diff --git a/current.txt b/current.txt
index af44905..fe76327 100644
--- a/current.txt
+++ b/current.txt
@@ -238,15 +238,16 @@
 619600109232ed64b827c8a11beed8070b1827ae464547d7aa146cf0473b4bca android.hardware.cas.native@1.0::IDescrambler
 0a159f81359cd4f71bbe00972ee8403ea79351fb7c0cd48be72ebb3e424dbaef android.hardware.radio@1.0::types
 09342041e17c429fce0034b9096d17849122111436a5f0053e7e59500e1cb89c android.hardware.media.omx@1.0::IOmxStore
-246a56d37d57a47224562c9d077b4a2886ce6242b9311bd98a17325944c280d7 android.hardware.neuralnetworks@1.0::types
 93eb3757ceaf21590fa4cd1d4a7dfe3b3794af5396100a6d25630879352abce9 android.hardware.neuralnetworks@1.0::IDevice
 f66f9a38541bf92001d3adcce678cd7e3da2262124befb460b1c9aea9492813b android.hardware.neuralnetworks@1.0::IExecutionCallback
 953607822954435874f4b81686440a604e2a88cdd2d9164c6293f3d5772510d7 android.hardware.neuralnetworks@1.0::IPreparedModel
 73e03573494ba96f0e711ab7f1956c5b2d54c3da690cd7ecf4d6d0f287447730 android.hardware.neuralnetworks@1.0::IPreparedModelCallback
+246a56d37d57a47224562c9d077b4a2886ce6242b9311bd98a17325944c280d7 android.hardware.neuralnetworks@1.0::types
 f4945e397b5dea41bb64518dfde59be71245d8a125fd1e0acffeb57ac7b08fed android.hardware.thermal@1.1::IThermal
 c8bc853546dd55584611def2a9fa1d99f657e3366c976d2f60fe6b8aa6d2cb87 android.hardware.thermal@1.1::IThermalCallback
 
 # Future changes to HALs
 5804ca86611d72e5481f022b3a0c1b334217f2e4988dad25730c42af2d1f4d1c android.hardware.neuralnetworks@1.0::IDevice
-1488db5ffb8a7979488d1084761aab8bca2f59bc9a02d75cdefc296afeaf591b android.hardware.neuralnetworks@1.0::types
+12e8dca4ab7d8aadd0ef8f1b438021938e2396139e85db2ed65783b08800aa52 android.hardware.neuralnetworks@1.0::IExecutionCallback
+702f9a4cd3b7486a4b04f7155b737757ac2ca4b3548976d5782ad3cae9ff9780 android.hardware.neuralnetworks@1.0::types
 
diff --git a/neuralnetworks/1.0/IExecutionCallback.hal b/neuralnetworks/1.0/IExecutionCallback.hal
index ef0f454..9c06166 100644
--- a/neuralnetworks/1.0/IExecutionCallback.hal
+++ b/neuralnetworks/1.0/IExecutionCallback.hal
@@ -28,7 +28,7 @@
      * ErrorStatus resulting from the execution. If the asynchronous task
      * is not launched, notify must be invoked with the appropriate error.
      *
-     * @return param Error status returned from launching the asynchronous task
+     * @param status Error status returned from launching the asynchronous task
      *               (if the launch fails) or from the asynchronous task itself
      *               (if the launch succeeds). Must be:
      *               - NONE if the asynchronous execution was successful
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 5b8f22c..8c07fcc 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -24,38 +24,40 @@
  * Types prefaced with TENSOR_* must be used for tensor data (i.e., tensors
  * with at least one dimension). Types not prefaced by TENSOR_* represent
  * scalar values and must have no dimensions.
+ *
+ * Although many types are defined, most operators accept just a few
+ * types. Most used are {@link OperandType::TENSOR_FLOAT32},
+ * {@link OperandType::TENSOR_QUANT8_ASYMM},
+ * and {@link OperandType::INT32}.
  */
 enum OperandType : int32_t {
-    /**
-     * The following entries are used to declare scalars.
-     */
+    /** A 32 bit floating point scalar value. */
     FLOAT32             = 0,
+    /** A signed 32 bit integer scalar value. */
     INT32               = 1,
+    /** An unsigned 32 bit integer scalar value. */
     UINT32              = 2,
 
-    /**
-     * The following entries are used to declare tensors.
-     */
+    /** A tensor of 32 bit floating point values. */
     TENSOR_FLOAT32      = 3,
+    /** A tensor of 32 bit integer values. */
     TENSOR_INT32        = 4,
-
-    /**
-     * A tensor of 8 bit integers that represent real numbers.
+    /** A tensor of 8 bit integers that represent real numbers.
      *
      * Attached to this tensor are two numbers that can be used to convert the
      * 8 bit integer to the real value and vice versa. These two numbers are:
-     * - scale: a 32 bit floating point value greater than zero
-     * - zero_value: a 32 bit integer
+     * - scale: a 32 bit floating point value greater than zero.
+     * - zeroPoint: a 32 bit integer, in range [0, 255].
      *
      * The formula is:
-     * real_value = (integer_value - zero_value) * scale.
+     * real_value = (integer_value - zeroPoint) * scale.
      */
     TENSOR_QUANT8_ASYMM = 5,
 
-    /**
-     * The following entries are OEM specific operand types.
-     */
+    /** OEM specific scalar value. */
     OEM                 = 10000,
+
+    /** A tensor of OEM specific values. */
     TENSOR_OEM_BYTE     = 10001,
 };
 
@@ -66,9 +68,9 @@
  */
 enum OperationType : int32_t {
     /**
-     * Adds two tensors, elment-wise.
+     * Adds two tensors, element-wise.
      *
-     * Takes two input tensors of identical type and compatible dimensions.  The output
+     * Takes two input tensors of identical type and compatible dimensions. The output
      * is the sum of both input tensors, optionally modified by an activation function.
      *
      * Two dimensions are compatible when:
@@ -79,22 +81,25 @@
      * It starts with the trailing dimensions, and works its way forward.
      *
      * Example:
-     *     input1.dimension =    {4, 1, 2}
+     *
+     *     input1.dimension = {4, 1, 2}
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: A tensor.
-     * 1: A tensor of the same type, and compatible dimensions as input0.
-     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * * 0: A tensor.
+     * * 1: A tensor of the same type, and compatible dimensions as input0.
+     * * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
      *
-     * Ouputs:
-     * 0: The sum, a tensor of the same type as input0.
+     * Outputs:
+     * * 0: The sum, a tensor of the same type as input0.
      */
     ADD = 0,
 
@@ -103,29 +108,50 @@
      *
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[batch, row, col, channel] =
      *         sum_{i, j}(input[batch, row + i, col + j, channel]) / sum(1)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
-     * Supported tensor rank: 4, with "NHWC" data layout.
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
-     * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
-     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
-     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
-     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
-     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
-     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
-     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
-     * 7: An INT32 value, specifying the filter width.
-     * 8: An INT32 value, specifying the filter height.
-     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels)
+     * data layout.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * Both explicit padding and implicit padding are supported.
+     *
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * * 5: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 6: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 7: An INT32 value, specifying the filter width.
+     * * 8: An INT32 value, specifying the filter height.
+     * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 2: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the filter width.
+     * * 5: An INT32 value, specifying the filter height.
+     * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
      */
     AVERAGE_POOL_2D = 1,
 
@@ -135,19 +161,21 @@
      * The input tensors must have identical type and the same dimensions except the
      * dimension along the concatenation axis.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0 ~ n: The list on n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm]
-     * n+1: An INT32 value, specifying the concatenation axis.
-     * n+2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * * 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm].
+     *            For inputs of {@link OperandType::TENSOR_QUANT8_ASYMM} type, all
+     *            input tensors must have the same scale and zeroPoint.
+     * * n: An INT32 value, specifying the concatenation axis.
      *
-     * Ouputs:
-     * 0: The output, a tensor of the same type as the input tensors.
-          The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+     * Outputs:
+     * * 0: The output, a tensor of the same type as the input tensors.
+     *      The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
      */
     CONCATENATION = 2,
 
@@ -159,7 +187,8 @@
      *
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[batch, row, col, channel] =
      *         sum_{i, j} (
      *             input[batch, row + i, col + j, k] *
@@ -167,77 +196,135 @@
      *             bias[channel]
      *         )
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
-     * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
-     * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
-     *    specifying the filter.
-     * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
-     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
-     *    also be of {@link OperandType::TENSOR_FLOAT32}.
-     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
-     *    should be of {@link OperandType::TENSOR_INT32}.
-     * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
-     * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
-     * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
-     * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
-     * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
-     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
-     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * Both explicit padding and implicit padding are supported.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
+     *      specifying the filter.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}.
+     *      For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * * 7: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 8: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
+     *      specifying the filter.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}.
+     *      For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 4: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 5: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+     *      condition must be satisfied: output_scale > input_scale * filter_scale.
      */
     CONV_2D = 3,
 
     /**
-     * Performs an depthwise 2-D convolution operation.
+     * Performs a depthwise 2-D convolution operation.
      *
      * Given an input tensor of shape [batches, height, width, depth_in] and a filter
-     * tensor of shape [depth_out, filter_height, filter_width, depth_in] containing
-     * in_channels convolutional filters of depth 1, DEPTHWISE_CONV applies a different
+     * tensor of shape [1, filter_height, filter_width, depth_out] containing
+     * depth_out convolutional filters of depth 1, DEPTHWISE_CONV applies a different
      * filter to each input channel (expanding from 1 channel to channel_multiplier channels
      * for each), then concatenates the results together.
      *
      * The output has depth_out = depth_in * depth_multiplier channels.
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[b, i, j, k * channel_multiplier + q] =
      *         sum_{di, dj} (
      *             input[b, strides[1] * i + di, strides[2] * j + dj, k] *
-     *             filter[di, dj, k, q]
+     *             filter[1, di, dj, k * channel_multiplier + q]
      *         )
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
-     * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
-     * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
-     *    specifying the filter.
-     * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
-     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
-     *    also be of {@link OperandType::TENSOR_FLOAT32}.
-     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
-     *    should be of {@link OperandType::TENSOR_INT32}.
-     * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
-     * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
-     * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
-     * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
-     * 7: An INT32 value, specifying the output stride in the ‘width’ dimension.
-     * 8: An INT32 value, specifying the output stride in the ‘height’ dimension.
-     * 9: An INT32 value, specifying the depthwise multiplier.
-     * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * Both explicit padding and implicit padding are supported.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+     *      specifying the filter.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}.
+     *      For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * * 4: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * * 5: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * * 6: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * * 7: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 8: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 9: An INT32 value, specifying the depthwise multiplier.
+     * * 10: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *       Specifies the activation to invoke on the result of each addition.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 1: A 4-D tensor, of shape [1, filter_height, filter_width, depth_out],
+     *      specifying the filter.
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}.
+     *      For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 4: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 5: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 6: An INT32 value, specifying the depthwise multiplier.
+     * * 7: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *       Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth_out].
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+     *      condition must be satisfied: output_scale > input_scale * filter_scale.
      */
     DEPTHWISE_CONV_2D = 4,
 
@@ -255,18 +342,20 @@
      * input_height * block_size.
      * The depth of the input tensor must be divisible by block_size * block_size
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
      * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
-     * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
-     *    block_size * block_size must be a divisor of the input depth.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+     *      block_size * block_size must be a divisor of the input depth.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
-     *    depth/(block_size*block_size)].
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
+     *      depth/(block_size*block_size)].
      */
     DEPTH_TO_SPACE = 5,
 
@@ -274,53 +363,69 @@
      * Dequantizes the input tensor.
      *
      * The formula is:
-     *     output = (input - zero_value) * scale.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *     output = (input - zeroPoint) * scale.
+     *
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}.
+     * * 0: A tensor of type {@link OperandType::TENSOR_QUANT8_ASYMM}.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0, but with type
-          {@link OperandType::TENSOR_FLOAT32}.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0, but with type
+     *      {@link OperandType::TENSOR_FLOAT32}.
      */
     DEQUANTIZE = 6,
 
     /**
-     * Looks up items from a given tensor.
+     * Looks up sub-tensors in the input tensor.
      *
-     * Each item in the output is a raw copy of the corresponding item in
-     * the input “values”. If the the given “lookup” indices are out of bounds,
-     * the op will fail and an error will be reported.
+     * This operator takes for input a tensor of values (Values) and
+     * a one-dimensional tensor of selection indices (Lookups).
+     * The output tensor is the concatenation of sub-tensors of Values as
+     * selected by Lookups.
+     *
+     * Think of Values as being sliced along its first dimension:
+     * The entries in Lookups select which slices are concatenated together
+     * to create the output tensor.
+     *
+     * For example, if Values has shape of [40, 200, 300] and
+     * Lookups has shape of [3], all three values found in Lookups are
+     * expected to be between 0 and 39. The resulting tensor must
+     * have shape of [3, 200, 300].
+     *
+     * If a value in Lookups is out of bounds, the operation must fail
+     * and an error must be reported.
      *
      * Inputs:
-     * * 0: Values. An n-D tensor of any type X (where n >= 2). E.g., if n is 2,
-     *      then the shape would be [lookup_dimension, values_dimension], where
-     *      “lookup_dimension” corresponds to the indexing dimension in the lookup
-     *      table, and “values_dimension” to the contents.
-     * * 1: Lookups. An 1-D tensor of type T, of shape [lookup_size], where
-     *      “lookup_size” is the number of elements to look for, and each entry
-     *      corresponds to the first dimension of the “values” tensor.
+     * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32} type.
+     *      The values are indices into the first dimension of Values.
+     * * 1: Values. An n-D tensor, where n >= 2, from which sub-tensors are
+     *      extracted.
      *
      * Output:
-     * * 0: A n-D tensor of type X and the same rank and shape as the “values”
-     *      tensor, except for the first dimension which has size “lookup_size”.
+     * * 0: A n-D tensor with the same rank and shape as the Values
+     *      tensor, except for the first dimension which has the same size
+     *      as Lookups' only dimension.
      */
     EMBEDDING_LOOKUP = 7,
 
     /**
      * Computes element-wise floor() on the input tensor.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: A tensor.
+     * * 0: A tensor.
      *
-     * Ouputs:
-     * 0: The output, a tensor of the same type and dimensions as input0.
+     * Outputs:
+     * * 0: The output tensor, of the same type and dimensions as the input tensor.
      */
     FLOOR = 8,
 
@@ -329,66 +434,104 @@
      * tensor with each element in the output tensor.
      *
      * This layer implements the operation:
+     *
      *     outputs = activation(inputs * weights’ + bias)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
-     *    a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
-     *    [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
-     *    and “input_size” is the size of the input.
-     * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where “num_units”
-     *    corresponds to the number of output nodes.
-     * 2: A 1-D tensor, of shape [num_units], specifying the bias.
-     *    For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
-     *    also be of {@link OperandType::TENSOR_FLOAT32}.
-     *    For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
-     *    should be of {@link OperandType::TENSOR_INT32}.
-     * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * * 0: A tensor, specifying the input. If rank is greater than 2, then it gets flattened to
+     *      a 2-D Tensor. The 2-D Tensor is handled as if dimensions corresponded to shape
+     *      [batch_size, input_size], where “batch_size” corresponds to the batching dimension,
+     *      and “input_size” is the size of the input.
+     * * 1: A 2-D tensor, specifying the weights, of shape [num_units, input_size], where
+     *      "num_units" corresponds to the number of output nodes.
+     * * 2: A 1-D tensor, of shape [num_units], specifying the bias.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32} type, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}.
+     *      For input tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
+     *      bias_scale == input_scale * filter_scale.
+     * * 3: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
      *
-     * Ouputs:
-     * 0: The output tensor, of shape [batch_size, num_units].
+     * Outputs:
+     * * 0: The output tensor, of shape [batch_size, num_units].
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+     *      condition must be satisfied: output_scale > input_scale * filter_scale.
      */
     FULLY_CONNECTED = 9,
 
     /**
-     * Looks up values of a hash table with given keys.
+     * Looks up sub-tensors in the input tensor using a key-value map.
+     *
+     * This operator takes for input a tensor of values (Values),
+     * a one-dimensional tensor of selection values (Lookups) and
+     * a one-dimensional tensor that maps these values to Values
+     * indexes. The output tensor is the concatenation of sub-tensors of
+     * Values as selected by Lookups via Keys.
+     *
+     * Think of Values as being sliced along its outer-most dimension.
+     * The output is a concatenation of selected slices, with one slice
+     * for each entry of Lookups. The slice selected is the one at the
+     * same index as the Maps entry that matches the value in Lookups.
+     *
+     * For a hit, the corresponding sub-tensor of Values is included
+     * in the Output tensor. For a miss, the corresponding sub-tensor in
+     * Output must have zero values.
+     *
+     * For example, if Values has shape of [40, 200, 300],
+     * Keys should have a shape of [40]. If Lookups tensor has shape
+     * of [3], three slices are being concatenated, so the resulting tensor
+     * must have the shape of [3, 200, 300]. If the first entry in Lookups
+     * has the value 123456, that value must be located in Keys tensor.
+     * If the sixth entry of Keys contains 123456, the sixth slice of Values
+     * must be selected. If no entry in Keys has 123456, a slice of zeroes
+     * must be concatenated.
      *
      * Inputs:
-     * * 0: Lookups. A 1-D int32 tensor with shape [ k ].
-     * * 1: Keys. A 1-D int32 tensor with shape [ n ], *MUST* be sorted in
-     *      ascending order.
-     * * 2: Values. A tensor with shape [ n … ].
+     * * 0: Lookups. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [ k ].
+     * * 1: Keys. A 1-D {@link OperandType::TENSOR_INT32} tensor with shape [ n ];
+     *      Keys and Values pair represent a map, i.e., the ith element
+     *      in Keys (Keys[i]) is the key to select the ith sub-tensor
+     *      in Values (Values[i]), where 0 <= i <= n-1.
+     *      Keys tensor *MUST* be sorted in ascending order.
+     * * 2: Values. A tensor with shape of [ n, … ]; i.e., the first dimension must be n.
      *
      * Outputs:
      * * 0: Output. A tensor with shape [ k …].
-     * * 1: Hits. A uint8 tensor with shape [ k ] indicates whether the lookup
-     *      hits or not.
+     * * 1: Hits. A boolean tensor with shape [ k ] indicates whether the lookup
+     *      hits (True) or not (False).
+     *      Stored as {@link OperandType::TENSOR_QUANT8_ASYMM} with offset 0 and scale 1.0f.
+     *      A non-zero byte represents True, a hit. A zero indicates otherwise.
      */
     HASHTABLE_LOOKUP = 10,
 
     /**
-     * Applies L2 normalization along a the depth dimension.
+     * Applies L2 normalization along the depth dimension.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[batch, row, col, channel] =
      *         input[batch, row, col, channel] /
      *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
      *
-     * For x with more dimensions, independently normalizes each 1-D slice along dimension dim.
+     * For input tensor with more dimensions, independently normalizes each 1-D slice along dimension dim.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     * Supported tensor rank: 4, with "NHWC" data layout.
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
+     * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels).
      *
      * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth].
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
      */
     L2_NORMALIZATION = 11,
 
@@ -397,28 +540,48 @@
      *
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[batch, row, col, channel] =
      *         sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) / sum(1))
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
-     * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
-     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
-     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
-     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
-     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
-     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
-     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
-     * 7: An INT32 value, specifying the filter width.
-     * 8: An INT32 value, specifying the filter height.
-     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * Both explicit padding and implicit padding are supported.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * * 5: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 6: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 7: An INT32 value, specifying the filter width.
+     * * 8: An INT32 value, specifying the filter height.
+     * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 2: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the filter width.
+     * * 5: An INT32 value, specifying the filter height.
+     * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
      */
     L2_POOL_2D = 12,
 
@@ -429,41 +592,49 @@
      * dimension), and each vector is normalized independently. Within a given vector,
      * each component is divided by the weighted, squared sum of inputs within depth_radius.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     sqr_sum[a, b, c, d] =
      *         sum(pow(input[a, b, c, d - depth_radius : d + depth_radius + 1], 2)
      *     output = input / pow((bias + alpha * sqr_sum), beta)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
      * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
-     * 1: An INT32 value, specifying the radius of the normalization window.
-     * 2: A FLOAT32 value, specifying the bias, must not be zero.
-     * 3: A FLOAT32 value, specifying the scale factor, alpha.
-     * 4: A FLOAT32 value, specifying the exponent, beta.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the radius of the normalization window.
+     * * 2: A FLOAT32 value, specifying the bias, must not be zero.
+     * * 3: A FLOAT32 value, specifying the scale factor, alpha.
+     * * 4: A FLOAT32 value, specifying the exponent, beta.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     LOCAL_RESPONSE_NORMALIZATION = 13,
 
     /**
      * Computes sigmoid activation on the input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = 1 / (1 + exp(-input))
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM} type,
+     *      the scale must be 1.f / 256 and the zeroPoint must be 0.
      */
     LOGISTIC = 14,
 
@@ -502,102 +673,165 @@
     LSH_PROJECTION = 15,
 
     /**
-     * Long short-term memory unit (LSTM) recurrent network layer.
+     * Performs a single time step in a Long Short-Term Memory (LSTM) layer
      *
-     * The default non-peephole implementation is based on:
-     * http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
+     * The LSTM operation is described by the following equations.
+     *
+     * \f{eqnarray*}{
+     * i_t =& \sigma(W_{xi}x_t+W_{hi}h_{t-1}+W_{ci}C_{t-1}+b_i) & \\
+     * f_t =& \sigma(W_{xf}x_t+W_{hf}h_{t-1}+W_{cf}C_{t-1}+b_f) & \\
+     * C_t =& clip(f_t \odot C_{t-1} + i_t \odot g(W_{xc}x_t+W_{hc}h_{t-1}+b_c),\ t_{cell})& \\
+     * o_t =& \sigma(W_{xo}x_t+W_{ho}h_{t-1}+W_{co}C_t+b_o)& \\
+     *      & clip(W_{proj}(o_t \odot g(C_t))+b_{proj},\ t_{proj}) & if\ there\ is\ a\ projection; \\
+     * h_t =& & \\
+     *      & o_t \odot g(C_t) & otherwise. \\
+     * \f}
+     * Where:
+     * * \f$x_t\f$ is the input,
+     * * \f$i_t\f$ is the input gate,
+     * * \f$f_t\f$ is the forget gate,
+     * * \f$C_t\f$ is the cell state,
+     * * \f$o_t\f$ is the output,
+     * * \f$h_t\f$ is the output state,
+     * * \f$\sigma\f$ is the logistic sigmoid function,
+     * * \f$g\f$ is the cell input and cell output activation function, usually \f$tahn\f$,
+     * * \f$W_{xi}\f$ is the input-to-input weight matrix,
+     * * \f$W_{hi}\f$ is the recurrent to input weight matrix,
+     * * \f$W_{ci}\f$ is the cell-to-input weight matrix,
+     * * \f$b_i\f$ is the input gate bias,
+     * * \f$W_{xf}\f$ is the input-to-forget weight matrix,
+     * * \f$W_{hf}\f$ is the recurrent-to-forget weight matrix,
+     * * \f$W_{cf}\f$ is the cell-to-forget weight matrix,
+     * * \f$b_f\f$ is the forget gate bias,
+     * * \f$W_{xc}\f$ is the input-to-cell weight matrix,
+     * * \f$W_{hc}\f$ is the recurrent-to-cell weight matrix,
+     * * \f$b_c\f$ is the cell bias,
+     * * \f$W_{xo}\f$ is the input-to-output weight matrix,
+     * * \f$W_{ho}\f$ is the recurrent-to-output weight matrix,
+     * * \f$W_{co}\f$ is the cell-to-output weight matrix,
+     * * \f$b_o\f$ is the output gate bias,
+     * * \f$W_{proj}\f$ is the projection weight matrix,
+     * * \f$b_{proj}\f$ is the projection bias,
+     * * \f$t_{cell}\f$ is the threshold for clipping the cell state, and
+     * * \f$t_{proj}\f$ is the threshold for clipping the projected output.
+     * * \f$\odot\f$ is the <a href="https://en.wikipedia.org/wiki/Hadamard_product_(matrices)">
+     *   Hadamard product</a> that takes two matrices and produces another
+     *   matrix, each element of which is the product of the corresponding
+     *   elements of the input matrices.
+     *
+     * The operation has the following independently optional inputs:
+     * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights (\f$W_{hi}\f$),
+     *   cell-to-input (\f$W_{ci}\f$) weights, and input gate bias (\f$b_i\f$) either all have values,
+     *   or none of them have values (i.e., all set to null). If they have no
+     *   values, coupling of input and forget gates (CIFG) is used, in which case
+     *   the input gate (\f$i_t\f$) is calculated using the following equation instead.
+     *   \f{eqnarray*}{
+     *   i_t = 1 - f_t
+     *   \f}
+     * * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights (\f$W_{cf}\f$), and cell-to-output
+     *   weights (\f$W_{co}\f$) either all have values or none of them have values.
+     *   If they have values, the peephole optimization is used.
+     * * The projection weights (\f$W_{proj}\f$) is required only for the recurrent projection
+     *   layer, and should otherwise have no value.
+     * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a value if the
+     *   recurrent projection layer exists, and should otherwise have no value.
+     *
+     * References:
+     *
+     * The default non-peephole non-CIFG implementation is based on:
+     * http://www.bioinf.jku.at/publications/older/2604.pdf
      * S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural
      * Computation, 9(8):1735-1780, 1997.
      *
-     * The peephole implementation is based on:
+     * The peephole implementation and projection layer is based on:
      * https://research.google.com/pubs/archive/43905.pdf
      * Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory
      * recurrent neural network architectures for large scale acoustic modeling."
      * INTERSPEECH, 2014.
+     * (However, the concept of peephole optimization was introduced in work
+     * prior to this paper.)
      *
      * The coupling of input and forget gate (CIFG) is based on:
      * http://arxiv.org/pdf/1503.04069.pdf
      * Greff et al. "LSTM: A Search Space Odyssey"
      *
-     * The class has the following independently optional inputs:
-     * * If input gate (if CIFG): “input_to_forget_weights”,
-     *   “recurrent_to_input_weights”, “cell_to_input_weights”, “input_gate_bias”.
-     * * If no peephole connections: “cell_to_input_weights”,
-     *   “cell_to_forget_weights”, “cell_to_output_weights”.
-     * * If no projection layer: “projection_weights” and “projection_bias”.
-     * * If no projection bias: “projection_bias”.
-     *
-     * Supported tensor types:
+     * Supported tensor types (type T):
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Inputs:
-     * * 0: Input.
+     * * 0: The input (\f$x_t\f$).
      *      A 2-D tensor of type T, of shape [batch_size, input_size], where
      *      “batch_size” corresponds to the batching dimension, and “input_size”
      *      is the size of the input.
-     * * 1: input_to_input_weights.
+     * * 1: The input-to-input weights (\f$W_{xi}\f$). Optional.
      *      A 2-D tensor of type T, of shape [num_units, input_size], where
      *      “num_units” corresponds to the number of cell units.
-     * * 2: input_to_forget_weights.
+     * * 2: The input-to-forget weights (\f$W_{xf}\f$).
      *      A 2-D tensor of type T, of shape [num_units, input_size].
-     * * 3: input_to_cell_weights.
+     * * 3: The input-to-cell weights (\f$W_{xc}\f$).
      *      A 2-D tensor of type T, of shape [num_units, input_size].
-     * * 4: input_to_output_weights.
+     * * 4: The input-to-output weights (\f$W_{xo}\f$).
      *      A 2-D tensor of type T, of shape [num_units, input_size].
-     * * 5: recurrent_to_input_weights.
+     * * 5: The recurrent-to-input weights (\f$W_{hi}\f$). Optional.
      *      A 2-D tensor of type T, of shape [num_units, output_size], where
      *      “output_size” corresponds to either the number of cell units (i.e.,
      *      “num_units”), or the second dimension of the “projection_weights”, if
      *      defined.
-     * * 6: recurrent_to_forget_weights.
+     * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
      *      A 2-D tensor of type T, of shape [num_units, output_size].
-     * * 7: recurrent_to_cell_weights.
+     * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
      *      A 2-D tensor of type T, of shape [num_units, output_size].
-     * * 8: recurrent_to_output_weights.
+     * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
      *      A 2-D tensor of type T, of shape [num_units, output_size].
-     * * 9: cell_to_input_weights.
+     * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 10:cell_to_forget_weights.
+     * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 11:cell_to_output_weights.
+     * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 12:input_gate_bias.
+     * * 12:The input gate bias (\f$b_i\f$). Optional.
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 13:forget_gate_bias.
+     * * 13:The forget gate bias (\f$b_f\f$).
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 14:cell_bias.
+     * * 14:The cell bias (\f$b_c\f$).
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 15:output_gate_bias.
+     * * 15:The output gate bias (\f$b_o\f$).
      *      A 1-D tensor of type T, of shape [num_units].
-     * * 16:projection_weights.
+     * * 16:The projection weights (\f$W_{proj}\f$). Optional.
      *      A 2-D tensor of type T, of shape [output_size, num_units].
-     * * 17:projection_bias.
+     * * 17:The projection bias (\f$b_{proj}\f$). Optional.
      *      A 1-D tensor of type T, of shape [output_size].
-     *
-     * Parameters:
-     * * 18:fused_activation_function.
-     *      An (optional) ActivationFunctionType indicating the activation
-     *      function.
-     *      If “NONE” is specified then it results in a linear activation.
-     * * 19:cell_clip.
-     *      A clipping threshold for the cell state, such that values are bound
+     * * 18:The output state (in) (\f$h_{t-1}\f$).
+     *      A 2-D tensor of type T, of shape [batch_size, output_size].
+     * * 19:The cell state (in) (\f$C_{t-1}\f$).
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     * * 20:The activation function (\f$g\f$).
+     *      A value indicating the activation function:
+     *      <ul>
+     *      <li>0: None;
+     *      <li>1: Relu;
+     *      <li>3: Relu6;
+     *      <li>4: Tanh;
+     *      <li>6: Sigmoid.
+     *      </ul>
+     * * 21:The clipping threshold (\f$t_{cell}\f$) for the cell state, such that values are bound
      *      within [-cell_clip, cell_clip]. If set to 0.0 then clipping is
      *      disabled.
-     * * 20:proj_clip.
-     *      A clipping threshold for the output from the projection layer, such
+     * * 22:The clipping threshold (\f$t_{proj}\f$) for the output from the projection layer, such
      *      that values are bound within [-proj_clip, proj_clip]. If set to 0.0
      *      then clipping is disabled.
      *
      * Outputs:
-     * * 0: scratch_buffer.
-     *      A 3-D tensor of type T, of shape [batch_size, num_cell, 4].
-     * * 1: output_state.
+     * * 0: The scratch buffer.
+     *      A 2-D tensor of type T, of shape [batch_size, num_units * 4] with
+     *      CIFG, or [batch_size, num_units * 3] without CIFG.
+     * * 1: The output state (out) (\f$h_t\f$).
      *      A 2-D tensor of type T, of shape [batch_size, output_size].
-     * * 2: cell_state.
+     * * 2: The cell state (out) (\f$C_t\f$).
      *      A 2-D tensor of type T, of shape [batch_size, num_units].
-     * * 3: output.
+     * * 3: The output (\f$o_t\f$).
      *      A 2-D tensor of type T, of shape [batch_size, output_size]. This is
-     *      effectively the same as the current “output_state” value.
+     *      effectively the same as the current “output state (out)” value.
      */
     LSTM = 16,
 
@@ -606,36 +840,56 @@
      *
      * The output dimensions are functions of the filter dimensions, stride, and padding.
      *
-     * The values in output Tensor is computed as:
+     * The values in the output tensor are computed as:
+     *
      *     output[batch, row, col, channel] =
      *         max_{i, j} (input[batch, row + i, col + j, channel])
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
-     * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
-     * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
-     * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
-     * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
-     * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
-     * 5: An INT32 value, specifying the output stride in the ‘width’ dimension.
-     * 6: An INT32 value, specifying the output stride in the ‘height’ dimension.
-     * 7: An INT32 value, specifying the filter width.
-     * 8: An INT32 value, specifying the filter height.
-     * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * Both explicit padding and implicit padding are supported.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * Inputs (explicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the padding on the left, in the ‘width’ dimension.
+     * * 2: An INT32 value, specifying the padding on the right,in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the padding on the top, in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the padding on the bottom, in the ‘height’ dimension.
+     * * 5: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 6: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 7: An INT32 value, specifying the filter width.
+     * * 8: An INT32 value, specifying the filter height.
+     * * 9: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Inputs (implicit padding):
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
+     * * 2: An INT32 value, specifying the stride when walking through input
+     *      in the ‘width’ dimension.
+     * * 3: An INT32 value, specifying the stride when walking through input
+     *      in the ‘height’ dimension.
+     * * 4: An INT32 value, specifying the filter width.
+     * * 5: An INT32 value, specifying the filter height.
+     * * 6: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
+     *
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
      */
     MAX_POOL_2D = 17,
 
     /**
-     * Multiplies two tensors, elment-wise.
+     * Multiplies two tensors, element-wise.
      *
-     * Takes two input tensors of identical type and compatible dimensions.  The output
+     * Takes two input tensors of identical type and compatible dimensions. The output
      * is the product of both input tensors, optionally modified by an activation function.
      *
      * Two dimensions are compatible when:
@@ -645,72 +899,85 @@
      * The size of the resulting output is the maximum size along each dimension of the
      * input operands. It starts with the trailing dimensions, and works its way forward.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: A tensor.
-     * 1: A tensor of the same type, and compatible dimensions as input0.
-     * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
-     *    Specifies the activation to invoke on the result of each addition.
+     * * 0: A tensor.
+     * * 1: A tensor of the same type, and compatible dimensions as input0.
+     * * 2: An INT32 value, and has to be one of the {@link FusedActivationFunc} values.
+     *      Specifies the activation to invoke on the result of each addition.
      *
-     * Ouputs:
-     * 0: The product, a tensor of the same type as input0.
+     * Outputs:
+     * * 0: The product, a tensor of the same type as input0.
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM} type, the following
+     *      condition must be satisfied: output_scale > input1_scale * input2_scale.
      */
     MUL = 18,
 
     /**
      * Computes rectified linear activation on the input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = max(0, input)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     RELU = 19,
 
     /**
      * Computes rectified linear 1 activation on the input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = min(1.f, max(-1.f, input))
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     RELU1 = 20,
 
     /**
      * Computes rectified linear 6 activation on the input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = min(6, max(0, input))
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     RELU6 = 21,
 
@@ -720,36 +987,41 @@
      * Given tensor, this operation returns a tensor that has the same values as tensor,
      * but with a newly specified shape.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the tensor to be reshaped.
-     * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32}, defining the shape
-     *    of the output tensor. The number of elements implied by shape must be the same
-     *    as the number of elements in the input tensor.
+     * * 0: A tensor, specifying the tensor to be reshaped.
+     * * 1: A 1-D tensor of type {@link OperandType::TENSOR_INT32}, defining the shape
+     *      of the output tensor. The number of elements implied by shape must be the same
+     *      as the number of elements in the input tensor.
      *
-     * Ouputs:
-     * 0: The output tensor, of shape specified by the input shape.
+     * Outputs:
+     * * 0: The output tensor, of shape specified by the input shape.
      */
     RESHAPE = 22,
 
     /**
      * Resizes images to given size using the bilinear interpretation.
      *
-     * Resized images will be distorted if their original aspect ratio is not the same as input.
+     * Resized images must be distorted if their output aspect ratio is not the same as
+     * input aspect ratio.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
      * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
-     * 1: An INT32 value, specifying the output width of the output tensor.
-     * 2: An INT32 value, specifying the output height of the output tensor.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying the input.
+     * * 1: An INT32 value, specifying the output height of the output tensor.
+     * * 2: An INT32 value, specifying the output width of the output tensor.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
      */
     RESIZE_BILINEAR = 23,
 
@@ -768,7 +1040,7 @@
      * * “activation” is the function passed as the “fused_activation_function”
      *   argument (if not “NONE”).
      *
-     * Supported tensor types:
+     * Supported tensor types (Type T):
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Inputs:
@@ -784,21 +1056,18 @@
      *      corresponding to the weights from each unit.
      * * 3: bias.
      *      A 1-D tensor of type T, of shape [num_units].
-     *
-     *    For FLOAT32 input tensor, bias must also be FLOAT32.
-     *    For UINT8 input tensor, bias must be INT32.
-     *
-     * Parameters
-     * * 4: fused_activation_function.
-     *      An (optional) ActivationFunctionType indicating the activation
+     * * 4: hidden state (in).
+     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     * * 5: fused_activation_function.
+     *      An optional {@link FusedActivationFunc} value indicating the activation
      *      function. If “NONE” is specified then it results in a linear
      *      activation.
      *
-     * * 5: Hidden state.
+     * Outputs:
+     * * 0: hidden state (out).
      *      A 2-D tensor of type T, of shape [batch_size, num_units].
      *
-     * Outputs:
-     * * 0: output.
+     * * 1: output.
      *      A 2-D tensor of type T, of shape [batch_size, num_units]. This is
      *      effectively the same as the current state value.
      */
@@ -808,21 +1077,26 @@
      * Computes the softmax activation on the input tensor element-wise, per batch, by
      * normalizing the input vector so the maximum coefficient is zero.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output[batch, i] =
      *         exp((input[batch, i] - max(input[batch, :])) * beta) /
      *         sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: 2 or 4.
      *
      * Inputs:
-     * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
-     * 1: A FLOAT32 value, specifying the scaling factor for the exponent, beta.
+     * * 0: A 2-D or 4-D tensor, specifying the tensor to be reshaped.
+     * * 1: A FLOAT32 value, specifying the positive scaling factor for the exponent, beta.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM} type,
+     *      the scale must be 1.f / 256 and the zeroPoint must be 0.
      */
     SOFTMAX = 25,
 
@@ -839,18 +1113,20 @@
      * The depth of the output tensor is input_depth * block_size * block_size.
      * The input tensor's height and width must be divisible by block_size.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: 4, with "NHWC" data layout.
      *
      * Inputs:
-     * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
-     * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
-     *    block_size must be a divisor of both the input height and width.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 1: An INT32 value, specifying the block_size. block_size must be >=1 and
+     *      block_size must be a divisor of both the input height and width.
      *
-     * Ouputs:
-     * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
-     *    depth*block_size*block_size].
+     * Outputs:
+     * * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
+     *      depth*block_size*block_size].
      */
     SPACE_TO_DEPTH = 26,
 
@@ -874,8 +1150,8 @@
      *
      * Specifically, for rank 1, this layer implements the operation:
      *
-     *    memory = push(conv1d(inputs, weights_feature, feature_dim, "VALID"));
-     *    outputs = activation(memory * weights_time + bias);
+     *     memory = push(conv1d(inputs, weights_feature, feature_dim, "PADDING_VALID"));
+     *     outputs = activation(memory * weights_time + bias);
      *
      * Where:
      * * “weights_feature” is a weights matrix that processes the inputs (by
@@ -892,7 +1168,7 @@
      * Each rank adds a dimension to the weights matrices by means of stacking
      * the filters.
      *
-     * Supported tensor types:
+     * Supported tensor types (type T):
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Inputs:
@@ -907,20 +1183,17 @@
      *      A 2-D tensor of type T, of shape [num_units, memory_size], where
      *      “memory_size” corresponds to the fixed-size of the memory.
      * * 3: bias.
-     *      A optional 1-D tensor of type T, of shape [num_units].
-     *
-     *    For FLOAT32 input tensor, bias must also be FLOAT32.
-     *    For UINT8 input tensor, bias must be INT32.
-     *
-     * Parameters:
-     * * 4: rank.
+     *      An optional 1-D tensor of type T, of shape [num_units].
+     * * 4: state (in).
+     *      A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+     * * 5: rank.
      *      The rank of the SVD approximation.
-     * * 5: fused_activation_function.
-     *      An (optional) ActivationFunctionType indicating the activation function.
+     * * 6: fused_activation_function.
+     *      An optional {@link FusedActivationFunc} value indicating the activation function.
      *      If “NONE” is specified then it results in a linear activation.
      *
      * Outputs:
-     * * 0: state.
+     * * 0: state (out).
      *      A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
      * * 1: output.
      *      A 2-D tensor of type T, of shape [batch_size, num_units].
@@ -930,17 +1203,20 @@
     /**
      * Computes hyperbolic tangent of input tensor element-wise.
      *
-     * In details:
+     * The output is calculated using this formula:
+     *
      *     output = tanh(input)
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * 0: A tensor, specifying the input.
+     * * 0: A tensor, specifying the input.
      *
-     * Ouputs:
-     * 0: The output tensor of same shape as input0.
+     * Outputs:
+     * * 0: The output tensor of same shape as input0.
      */
     TANH = 28,
 
@@ -967,8 +1243,8 @@
  */
 enum OperandLifeTime : int32_t {
     /**
-     * The operand is internal to the model.  It's created by an operation
-     * and consumed by other operations.
+     * The operand is internal to the model. It's created by an operation and
+     * consumed by other operations.
      */
     TEMPORARY_VARIABLE,
 
@@ -1112,7 +1388,7 @@
     /**
      * Where to find the data for this operand.
      * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or NO_VALUE:
-     * - All the fields will be 0.
+     * - All the fields must be 0.
      * If the lifetime is CONSTANT_COPY:
      * - location.poolIndex is 0.
      * - location.offset is the offset in bytes into Model.operandValues.
@@ -1220,7 +1496,7 @@
      * Updated dimension information.
      *
      * If dimensions.size() > 0, dimension information was provided along with the
-     * argument.  This can be the case for models that accept inputs of varying size.
+     * argument. This can be the case for models that accept inputs of varying size.
      * This can't change the rank, just the value of the dimensions that were
      * unspecified in the model.
      */
diff --git a/neuralnetworks/1.1/types.hal b/neuralnetworks/1.1/types.hal
index 1d470d6..b4fccae 100644
--- a/neuralnetworks/1.1/types.hal
+++ b/neuralnetworks/1.1/types.hal
@@ -27,25 +27,24 @@
  */
 enum OperationType : @1.0::OperationType {
     /**
-     * BatchToSpace for N-D tensors.
+     * BatchToSpace for N-dimensional tensors.
      *
-     * This operation reshapes the "batch" dimension 0 into M + 1 dimensions of shape
+     * This operation reshapes the batch dimension (dimension 0) into M + 1 dimensions of shape
      * block_shape + [batch], interleaves these blocks back into the grid defined by the
      * spatial dimensions [1, ..., M], to obtain a result with the same rank as the input.
-     * The spatial dimensions of this intermediate result are then optionally cropped
-     * according to the amount to crop to produce the output.
+     *
      * This is the reverse of SpaceToBatch.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
-     * Supported tensor rank: up to 4
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
+     * 0: An n-D tensor, specifying the tensor to be reshaped
      * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
      *    input tensor. All values must be >= 1.
-     * 2: A 1-D Tensor of type TENSOR_INT32, the amount to crop for each spatial diemension of the
-     *    input tensor. All values must be >= 0.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -53,9 +52,9 @@
     BATCH_TO_SPACE_ND = 29,
 
     /**
-     * Divides the second tensor from the first tensor, element-wise.
+     * Element-wise division of two tensors.
      *
-     * Takes two input tensors of identical OperandType and compatible dimensions. The output
+     * Takes two input tensors of identical type and compatible dimensions. The output
      * is the result of dividing the first input tensor by the second, optionally
      * modified by an activation function.
      *
@@ -71,7 +70,9 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
@@ -88,15 +89,17 @@
     /**
      * Computes the mean of elements across dimensions of a tensor.
      *
-     * Reduces input tensor along the dimensions given in axis. Unless keep_dims is true,
-     * the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is
-     * true, the reduced dimensions are retained with length 1.
+     * Reduces the input tensor along the given dimensions to reduce. Unless keep_dims
+     * is true, the rank of the tensor is reduced by 1 for each entry in axis.
+     * If keep_dims is true, the reduced dimensions are retained with length 1.
      *
-     * If axis has no entries, all dimensions are reduced, and a tensor with a single
-     * element is returned.
+     * If dimensions to reduce have no entries, all dimensions are reduced, and a tensor with
+     * a single element is returned.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
@@ -115,14 +118,18 @@
      *
      * This operation pads a tensor according to the specified paddings.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
-     * 1: A 2-D Tensor of type TENSOR_INT32. The paddings, before and after for each spatial dimension
-     *    of the input tensor.
+     * 0: An n-D tensor, specifying the tensor to be padded.
+     * 1: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial dimension of the
+     *    input tensor. The shape of the tensor must be {rank(input0), 2}.
+     *    padding[i, 0] specifies the number of element to be padded in the front of dimension i.
+     *    padding[i, 1] specifies the number of element to be padded after the end of dimension i.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -130,7 +137,7 @@
     PAD = 32,
 
     /**
-     * SpaceToBatch for N-D tensors.
+     * SpaceToBatch for N-Dimensional tensors.
      *
      * This operation divides "spatial" dimensions [1, ..., M] of the input into a grid of blocks
      * of shape block_shape, and interleaves these blocks with the "batch" dimension (0) such that
@@ -139,16 +146,20 @@
      * batch position. Prior to division into blocks, the spatial dimensions of the input are
      * optionally zero padded according to paddings.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
-     * Supported tensor rank: up to 4
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
+     * Supported tensor rank: 4
      *
      * Inputs:
      * 0: An n-D tensor, specifying the input.
      * 1: A 1-D Tensor of type TENSOR_INT32, the block sizes for each spatial dimension of the
      *    input tensor. All values must be >= 1.
      * 2: A 2-D Tensor of type TENSOR_INT32, the paddings for each spatial diemension of the
-     *    input tensor. All values must be >= 0.
+     *    input tensor. All values must be >= 0. The shape of the tensor must be {rank(input0), 2}.
+     *    padding[i, 0] specifies the number of element to be padded in the front of dimension i.
+     *    padding[i, 1] specifies the number of element to be padded after the end of dimension i.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -160,17 +171,20 @@
      *
      * Given a tensor input, this operation returns a tensor of the same type with all
      * dimensions of size 1 removed. If you don't want to remove all size 1 dimensions,
-     * you can remove specific size 1 dimensions by specifying axis.
+     * you can remove specific size 1 dimensions by specifying the axes (input1).
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
-     * 1: An 1-D Tensor of type TENSOR_INT32. The dimensions to squeeze. If None (the default),
-     *    squeezes all dimensions. If specified, only squeezes the dimensions listed. The dimension
-     *    index starts at 0. It is an error to squeeze a dimension that is not 1.
+     * 0: An n-D tensor, the tensor to be squeezed.
+     * 1: An optional 1-D tensor of type TENSOR_INT32. The dimensions to squeeze. If specified
+     *    only squeezes the dimensions listed. Otherwise, squeezes all dimensions.
+     *    The dimension index starts at 0. An error must be reported if squeezing a dimension that
+     *    is not 1.
      *
      * Outputs:
      * 0: A tensor of the same type as input0. Contains the same data as input, but has one or more
@@ -181,23 +195,25 @@
     /**
      * Extracts a strided slice of a tensor.
      *
-     * This op extracts a slice of size (end-begin)/stride from the given input tensor.
-    *  Starting at the location specified by begin the slice continues by adding
+     * Roughly speaking, this op extracts a slice of size (end - begin) / stride from the given
+     * input tensor. Starting at the location specified by begin the slice continues by adding
      * stride to the index until all dimensions are not less than end. Note that a stride can
      * be negative, which causes a reverse slice.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
+     * 0: An n-D tensor, specifying the tensor to be sliced.
      * 1: A 1-D Tensor of type TENSOR_INT32, the starts of the dimensions of the input
-     *    tensor to be sliced.
+     *    tensor to be sliced. The length must be of rank(input0).
      * 2: A 1-D Tensor of type TENSOR_INT32, the ends of the dimensions of the input
-     *    tensor to be sliced.
+     *    tensor to be sliced. The length must be of rank(input0).
      * 3: A 1-D Tensor of type TENSOR_INT32, the strides of the dimensions of the input
-     *    tensor to be sliced.
+     *    tensor to be sliced. The length must be of rank(input0).
      *
      * Outputs:
      * 0: A tensor of the same type as input0.
@@ -205,7 +221,7 @@
     STRIDED_SLICE = 35,
 
     /**
-     * Subtracts the second tensor from the first tensor, element-wise.
+     * Element-wise subtraction of two tensors.
      *
      * Takes two input tensors of identical type and compatible dimensions. The output
      * is the result of subtracting the second input tensor from the first one, optionally
@@ -223,7 +239,9 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
@@ -240,18 +258,20 @@
     /**
      * Transposes the input tensor, permuting the dimensions according to the perm tensor.
      *
-     * The returned tensor's dimension i must correspond to the input dimension perm[i].
+     * The returned tensor's dimension i corresponds to the input dimension perm[i].
      * If perm is not given, it is set to (n-1...0), where n is the rank of the input tensor.
      * Hence by default, this operation performs a regular matrix transpose on 2-D input Tensors.
      *
-     * Supported tensor types: {@link OperandType::TENSOR_FLOAT32}
-     *                         {@link OperandType::TENSOR_QUANT8_ASYMM}
+     * Supported tensor types:
+     * * {@link OperandType::TENSOR_FLOAT32}
+     * * {@link OperandType::TENSOR_QUANT8_ASYMM}
+     *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the input.
-     * 1: A 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the input
-     *    tensor.
+     * 0: An n-D tensor, specifying the tensor to be transposed.
+     * 1: An optional 1-D Tensor of type TENSOR_INT32, the permutation of the dimensions of the
+     *    input tensor.
      *
      * Outputs:
      * 0: A tensor of the same type as input0.