Update neuralnetworks/*/types.hal to match impl

Updates hardware/interfaces/neuralnetworks/1.(0|1)/types.hal to match
the NeuralNetworks.h header in framework/ml/nn. Only comments have
changed.

Updated using framework/ml/nn/tools/sync_enums_to_hal.py.

Change-Id: I0754868ad8acf6e2e0c5b83661d04682febec9b0
Bug: 77604249
Test: checked changes with git diff
Test: mm in $ANDROID_BUILD_TOP

current.txt

@@ -260,7 +260,7 @@ fb92e2b40f8e9d494e8fd3b4ac18499a3216342e7cff160714c3bbf3660b6e79 android.hardwar
 4e7169919d24fbe5573e5bcd683d0bd7abf553a4e6c34c41f9dfc1e12050db07 android.hardware.gnss@1.0::IGnssNavigationMessageCallback
 5804ca86611d72e5481f022b3a0c1b334217f2e4988dad25730c42af2d1f4d1c android.hardware.neuralnetworks@1.0::IDevice
 12e8dca4ab7d8aadd0ef8f1b438021938e2396139e85db2ed65783b08800aa52 android.hardware.neuralnetworks@1.0::IExecutionCallback
-702f9a4cd3b7486a4b04f7155b737757ac2ca4b3548976d5782ad3cae9ff9780 android.hardware.neuralnetworks@1.0::types
+934b9a0627080bca5dee83126d23ace31bdf1ed36fe192a2a7694f81b4f0c2af android.hardware.neuralnetworks@1.0::types
 d4840db8efabdf1e4b344fc981cd36e5fe81a39aff6e199f6d06c1c8da413efd android.hardware.radio@1.0::types
 b280c4704dfcc548a9bf127b59b7c3578f460c50cce70a06b66fe0df8b27cff0 android.hardware.wifi@1.0::types
 
@@ -339,7 +339,7 @@ b8c7ed58aa8740361e63d0ce9e7c94227572a629f356958840b34809d2393a7c android.hardwar
 4a2c0dc82780e6c90731725a103feab8ab6ecf85a64e049b9cbd2b2c61620fe1 android.hardware.media.bufferpool@1.0::IConnection
 6aef1218e5949f867b0104752ac536c1b707222a403341720de90141df129e3e android.hardware.media.bufferpool@1.0::types
 7698dc2382a2eeb43541840e3ee624f34108efdfb976b2bfa7c13ef15fb8c4c4 android.hardware.neuralnetworks@1.1::IDevice
-5604001029a255648a9e955de0a822a48d9ba7cc259b106fb8be0cd43dc8eece android.hardware.neuralnetworks@1.1::types
+ce5dab4b2dd828bcff09acfb93fcd4846f847868b9e914d214095532c28dc0cf android.hardware.neuralnetworks@1.1::types
 8d3d86da0bfa4bf070970d8303c659f67f35d670c287d45a3f542e4fedadd578 android.hardware.nfc@1.1::INfc
 e85f566698d2a2c28100e264fcf2c691a066756ddf8dd341d009ff50cfe10614 android.hardware.nfc@1.1::INfcClientCallback
 5e278fcaa3287d397d8eebe1c22aaa28150f5caae1cf9381cd6dc32cb37899c5 android.hardware.nfc@1.1::types

neuralnetworks/1.0/types.hal

@@ -444,10 +444,11 @@ enum OperationType : int32_t {
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
-     *      a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
-     *      [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
-     *      and “input_size” is the size of the input.
+     * * 0: A tensor of at least rank 2, specifying the input. If rank is greater than 2,
+     *      then it gets flattened to a 2-D Tensor. The (flattened) 2-D Tensor is reshaped
+     *      (if necessary) to [batch_size, input_size], where "input_size" corresponds to
+     *      the number of inputs to the layer, matching the second dimension of weights, and
+     *      "batch_size" is calculated by dividing the number of elements by "input_size".
      * * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where
      *      "num_units" corresponds to the number of output nodes.
      * * 2: A 1-D tensor, of shape [num_units], specifying the bias.
@@ -728,9 +729,11 @@ enum OperationType : int32_t {
      *   \f{eqnarray*}{
      *   i_t = 1 - f_t
      *   \f}
-     * * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights (\f$W_{cf}\f$), and cell-to-output
-     *   weights (\f$W_{co}\f$) either all have values or none of them have values.
-     *   If they have values, the peephole optimization is used.
+     * * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output
+     *   weights (\f$W_{co}\f$) either both have values or neither of them have values.
+     *   If they have values, the peephole optimization is used. Additionally,
+     *   if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also
+     *   required to have values for peephole optimization.
      * * The projection weights (\f$W_{proj}\f$) is required only for the recurrent projection
      *   layer, and should otherwise have no value.
      * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a value if the
@@ -1008,7 +1011,8 @@ enum OperationType : int32_t {
      * Resizes images to given size using the bilinear interpretation.
      *
      * Resized images must be distorted if their output aspect ratio is not the same as
-     * input aspect ratio.
+     * input aspect ratio. The corner pixels of output may not be the same as
+     * corner pixels of input.
      *
      * Supported tensor types:
      * * {@link OperandType::TENSOR_FLOAT32}

neuralnetworks/1.1/types.hal

@@ -214,6 +214,13 @@ enum OperationType : @1.0::OperationType {
      *    tensor to be sliced. The length must be of rank(input0).
      * 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input
      *    tensor to be sliced. The length must be of rank(input0).
+     * 4: An INT32 value, begin_mask. If the ith bit of begin_mask is set, begin[i] is ignored
+     *    and the fullest possible range in that dimension is used instead.
+     * 5: An INT32 value, end_mask. If the ith bit of end_mask is set, end[i] is ignored and
+     *    the fullest possible range in that dimension is used instead.
+     * 6: An INT32 value, shrink_axis_mask. An int32 mask. If the ith bit of shrink_axis_mask is
+     *    set, it implies that the ith specification shrinks the dimensionality by 1. A slice of
+     *    size 1 starting from begin[i] in the dimension must be preserved.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.