13 @@
      *      tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
      *      Since HAL version 1.2, for a {@link OperandType::TENSOR_QUANT8_ASYMM} tensor,
      *      the scale and zeroPoint values can be different from
-     *      input tensors. Before HAL version 1.2 they have to be the same as for the input tensors.
+     *      input tensors. Before HAL version 1.2 they have to be the same as for the
+     *      input tensors.
      *      For a {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} tensor,
      *      the scale and zeroPoint values can be different from input tensors.
      */
     CONCATENATION = 2,
+
     /**
      * Performs a 2-D convolution operation.
      *
@@ -243,7 +247,8 @@
      * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
      * * * input.scale * filter.scale).
      *
-     * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+     * * Quantized signed with filter symmetric per channel quantization
+     *   (since HAL version 1.3):
      * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
      * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
      * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
@@ -356,10 +361,12 @@
      * Outputs:
      * * 0: The output 4-D tensor, of shape
      *      [batches, out_height, out_width, depth_out].
-     *      Before HAL version 1.2, for output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
-     *      the following condition must be satisfied: output_scale > input_scale * filter_scale
+     *      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 = 3,
+
     /**
      * Performs a depthwise 2-D convolution operation.
      *
@@ -407,7 +414,8 @@
      * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
      * * * input.scale * filter.scale).
      *
-     * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+     * * Quantized signed with filter symmetric per channel quantization
+     *   (since HAL version 1.3):
      * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
      * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
      * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
@@ -521,6 +529,7 @@
      *      output_scale > input_scale * filter_scale
      */
     DEPTHWISE_CONV_2D = 4,
+
     /**
      * Rearranges data from depth into blocks of spatial data.
      *
@@ -566,6 +575,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     DEPTH_TO_SPACE = 5,
+
     /**
      * Dequantizes the input tensor.
      *
@@ -593,6 +603,7 @@
      * * 0: A tensor with the same shape as input0.
      */
     DEQUANTIZE = 6,
+
     /**
      * Looks up sub-tensors in the input tensor.
      *
@@ -637,6 +648,7 @@
      *      the scale and zeroPoint must be the same as input1.
      */
     EMBEDDING_LOOKUP = 7,
+
     /**
      * Computes element-wise floor() on the input tensor.
      *
@@ -654,6 +666,7 @@
      *      the input tensor.
      */
     FLOOR = 8,
+
     /**
      * Denotes a fully (densely) connected layer, which connects all elements
      * in the input tensor with each element in the output tensor.
@@ -699,6 +712,7 @@
      *      condition must be satisfied: output_scale > input_scale * filter_scale.
      */
     FULLY_CONNECTED = 9,
+
     /**
      * Looks up sub-tensors in the input tensor using a key-value map.
      *
@@ -755,6 +769,7 @@
      *      A non-zero byte represents True, a hit. A zero indicates otherwise.
      */
     HASHTABLE_LOOKUP = 10,
+
     /**
      * Applies L2 normalization along the axis dimension.
      *
@@ -795,6 +810,7 @@
      *      are all zeros, the result is logical zero.
      */
     L2_NORMALIZATION = 11,
+
     /**
      * Performs an 2-D L2 pooling operation.
      *
@@ -873,6 +889,7 @@
      *      [batches, out_height, out_width, depth].
      */
     L2_POOL_2D = 12,
+
     /**
      * Applies Local Response Normalization along the depth dimension.
      *
@@ -927,6 +944,7 @@
      * * 0: The output tensor of same shape as input0.
      */
     LOCAL_RESPONSE_NORMALIZATION = 13,
+
     /**
      * Computes sigmoid activation on the input tensor element-wise.
      *
@@ -954,6 +972,7 @@
      *      the scale must be 1.f / 256 and the zeroPoint must be -128.
      */
     LOGISTIC = 14,
+
     /**
      * Projects an input to a bit vector via locality senstive hashing.
      *
@@ -967,8 +986,8 @@
      *
      * Inputs:
      * * 0: Hash functions. Dim.size == 2, DataType: Float.
-     *      Tensor[0].Dim[0]: 15 of hash functions.
-     *      Tensor[0].Dim[1]: 16 of projected output bits generated by each
+     *      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
@@ -1009,6 +1028,7 @@
      * The offset value for sparse projections was added in HAL version 1.2.
      */
     LSH_PROJECTION = 15,
+
     /**
      * Performs a single time step in a Long Short-Term Memory (LSTM) layer
      *
@@ -1226,6 +1246,7 @@
      *      the same as the current “output state (out)” value.
      */
     LSTM = 16,
+
     /**
      * Performs an 2-D max pooling operation.
      *
@@ -1310,6 +1331,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     MAX_POOL_2D = 17,
+
     /**
      * Multiplies two tensors, element-wise.
      *
@@ -1356,6 +1378,7 @@
      *      output_scale > input1_scale * input2_scale.
      */
     MUL = 18,
+
     /**
      * Computes rectified linear activation on the input tensor element-wise.
      *
@@ -1382,6 +1405,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     RELU = 19,
+
     /**
      * Computes rectified linear 1 activation on the input tensor element-wise.
      *
@@ -1408,6 +1432,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     RELU1 = 20,
+
     /**
      * Computes rectified linear 6 activation on the input tensor element-wise.
      *
@@ -1434,6 +1459,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     RELU6 = 21,
+
     /**
      * Reshapes a tensor.
      *
@@ -1466,6 +1492,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     RESHAPE = 22,
+
     /**
      * Resizes images to given size using the bilinear interpretation.
      *
@@ -1547,6 +1574,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     RESIZE_BILINEAR = 23,
+
     /**
      * A basic recurrent neural network layer.
      *
@@ -1598,6 +1626,7 @@
      *      the same as the current state value.
      */
     RNN = 24,
+
     /**
      * Computes the softmax activation on the input tensor element-wise, per
      * batch, by normalizing the input vector so the maximum coefficient is
@@ -1645,6 +1674,7 @@
      *      the scale must be 1.f / 256 and the zeroPoint must be -128.
      */
     SOFTMAX = 25,
+
     /**
      * Rearranges blocks of spatial data, into depth.
      *
@@ -1689,6 +1719,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     SPACE_TO_DEPTH = 26,
+
     /**
      * SVDF op is a kind of stateful layer derived from the notion that a
      * densely connected layer that's processing a sequence of input frames can
@@ -1765,6 +1796,7 @@
      *      [batch_size, num_units].
      */
     SVDF = 27,
+
     /**
      * Computes hyperbolic tangent of input tensor element-wise.
      *
@@ -1792,6 +1824,7 @@
      *      the scale must be 1.f / 128 and the zeroPoint must be 0.
      */
     TANH = 28,
+
     /**
      * BatchToSpace for N-dimensional tensors.
      *
@@ -1830,6 +1863,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     BATCH_TO_SPACE_ND = 29,
+
     /**
      * Element-wise division of two tensors.
      *
@@ -1880,6 +1914,7 @@
      * * 0: A tensor of the same {@link OperandType} as input0.
      */
     DIV = 30,
+
     /**
      * Computes the mean of elements across dimensions of a tensor.
      *
@@ -1919,6 +1954,7 @@
      *      shape is [1].
      */
     MEAN = 31,
+
     /**
      * Pads a tensor.
      *
@@ -1960,6 +1996,7 @@
      *      Since HAL version 1.2, the pad value is always the logical zero.
      */
     PAD = 32,
+
     /**
      * SpaceToBatch for N-Dimensional tensors.
      *
@@ -2012,6 +2049,7 @@
      *      Since HAL version 1.2, the pad value is always the logical zero.
      */
     SPACE_TO_BATCH_ND = 33,
+
     /**
      * Removes dimensions of size 1 from the shape of a tensor.
      *
@@ -2047,6 +2085,7 @@
      *      output shape is [1].
      */
     SQUEEZE = 34,
+
     /**
      * Extracts a strided slice of a tensor.
      *
@@ -2097,6 +2136,7 @@
      *      shape is [1].
      */
     STRIDED_SLICE = 35,
+
     /**
      * Element-wise subtraction of two tensors.
      *
@@ -2147,6 +2187,7 @@
      *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     SUB = 36,
+
     /**
      * Transposes the input tensor, permuting the dimensions according to the
      * perm tensor.
@@ -2177,6 +2218,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     TRANSPOSE = 37,
+
     /**
      * Computes the absolute value of a tensor, element-wise.
      *
@@ -2194,6 +2236,7 @@
      * * 0: The output tensor of same shape as input0.
      */
     ABS = 38,
+
     /**
      * Returns the index of the largest element along an axis.
      *
@@ -2216,7 +2259,10 @@
      * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
      *      If input is 1-dimensional, the output shape is [1].
      */
+    // There is no underscore in ARG_MAX to avoid name conflict with
+    // the macro defined in libc/kernel/uapi/linux/limits.h.
     ARGMAX = 39,
+
     /**
      * Returns the index of the smallest element along an axis.
      *
@@ -2239,7 +2285,8 @@
      * * 0: An (n - 1)-D {@link OperandType::TENSOR_INT32} tensor.
      *      If input is 1-dimensional, the output shape is [1].
      */
-    ARGMIN = 40,
+    ARGMIN = 40,  // See ARGMAX for naming discussion.
+
     /**
      * Transform axis-aligned bounding box proposals using bounding box deltas.
      *
@@ -2286,6 +2333,7 @@
      *      scale must be 0.125 and the zero point must be 0.
      */
     AXIS_ALIGNED_BBOX_TRANSFORM = 41,
+
     /**
      * A recurrent neural network layer that applies an LSTM cell to a
      * sequence of inputs in forward and backward directions.
@@ -2560,6 +2608,7 @@
      *      Available since HAL version 1.3.
      */
     BIDIRECTIONAL_SEQUENCE_LSTM = 42,
+
     /**
      * A recurrent neural network layer that applies a basic RNN cell to a
      * sequence of inputs in forward and backward directions.
@@ -2711,6 +2760,7 @@
      *      Available since HAL version 1.3.
      */
     BIDIRECTIONAL_SEQUENCE_RNN = 43,
+
     /**
      * Greedily selects a subset of bounding boxes in descending order of score.
      *
@@ -2794,6 +2844,7 @@
      *      with the same batch index are grouped together.
      */
     BOX_WITH_NMS_LIMIT = 44,
+
     /**
      * Casts a tensor to a type.
      *
@@ -2824,6 +2875,7 @@
      * * 0: A tensor with the same shape as input0.
      */
     CAST = 45,
+
     /**
      * Shuffle the channels of the input tensor.
      *
@@ -2863,6 +2915,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     CHANNEL_SHUFFLE = 46,
+
     /**
      * Apply postprocessing steps to bounding box detections.
      *
@@ -2942,6 +2995,7 @@
      *      specifying the number of valid output detections for each batch.
      */
     DETECTION_POSTPROCESSING = 47,
+
     /**
      * For input tensors x and y, computes x == y elementwise.
      *
@@ -2966,6 +3020,7 @@
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
      */
     EQUAL = 48,
+
     /**
      * Computes exponential of x element-wise.
      *
@@ -2982,6 +3037,7 @@
      * * 0: The output tensor of same shape as input0.
      */
     EXP = 49,
+
     /**
      * Inserts a dimension of 1 into a tensor's shape.
      *
@@ -3012,6 +3068,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     EXPAND_DIMS = 50,
+
     /**
      * Gathers values along an axis.
      *
@@ -3051,6 +3108,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     GATHER = 51,
+
     /**
      * Generate aixs-aligned bounding box proposals.
      *
@@ -3132,6 +3190,7 @@
      *      with the same batch index are grouped together.
      */
     GENERATE_PROPOSALS = 52,
+
     /**
      * For input tensors x and y, computes x > y elementwise.
      *
@@ -3180,6 +3239,7 @@
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
      */
     GREATER_EQUAL = 54,
+
     /**
      * Performs a grouped 2-D convolution operation.
      *
@@ -3232,7 +3292,8 @@
      * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
      * * * each value scaling is separate and equal to input.scale * filter.scales[channel]).
      *
-     * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+     * * Quantized signed with filter symmetric per channel quantization
+     *   (since HAL version 1.3):
      * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
      * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
      * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
@@ -3329,6 +3390,7 @@
      *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     GROUPED_CONV_2D = 55,
+
     /**
      * Localize the maximum keypoints from heatmaps.
      *
@@ -3382,6 +3444,7 @@
      *      scale must be 0.125 and the zero point must be 0.
      */
     HEATMAP_MAX_KEYPOINT = 56,
+
     /**
      * Applies instance normalization to the input tensor.
      *
@@ -3432,6 +3495,7 @@
      * * 0: A tensor of the same {@link OperandType} and same shape as input0.
      */
     INSTANCE_NORMALIZATION = 57,
+
     /**
      * For input tensors x and y, computes x < y elementwise.
      *
@@ -3456,6 +3520,7 @@
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
      */
     LESS = 58,
+
     /**
      * For input tensors x and y, computes x <= y elementwise.
      *
@@ -3480,6 +3545,7 @@
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
      */
     LESS_EQUAL = 59,
+
     /**
      * Computes natural logarithm of x element-wise.
      *
@@ -3496,6 +3562,7 @@
      * * 0: The output tensor of same shape as input0.
      */
     LOG = 60,
+
     /**
      * Returns the truth value of x AND y element-wise.
      *
@@ -3515,6 +3582,7 @@
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
      */
     LOGICAL_AND = 61,
+
     /**
      * Computes the truth value of NOT x element-wise.
      *
@@ -3530,6 +3598,7 @@
      * * 0: The output tensor of same shape as input0.
      */
     LOGICAL_NOT = 62,
+
     /**
      * Returns the truth value of x OR y element-wise.
      *
@@ -3549,6 +3618,7 @@
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
      */
     LOGICAL_OR = 63,
+
     /**
      * Computes the log softmax activations given logits.
      *
@@ -3579,6 +3649,7 @@
      *      input0.
      */
     LOG_SOFTMAX = 64,
+
     /**
      * Returns the element-wise maximum of two tensors.
      *
@@ -3605,6 +3676,7 @@
      *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     MAXIMUM = 65,
+
     /**
      * Returns the element-wise minimum of two tensors.
      *
@@ -3631,6 +3703,7 @@
      *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     MINIMUM = 66,
+
     /**
      * Computes numerical negative value element-wise.
      *
@@ -3648,6 +3721,7 @@
      * * 0: The output tensor of same shape as input0.
      */
     NEG = 67,
+
     /**
      * For input tensors x and y, computes x != y elementwise.
      *
@@ -3672,6 +3746,7 @@
      * * 0: A tensor of {@link OperandType::TENSOR_BOOL8}.
      */
     NOT_EQUAL = 68,
+
     /**
      * Pads a tensor with the given constant value according to the specified
      * paddings.
@@ -3716,6 +3791,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     PAD_V2 = 69,
+
     /**
      * Computes the power of one value to another.
      *
@@ -3745,6 +3821,7 @@
      * * 0: An output tensor.
      */
     POW = 70,
+
     /**
      * Parametric Rectified Linear Unit.
      *
@@ -3785,6 +3862,7 @@
      *      the scales and zeroPoint can be different from input0 scale and zeroPoint.
      */
     PRELU = 71,
+
     /**
      * Quantizes the input tensor.
      *
@@ -3816,6 +3894,7 @@
      *      {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED}.
      */
     QUANTIZE = 72,
+
     /**
      * A version of quantized LSTM, using 16 bit quantization for internal
      * state.
@@ -3920,6 +3999,7 @@
      *      (scale = 1/128, zeroPoint = 128).
      */
     QUANTIZED_16BIT_LSTM = 73,
+
     /**
      * Draws samples from a multinomial distribution.
      *
@@ -3940,6 +4020,7 @@
      *      [batches, samples], containing the drawn samples.
      */
     RANDOM_MULTINOMIAL = 74,
+
     /**
      * Reduces a tensor by computing the "logical and" of elements along given
      * dimensions.
@@ -3966,6 +4047,7 @@
      *      shape is [1].
      */
     REDUCE_ALL = 75,
+
     /**
      * Reduces a tensor by computing the "logical or" of elements along given
      * dimensions.
@@ -3992,6 +4074,7 @@
      *      shape is [1].
      */
     REDUCE_ANY = 76,
+
     /**
      * Reduces a tensor by computing the maximum of elements along given
      * dimensions.
@@ -4024,6 +4107,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     REDUCE_MAX = 77,
+
     /**
      * Reduces a tensor by computing the minimum of elements along given
      * dimensions.
@@ -4056,6 +4140,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     REDUCE_MIN = 78,
+
     /**
      * Reduces a tensor by multiplying elements along given dimensions.
      *
@@ -4082,6 +4167,7 @@
      *      shape is [1].
      */
     REDUCE_PROD = 79,
+
     /**
      * Reduces a tensor by summing elements along given dimensions.
      *
@@ -4108,6 +4194,7 @@
      *      shape is [1].
      */
     REDUCE_SUM = 80,
+
     /**
      * Select and scale the feature map of each region of interest to a unified
      * output size by average pooling sampling points from bilinear interpolation.
@@ -4169,6 +4256,7 @@
      *      the scale and zeroPoint can be different from the input0 scale and zeroPoint.
      */
     ROI_ALIGN = 81,
+
     /**
      * Select and scale the feature map of each region of interest to a unified
      * output size by max-pooling.
@@ -4222,6 +4310,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     ROI_POOLING = 82,
+
     /**
      * Computes reciprocal of square root of x element-wise.
      *
@@ -4238,6 +4327,7 @@
      * * 0: The output tensor of same shape as input0.
      */
     RSQRT = 83,
+
     /**
      * Using a tensor of booleans c and input tensors x and y select values
      * elementwise from both input tensors:
@@ -4270,6 +4360,7 @@
      *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     SELECT = 84,
+
     /**
      * Computes sin of x element-wise.
      *
@@ -4286,6 +4377,7 @@
      * * 0: The output tensor of same shape as input0.
      */
     SIN = 85,
+
     /**
      * Extracts a slice of specified size from the input tensor starting at a
      * specified location.
@@ -4321,6 +4413,7 @@
      *      its scale and zeroPoint has to be same as the input0 scale and zeroPoint.
      */
     SLICE = 86,
+
     /**
      * Splits a tensor along a given axis into num_splits subtensors.
      *
@@ -4347,6 +4440,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     SPLIT = 87,
+
     /**
      * Computes square root of x element-wise.
      *
@@ -4363,6 +4457,7 @@
      * * 0: The output tensor of same shape as input0.
      */
     SQRT = 88,
+
     /**
      * Constructs a tensor by tiling a given tensor.
      *
@@ -4393,6 +4488,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     TILE = 89,
+
     /**
      * Finds values and indices of the k largest entries for the last dimension.
      *
@@ -4423,6 +4519,7 @@
      *      containing the indices of values within the last dimension of input.
      */
     TOPK_V2 = 90,
+
     /**
      * Performs the transpose of 2-D convolution operation.
      *
@@ -4457,7 +4554,8 @@
      * * * {@link OperandType::TENSOR_INT32} for bias (with scale set to
      * * * input.scale * filter.scale).
      *
-     * * Quantized signed with filter symmetric per channel quantization (since HAL version 1.3):
+     * * Quantized signed with filter symmetric per channel quantization
+     *   (since HAL version 1.3):
      * * * {@link OperandType::TENSOR_QUANT8_ASYMM_SIGNED} for input, and output.
      * * * {@link OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL} for filter.
      * * * {@link OperandType::TENSOR_INT32} for bias (scale set to 0.0,
@@ -4551,6 +4649,7 @@
      *      the scale and zeroPoint can be different from inputs' scale and zeroPoint.
      */
     TRANSPOSE_CONV_2D = 91,
+
     /**
      * A recurrent neural network specified by an LSTM cell.
      *
@@ -4668,6 +4767,7 @@
      *      Available since HAL version 1.3.
      */
     UNIDIRECTIONAL_SEQUENCE_LSTM = 92,
+
     /**
      * A recurrent neural network layer that applies a basic RNN cell to a
      * sequence of inputs.
@@ -4726,6 +4826,7 @@
      *      Available since HAL version 1.3.
      */
     UNIDIRECTIONAL_SEQUENCE_RNN = 93,
+
     /**
      * Resizes images to given size using the nearest neighbor interpretation.
      *
@@ -4804,6 +4905,7 @@
      *      the scale and zeroPoint must be the same as input0.
      */
     RESIZE_NEAREST_NEIGHBOR = 94,
+
     /**
      * Quantized version of {@link OperationType::LSTM}.
      *
@@ -4932,6 +5034,7 @@
      *      Shape: [batchSize, outputSize]
      */
     QUANTIZED_LSTM = 95,
+
     /**
      * Executes one of the two referenced subgraphs as determined by a boolean
      * value.
@@ -4958,6 +5061,7 @@
      * * 0 ~ (m - 1): Outputs produced by the selected subgraph.
      */
     IF = 96,
+
     /**
      * Executes the body subgraph until the condition subgraph outputs false.
      *
@@ -5024,6 +5128,7 @@
      * * 0 ~ (m - 1): Outputs produced by the loop.
      */
     WHILE = 97,
+
     /**
      * Computes exponential linear activation on the input tensor element-wise.
      *
@@ -5049,6 +5154,7 @@
      * * 0: The output tensor of same shape and type as input0.
      */
     ELU = 98,
+
     /**
      * Computes hard-swish activation on the input tensor element-wise.
      *
@@ -5076,6 +5182,7 @@
      *      tensor's parameters.
      */
     HARD_SWISH = 99,
+
     /**
      * Creates a tensor filled with a scalar value.
      *
@@ -5100,6 +5207,7 @@
      * * 0: The output tensor.
      */
     FILL = 100,
+
     /**
      * Returns the rank of a tensor.
      *