diff --git a/current.txt b/current.txt
index fe76327..14cdaa0 100644
--- a/current.txt
+++ b/current.txt
@@ -249,5 +249,5 @@
 # Future changes to HALs
 5804ca86611d72e5481f022b3a0c1b334217f2e4988dad25730c42af2d1f4d1c android.hardware.neuralnetworks@1.0::IDevice
 12e8dca4ab7d8aadd0ef8f1b438021938e2396139e85db2ed65783b08800aa52 android.hardware.neuralnetworks@1.0::IExecutionCallback
-702f9a4cd3b7486a4b04f7155b737757ac2ca4b3548976d5782ad3cae9ff9780 android.hardware.neuralnetworks@1.0::types
+18e6885e184fe48401c2c53f1d1b8bfb07240f40c81ae6b9d2e336fca6efdbb7 android.hardware.neuralnetworks@1.0::types
 
diff --git a/neuralnetworks/1.0/types.hal b/neuralnetworks/1.0/types.hal
index 802f6cb..4efa13a 100644
--- a/neuralnetworks/1.0/types.hal
+++ b/neuralnetworks/1.0/types.hal
@@ -42,7 +42,8 @@
     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:
@@ -70,15 +71,17 @@
     /**
      * Adds two tensors, element-wise.
      *
-     * 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.
+     * Takes two input tensors of identical {@link OperandType} and compatible
+     * dimensions. The output is the sum of both input tensors, optionally
+     * modified by an activation function.
      *
      * Two dimensions are compatible when:
      *     1. they are equal, or
      *     2. one of them is 1
      *
-     * The size of the output is the maximum size along each dimension of the input operands.
-     * It starts with the trailing dimensions, and works its way forward.
+     * The size of the output is the maximum size along each dimension of the
+     * input operands. It starts with the trailing dimensions, and works its
+     * way forward.
      *
      * Example:
      *
@@ -86,7 +89,7 @@
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -94,98 +97,119 @@
      *
      * 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.
+     * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+     *      as input0.
+     * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * Outputs:
-     * * 0: The sum, a tensor of the same type as input0.
+     * * 0: The sum, a tensor of the same {@link OperandType} as input0.
      */
     ADD = 0,
 
     /**
      * Performs a 2-D average pooling operation.
      *
-     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     * The output dimensions are functions of the filter dimensions, stride, and
+     * padding.
      *
      * 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:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
-     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width, and Channels)
-     * data layout.
+     * Supported tensor rank: 4, with "NHWC" (i.e., Num_samples, Height, Width,
+     * and Channels) data layout.
      *
      * Both explicit padding and implicit padding are supported.
      *
      * 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.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the left, in the ‘width’ dimension.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the right, in the ‘width’ dimension.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the top, in the ‘height’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the bottom, in the ‘height’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+     *      width.
+     * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+     *      height.
+     * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * 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
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
+     *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
-     * * 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.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+     *      width.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+     *      height.
+     * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * Outputs:
-     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * * 0: The output 4-D tensor, of shape
+            [batches, out_height, out_width, depth].
      */
     AVERAGE_POOL_2D = 1,
 
     /**
      * Concatenates the input tensors along the given dimension.
      *
-     * The input tensors must have identical type and the same dimensions except the
-     * dimension along the concatenation axis.
+     * The input tensors must have identical {@link OperandType} and the same
+     * dimensions except the dimension along the concatenation axis.
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * * 0 ~ n-1: The list of n input tensors, of shape [D0, D1, ..., Daxis(i), ..., Dm].
-     *            For inputs of {@link OperandType::TENSOR_QUANT8_ASYMM} type, all
-     *            input tensors must have the same scale and zeroPoint.
-     * * n: An INT32 value, specifying the concatenation axis.
+     * * 0 ~ n-1: The list of n input tensors, of shape
+     *            [D0, D1, ..., Daxis(i), ..., Dm]. For inputs of
+     *            {@link OperandType::TENSOR_QUANT8_ASYMM}, all input tensors
+     *            must have the same scale and zeroPoint.
+     * * n: An {@link OperandType::INT32} scalar, specifying the
+     *      concatenation axis.
      *
      * Outputs:
-     * * 0: The output, a tensor of the same type as the input tensors.
-     *      The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
+     * * 0: The output, a tensor of the same {@link OperandType} as the input
+     *      tensors. The output shape is [D0, D1, ..., sum(Daxis(i)), ..., Dm].
      */
     CONCATENATION = 2,
 
     /**
      * Performs an 2-D convolution operation.
      *
-     * The CONV_2D op sweeps a 2-D filter that can mix channels together over a batch of
-     * images, applying the filter to each window of each image of the appropriate size.
+     * The CONV_2D op sweeps a 2-D filter that can mix channels together over a
+     * batch of images, applying the filter to each window of each image of the
+     * appropriate size.
      *
-     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     * The output dimensions are functions of the filter dimensions, stride, and
+     * padding.
      *
      * The values in the output tensor are computed as:
      *
@@ -196,7 +220,7 @@
      *             bias[channel]
      *         )
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -205,63 +229,77 @@
      * Both explicit padding and implicit padding are supported.
      *
      * Inputs (explicit padding):
-     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
-     * * 1: A 4-D tensor, of shape [depth_out, filter_height, filter_width, depth_in],
-     *      specifying the filter.
+     * * 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.
+     *      For input tensor of {@link OperandType::TENSOR_FLOAT32}, the bias
+     *      should also be of {@link OperandType::TENSOR_FLOAT32}. For input
+     *      tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias
+     *      should be of {@link OperandType::TENSOR_INT32}, with zeroPoint of
+     *      0 and bias_scale == input_scale * filter_scale.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the left, in the ‘width’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the right, in the ‘width’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the top, in the ‘height’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the bottom, in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * 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
+     * * 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}, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
+     *      of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
+     *      of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
      *      bias_scale == input_scale * filter_scale.
-     * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
+     *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
-     * * 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.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+    *       walking through input in the ‘height’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * Outputs:
-     * * 0: The 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.
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, out_height, out_width, depth_out]. For output tensor of
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
+     *      must be satisfied: output_scale > input_scale * filter_scale.
      */
     CONV_2D = 3,
 
     /**
      * Performs a depthwise 2-D convolution operation.
      *
-     * Given an input tensor of shape [batches, height, width, depth_in] and a filter
-     * 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.
+     * Given an input tensor of shape [batches, height, width, depth_in] and a
+     * filter 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 output dimensions are functions of the filter dimensions, stride, and
+     * padding.
      *
      * The values in the output tensor are computed as:
      *
@@ -271,7 +309,7 @@
      *             filter[1, di, dj, k * channel_multiplier + q]
      *         )
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -280,82 +318,97 @@
      * Both explicit padding and implicit padding are supported.
      *
      * Inputs (explicit padding):
-     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 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
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+     *      tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
+     *      of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
+     *      of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
      *      bias_scale == input_scale * filter_scale.
-     * * 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.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the left, in the ‘width’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the right, in the ‘width’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the top, in the ‘height’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the bottom, in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 8: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 9: An {@link OperandType::INT32} scalar, specifying the depthwise
+     *      multiplier.
+     * * 10: An {@link OperandType::INT32} scalar, and has to be one of the
+     *       {@link FusedActivationFunc} values. Specifies the activation to
+     *       invoke on the result.
      *
      * Inputs (implicit padding):
-     * * 0: A 4-D tensor, of shape [batches, height, width, depth_in], specifying the input.
+     * * 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
+     * * 2: A 1-D tensor, of shape [depth_out], specifying the bias. For input
+     *      tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
+     *      of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
+     *      of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
      *      bias_scale == input_scale * filter_scale.
-     * * 3: An INT32 value, specifying the implicit padding scheme, has to be one of the
+     * * 3: An {@link OperandType::INT32} scalar, specifying the implicit
+     *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
-     * * 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.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the depthwise
+     *      multiplier.
+     * * 7: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * Outputs:
-     * * 0: The 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.
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, out_height, out_width, depth_out]. For output tensor of
+     *      {@link OperandType::TENSOR_QUANT8_ASYMM}, the following condition
+     *      must be satisfied: output_scale > input_scale * filter_scale.
      */
     DEPTHWISE_CONV_2D = 4,
 
     /**
      * Rearranges data from depth into blocks of spatial data.
      *
-     * More specifically, this op outputs a copy of the input tensor where values from
-     * the depth dimension are moved in spatial blocks to the height and width dimensions.
-     * The value block_size indicates the input block size and how the data is moved.
+     * More specifically, this op outputs a copy of the input tensor where
+     * values from the depth dimension are moved in spatial blocks to the height
+     * and width dimensions. The value block_size indicates the input block size
+     * and how the data is moved.
      *
-     * Chunks of data of size block_size * block_size from depth are rearranged into
-     * non-overlapping blocks of size block_size x block_size.
+     * Chunks of data of size block_size * block_size from depth are rearranged
+     * into non-overlapping blocks of size block_size x block_size.
      *
-     * The width of the output tensor is input_depth * block_size, whereas the height is
-     * input_height * block_size.
-     * The depth of the input tensor must be divisible by block_size * block_size
+     * The width of the output tensor is input_depth * block_size, whereas the
+     * height is input_height * block_size. The depth of the input tensor must
+     * be divisible by block_size * block_size
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@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 {@link OperandType::INT32} scalar, specifying the block_size.
+     *      block_size must be >=1 and block_size * block_size must be a divisor
+     *      of the input depth.
      *
      * Outputs:
-     * * 0: The output 4-D tensor, of shape [batch, height*block_size, width*block_size,
-     *      depth/(block_size*block_size)].
+     * * 0: The output 4-D tensor, of shape [batch, height*block_size,
+     *      width*block_size, depth/(block_size*block_size)].
      */
     DEPTH_TO_SPACE = 5,
 
@@ -366,16 +419,16 @@
      *
      *     output = (input - zeroPoint) * scale.
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@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 {@link OperandType::TENSOR_QUANT8_ASYMM}.
      *
      * Outputs:
-     * * 0: The output tensor of same shape as input0, but with type
+     * * 0: The output tensor of same shape as input0, but with
      *      {@link OperandType::TENSOR_FLOAT32}.
      */
     DEQUANTIZE = 6,
@@ -401,7 +454,7 @@
      * and an error must be reported.
      *
      * Inputs:
-     * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32} type.
+     * * 0: Lookups. A 1-D tensor of {@link OperandType::TENSOR_INT32}.
      *      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.
@@ -416,7 +469,7 @@
     /**
      * Computes element-wise floor() on the input tensor.
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Supported tensor rank: up to 4
@@ -425,45 +478,51 @@
      * * 0: A tensor.
      *
      * Outputs:
-     * * 0: The output tensor, of the same type and dimensions as the input tensor.
+     * * 0: The output tensor, of the same {@link OperandType} and dimensions as
+     *      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.
+     * Denotes a fully (densely) connected layer, which connects all elements
+     * in the input tensor with each element in the output tensor.
      *
      * This layer implements the operation:
      *
      *     outputs = activation(inputs * weights’ + bias)
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: up to 4.
      *
      * Inputs:
-     * * 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.
-     *      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
+     * * 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. For input
+     *      tensor of {@link OperandType::TENSOR_FLOAT32}, the bias should
+     *      also be of {@link OperandType::TENSOR_FLOAT32}. For input tensor
+     *      of {@link OperandType::TENSOR_QUANT8_ASYMM}, the bias should be
+     *      of {@link OperandType::TENSOR_INT32}, with zeroPoint of 0 and
      *      bias_scale == input_scale * filter_scale.
-     * * 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.
+     * * 3: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * Outputs:
-     * * 0: The 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.
+     * * 0: The output tensor, of shape [batch_size, num_units]. For output
+     *      tensor of {@link OperandType::TENSOR_QUANT8_ASYMM}, the following
+     *      condition must be satisfied:
+     *      output_scale > input_scale * filter_scale.
      */
     FULLY_CONNECTED = 9,
 
@@ -495,19 +554,22 @@
      * must be concatenated.
      *
      * Inputs:
-     * * 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.
+     * * 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 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.
+     *      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,
@@ -521,32 +583,37 @@
      *         input[batch, row, col, channel] /
      *         sqrt(sum_{c} pow(input[batch, row, col, c], 2))
      *
-     * For input tensor with more dimensions, independently normalizes each 1-D slice along dimension dim.
+     * For input tensor with more dimensions, independently normalizes each 1-D
+     * slice along dimension dim.
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
-     * Supported tensor rank: 4, with "NHWC" data layout (i.e., Num_samples, Height, Width, and Channels).
+     * 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].
      *
      * Outputs:
-     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, out_height, out_width, depth].
      */
     L2_NORMALIZATION = 11,
 
     /**
      * Performs an 2-D L2 pooling operation.
      *
-     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     * The output dimensions are functions of the filter dimensions, stride, and
+     * padding.
      *
      * 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))
+     *         sqrt(sum_{i, j} pow(input[batch, row + i, col + j, channel], 2) /
+     *              sum(1))
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Supported tensor rank: 4, with "NHWC" data layout.
@@ -554,62 +621,82 @@
      * 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.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the left, in the ‘width’ dimension.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the right, in the ‘width’ dimension.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the top, in the ‘height’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the bottom, in the ‘height’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+     *      width.
+     * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+     *      height.
+     * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * 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
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
+     *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
-     * * 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.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+     *      width.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+     *      height.
+     * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * Outputs:
-     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, out_height, out_width, depth].
      */
     L2_POOL_2D = 12,
 
     /**
      * Applies Local Response Normalization along the depth dimension.
      *
-     * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the last
-     * 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.
+     * The 4-D input tensor is treated as a 3-D array of 1-D vectors (along the
+     * last 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.
      *
      * 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)
+     *     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:
+     * Supported tensor {@link OperandType}:
      * * {@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 {@link OperandType::INT32} scalar, specifying the radius of
+     *      the normalization window.
+     * * 2: An {@link OperandType::FLOAT32} scalar, specifying the bias, must
+     *      not be zero.
+     * * 3: An {@link OperandType::FLOAT32} scalar, specifying the scale
+     *      factor, alpha.
+     * * 4: An {@link OperandType::FLOAT32} scalar, specifying the exponent,
+     *      beta.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
@@ -623,7 +710,7 @@
      *
      *     output = 1 / (1 + exp(-input))
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -634,7 +721,7 @@
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *      For {@link OperandType::TENSOR_QUANT8_ASYMM} type,
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the scale must be 1.f / 256 and the zeroPoint must be 0.
      */
     LOGISTIC = 14,
@@ -650,18 +737,19 @@
      *
      * * 1: Input. Dim.size >= 1, no restriction on DataType.
      * * 2: Weight. Optional. Dim.size == 1, DataType: Float.
-     *     If not set, each input element is considered to have the same weight of
-     *     1.0.
+     *     If not set, each input element is considered to have the same weight
+     *     of 1.0.
      *     Tensor[1].Dim[0] == Tensor[2].Dim[0]
      * * 3: Type:
      *        Sparse: Value LSHProjectionType_SPARSE(=1).
      *          Computed bit vector is considered to be sparse.
-     *          Each output element is an int32 made up of multiple bits computed from
-     *          hash functions.
+     *          Each output element is an int32 made up of multiple bits
+     *          computed from hash functions.
      *
      *        Dense: Value LSHProjectionType_DENSE(=2).
-     *          Computed bit vector is considered to be dense. Each output element
-     *          represents a bit and can take the value of either 0 or 1.
+     *          Computed bit vector is considered to be dense. Each output
+     *          element represents a bit and can take the value of either
+     *          0 or 1.
      *
      * Outputs:
      * * 0: If the projection type is sparse:
@@ -681,9 +769,12 @@
      * \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; \\
+     * 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}
@@ -695,7 +786,8 @@
      * * \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$g\f$ 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,
@@ -715,29 +807,32 @@
      * * \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)">
+     * * \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.
+     * * 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-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.
+     * * 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
-     *   recurrent projection layer exists, and should otherwise have no value.
+     * * 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:
      *
@@ -749,8 +844,8 @@
      * 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.
+     * 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.)
      *
@@ -758,56 +853,74 @@
      * http://arxiv.org/pdf/1503.04069.pdf
      * Greff et al. "LSTM: A Search Space Odyssey"
      *
-     * Supported tensor types (type T):
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Inputs:
      * * 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.
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [batch_size, input_size], where “batch_size” corresponds to the
+     *      batching dimension, and “input_size” is the size of the input.
      * * 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.
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units, input_size], where “num_units” corresponds to the
+     *      number of cell units.
      * * 2: The input-to-forget weights (\f$W_{xf}\f$).
-     *      A 2-D tensor of type T, of shape [num_units, input_size].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units, input_size].
      * * 3: The input-to-cell weights (\f$W_{xc}\f$).
-     *      A 2-D tensor of type T, of shape [num_units, input_size].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units, input_size].
      * * 4: The input-to-output weights (\f$W_{xo}\f$).
-     *      A 2-D tensor of type T, of shape [num_units, input_size].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units, input_size].
      * * 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.
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units, output_size], where “output_size” corresponds to either
+     *      the number of cell units (i.e., “num_units”), or the second
+     *      dimension of the “projection_weights”, if defined.
      * * 6: The recurrent-to-forget weights (\f$W_{hf}\f$).
-     *      A 2-D tensor of type T, of shape [num_units, output_size].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units, output_size].
      * * 7: The recurrent-to-cell weights (\f$W_{hc}\f$).
-     *      A 2-D tensor of type T, of shape [num_units, output_size].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units, output_size].
      * * 8: The recurrent-to-output weights (\f$W_{ho}\f$).
-     *      A 2-D tensor of type T, of shape [num_units, output_size].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units, output_size].
      * * 9: The cell-to-input weights (\f$W_{ci}\f$). Optional.
-     *      A 1-D tensor of type T, of shape [num_units].
+     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units].
      * * 10:The cell-to-forget weights (\f$W_{cf}\f$). Optional.
-     *      A 1-D tensor of type T, of shape [num_units].
+     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units].
      * * 11:The cell-to-output weights (\f$W_{co}\f$). Optional.
-     *      A 1-D tensor of type T, of shape [num_units].
+     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units].
      * * 12:The input gate bias (\f$b_i\f$). Optional.
-     *      A 1-D tensor of type T, of shape [num_units].
+     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units].
      * * 13:The forget gate bias (\f$b_f\f$).
-     *      A 1-D tensor of type T, of shape [num_units].
+     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units].
      * * 14:The cell bias (\f$b_c\f$).
-     *      A 1-D tensor of type T, of shape [num_units].
+     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units].
      * * 15:The output gate bias (\f$b_o\f$).
-     *      A 1-D tensor of type T, of shape [num_units].
+     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units].
      * * 16:The projection weights (\f$W_{proj}\f$). Optional.
-     *      A 2-D tensor of type T, of shape [output_size, num_units].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [output_size, num_units].
      * * 17:The projection bias (\f$b_{proj}\f$). Optional.
-     *      A 1-D tensor of type T, of shape [output_size].
+     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [output_size].
      * * 18:The output state (in) (\f$h_{t-1}\f$).
-     *      A 2-D tensor of type T, of shape [batch_size, output_size].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, 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].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [batch_size, num_units].
      * * 20:The activation function (\f$g\f$).
      *      A value indicating the activation function:
      *      <ul>
@@ -817,38 +930,43 @@
      *      <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.
-     * * 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
+     * * 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.
+     * * 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: 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.
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, 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].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [batch_size, output_size].
      * * 2: The cell state (out) (\f$C_t\f$).
-     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [batch_size, num_units].
      * * 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 (out)” value.
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [batch_size, output_size]. This is effectively the same as the
+     *      current “output state (out)” value.
      */
     LSTM = 16,
 
     /**
      * Performs an 2-D max pooling operation.
      *
-     * The output dimensions are functions of the filter dimensions, stride, and padding.
+     * The output dimensions are functions of the filter dimensions, stride, and
+     * padding.
      *
      * 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:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -857,52 +975,68 @@
      * 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.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the left, in the ‘width’ dimension.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the right, in the ‘width’ dimension.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the top, in the ‘height’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the padding on
+     *      the bottom, in the ‘height’ dimension.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 6: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 7: An {@link OperandType::INT32} scalar, specifying the filter
+     *      width.
+     * * 8: An {@link OperandType::INT32} scalar, specifying the filter
+     *      height.
+     * * 9: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * 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
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the implicit
+     *      padding scheme, has to be one of the
      *      following values: {0 (NONE), 1 (SAME), 2 (VALID)}.
-     * * 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.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘width’ dimension.
+     * * 3: An {@link OperandType::INT32} scalar, specifying the stride when
+     *      walking through input in the ‘height’ dimension.
+     * * 4: An {@link OperandType::INT32} scalar, specifying the filter
+     *      width.
+     * * 5: An {@link OperandType::INT32} scalar, specifying the filter
+     *      height.
+     * * 6: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * Outputs:
-     * * 0: The output 4-D tensor, of shape [batches, out_height, out_width, depth].
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, out_height, out_width, depth].
      */
     MAX_POOL_2D = 17,
 
     /**
      * Multiplies two tensors, element-wise.
      *
-     * 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.
+     * Takes two input tensors of identical {@link OperandType} and compatible
+     * dimensions. The output is the product of both input tensors, optionally
+     * modified by an activation function.
      *
      * Two dimensions are compatible when:
      *     1. they are equal, or
      *     2. one of them is 1
      *
-     * 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.
+     * 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:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -910,14 +1044,17 @@
      *
      * 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.
+     * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+     *      as input0.
+     * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * Outputs:
-     * * 0: The 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.
+     * * 0: The product, a tensor of the same {@link OperandType} as input0.
+     *      For output tensor of {@link OperandType::TENSOR_QUANT8_ASYMM},
+     *      the following condition must be satisfied:
+     *      output_scale > input1_scale * input2_scale.
      */
     MUL = 18,
 
@@ -928,7 +1065,7 @@
      *
      *     output = max(0, input)
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -949,7 +1086,7 @@
      *
      *     output = min(1.f, max(-1.f, input))
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -970,7 +1107,7 @@
      *
      *     output = min(6, max(0, input))
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -987,10 +1124,10 @@
     /**
      * Reshapes a tensor.
      *
-     * Given tensor, this operation returns a tensor that has the same values as tensor,
-     * but with a newly specified shape.
+     * Given tensor, this operation returns a tensor that has the same values as
+     * tensor, but with a newly specified shape.
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -998,9 +1135,9 @@
      *
      * 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.
+     * * 1: A 1-D tensor of {@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.
      *
      * Outputs:
      * * 0: The output tensor, of shape specified by the input shape.
@@ -1010,22 +1147,26 @@
     /**
      * 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. The corner pixels of output may not be the same as
-     * corner pixels of input.
+     * Resized images must be distorted if their output aspect ratio is not the
+     * same as input aspect ratio. The corner pixels of output may not be the
+     * same as corner pixels of input.
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@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 height of the output tensor.
-     * * 2: An INT32 value, specifying the output width of the output tensor.
+     * * 0: A 4-D tensor, of shape [batches, height, width, depth], specifying
+     *      the input.
+     * * 1: An {@link OperandType::INT32} scalar, specifying the output
+     *      height of the output tensor.
+     * * 2: An {@link OperandType::INT32} scalar, specifying the output
+     *      width of the output tensor.
      *
      * Outputs:
-     * * 0: The output 4-D tensor, of shape [batches, new_height, new_width, depth].
+     * * 0: The output 4-D tensor, of shape
+     *      [batches, new_height, new_width, depth].
      */
     RESIZE_BILINEAR = 23,
 
@@ -1033,7 +1174,8 @@
      * A basic recurrent neural network layer.
      *
      * This layer implements the operation:
-     * outputs = state = activation(inputs * input_weights + state * recurrent_weights + bias)
+     * outputs = state = activation(inputs * input_weights +
+     *                              state * recurrent_weights + bias)
      *
      * Where:
      * * “input_weights” is a weight matrix that multiplies the inputs;
@@ -1044,42 +1186,49 @@
      * * “activation” is the function passed as the “fused_activation_function”
      *   argument (if not “NONE”).
      *
-     * Supported tensor types (Type T):
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Inputs:
      * * 0: input.
-     *      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.
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32} of shape
+     *      [batch_size, input_size], where “batch_size” corresponds to the
+     *      batching dimension, and “input_size” is the size of the input.
      * * 1: weights.
-     *      A 2-D tensor of type T, of shape [num_units, input_size], where
-     *      “num_units” corresponds to the number of units.
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units, input_size], where “num_units” corresponds to the
+     *      number of units.
      * * 2: recurrent_weights.
-     *      A 2-D tensor of type T, of shape [num_units, num_units], with columns
-     *      corresponding to the weights from each unit.
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units, num_units], with columns corresponding to the weights
+     *      from each unit.
      * * 3: bias.
-     *      A 1-D tensor of type T, of shape [num_units].
+     *      A 1-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units].
      * * 4: hidden state (in).
-     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, 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.
+     *      An optional {@link FusedActivationFunc} value indicating the
+     *      activation function. If “NONE” is specified then it results in a
+     *      linear activation.
      *
      * Outputs:
      * * 0: hidden state (out).
-     *      A 2-D tensor of type T, of shape [batch_size, num_units].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [batch_size, num_units].
      *
      * * 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.
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [batch_size, num_units]. This is effectively the same as the
+     *      current state value.
      */
     RNN = 24,
 
     /**
-     * Computes the softmax activation on the input tensor element-wise, per batch, by
-     * normalizing the input vector so the maximum coefficient is zero.
+     * Computes the softmax activation on the input tensor element-wise, per
+     * batch, by normalizing the input vector so the maximum coefficient is
+     * zero.
      *
      * The output is calculated using this formula:
      *
@@ -1087,7 +1236,7 @@
      *         exp((input[batch, i] - max(input[batch, :])) * beta) /
      *         sum_{k}{exp((input[batch, k] - max(input[batch, :])) * beta)}
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
@@ -1095,11 +1244,12 @@
      *
      * Inputs:
      * * 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.
+     * * 1: An {@link OperandType::FLOAT32} scalar, specifying the positive
+     *      scaling factor for the exponent, beta.
      *
      * Outputs:
      * * 0: The output tensor of same shape as input0.
-     *      For {@link OperandType::TENSOR_QUANT8_ASYMM} type,
+     *      For {@link OperandType::TENSOR_QUANT8_ASYMM},
      *      the scale must be 1.f / 256 and the zeroPoint must be 0.
      */
     SOFTMAX = 25,
@@ -1107,30 +1257,33 @@
     /**
      * Rearranges blocks of spatial data, into depth.
      *
-     * More specifically, this op outputs a copy of the input tensor where values from
-     * the height and width dimensions are moved to the depth dimension.
-     * The value block_size indicates the input block size and how the data is moved.
+     * More specifically, this op outputs a copy of the input tensor where
+     * values from the height and width dimensions are moved to the depth
+     * dimension. The value block_size indicates the input block size and how
+     * the data is moved.
      *
-     * Chunks of data of size block_size * block_size from depth are rearranged into
-     * non-overlapping blocks of size block_size x block_size.
+     * Chunks of data of size block_size * block_size from depth are rearranged
+     * into non-overlapping blocks of size block_size x block_size.
      *
      * 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:
+     * Supported tensor {@link OperandType}:
      * * {@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 {@link OperandType::INT32} scalar, specifying the block_size.
+     *      block_size must be >=1 and block_size must be a divisor of both the
+     *      input height and width.
      *
      * Outputs:
-     * * 0: The output 4-D tensor, of shape [batch, height/block_size, width/block_size,
-     *      depth*block_size*block_size].
+     * * 0: The output 4-D tensor, of shape [batch, height/block_size,
+     *      width/block_size, depth*block_size*block_size].
      */
     SPACE_TO_DEPTH = 26,
 
@@ -1147,21 +1300,22 @@
      * INTERSPEECH, 2015.
      *
      * It processes the incoming input using a 2-stage filtering mechanism:
-     * * stage 1 performs filtering on the "features" dimension, whose outputs get
-     *   pushed into a memory of fixed-size memory_size.
+     * * stage 1 performs filtering on the "features" dimension, whose outputs
+     *   get pushed into a memory of fixed-size memory_size.
      * * stage 2 performs filtering on the "time" dimension of the memory_size
      *   memoized outputs of stage 1.
      *
      * Specifically, for rank 1, this layer implements the operation:
      *
-     *     memory = push(conv1d(inputs, weights_feature, feature_dim, "PADDING_VALID"));
+     *     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
-     *   convolving the input with every “feature filter”), and whose outputs get
-     *   pushed, stacked in order, into the fixed-size “memory” (the oldest entry
-     *   gets dropped);
+     *   convolving the input with every “feature filter”), and whose outputs
+     *   get pushed, stacked in order, into the fixed-size “memory” (the oldest
+     *   entry gets dropped);
      * * “weights_time” is a weights matrix that processes the “memory” (by a
      *   batched matrix multiplication on the num_units);
      * * “bias” is an optional bias vector (added to each output vector in the
@@ -1172,35 +1326,42 @@
      * Each rank adds a dimension to the weights matrices by means of stacking
      * the filters.
      *
-     * Supported tensor types (type T):
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Inputs:
      * * 0: input.
-     *      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.
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [batch_size, input_size], where “batch_size” corresponds to the
+     *      batching dimension, and “input_size” is the size of the input.
      * * 1: weights_feature.
-     *      A 2-D tensor of type T, of shape [num_units, input_size], where
-     *      “num_units” corresponds to the number of units.
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units, input_size], where “num_units” corresponds to the
+     *      number of units.
      * * 2: weights_time.
-     *      A 2-D tensor of type T, of shape [num_units, memory_size], where
-     *      “memory_size” corresponds to the fixed-size of the memory.
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [num_units, memory_size], where “memory_size” corresponds to the
+     *      fixed-size of the memory.
      * * 3: bias.
-     *      An optional 1-D tensor of type T, of shape [num_units].
+     *      An optional 1-D tensor of {@link OperandType::TENSOR_FLOAT32},
+     *      of shape [num_units].
      * * 4: state (in).
-     *      A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+     *      [batch_size, (memory_size - 1) * num_units * rank].
      * * 5: rank.
      *      The rank of the SVD approximation.
      * * 6: fused_activation_function.
-     *      An optional {@link FusedActivationFunc} value indicating the activation function.
-     *      If “NONE” is specified then it results in a linear activation.
+     *      An optional {@link FusedActivationFunc} value indicating the
+     *      activation function. If “NONE” is specified then it results in a
+     *      linear activation.
      *
      * Outputs:
      * * 0: state (out).
-     *      A 2-D tensor of type T, of shape [batch_size, (memory_size - 1) * num_units * rank].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, 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].
+     *      A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
+         *      [batch_size, num_units].
      */
     SVDF = 27,
 
@@ -1211,7 +1372,7 @@
      *
      *     output = tanh(input)
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Supported tensor rank: up to 4.
@@ -1227,7 +1388,8 @@
     /**
      * OEM specific operation.
      *
-     * This operation is OEM specific. It should only be used for OEM applications.
+     * This operation is OEM specific. It should only be used for OEM
+     * applications.
      */
     OEM_OPERATION = 10000,
 };
@@ -1274,8 +1436,8 @@
     CONSTANT_REFERENCE,
 
     /**
-     * The operand does not have a value. This is valid only for optional arguments
-     * of operations.
+     * The operand does not have a value. This is valid only for optional
+     * arguments of operations.
      */
     NO_VALUE,
 };
@@ -1391,7 +1553,8 @@
 
     /**
      * Where to find the data for this operand.
-     * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or NO_VALUE:
+     * If the lifetime is TEMPORARY_VARIABLE, MODEL_INPUT, MODEL_OUTPUT, or
+     * NO_VALUE:
      * - All the fields must be 0.
      * If the lifetime is CONSTANT_COPY:
      * - location.poolIndex is 0.
@@ -1485,9 +1648,9 @@
  */
 struct RequestArgument {
     /**
-     * If true, the argument does not have a value. This can be used for operations
-     * that take optional arguments. If true, the fields of location are set to 0 and
-     * the dimensions vector is left empty.
+     * If true, the argument does not have a value. This can be used for
+     * operations that take optional arguments. If true, the fields of location
+     * are set to 0 and the dimensions vector is left empty.
      */
     bool hasNoValue;
 
@@ -1499,10 +1662,10 @@
     /**
      * Updated dimension information.
      *
-     * If dimensions.size() > 0, dimension information was provided along with the
-     * argument. This can be the case for models that accept inputs of varying size.
-     * This can't change the rank, just the value of the dimensions that were
-     * unspecified in the model.
+     * If dimensions.size() > 0, dimension information was provided along with
+     * the argument. This can be the case for models that accept inputs of
+     * varying size. This can't change the rank, just the value of the
+     * dimensions that were unspecified in the model.
      */
     vec<uint32_t> dimensions;
 };
diff --git a/neuralnetworks/1.1/types.hal b/neuralnetworks/1.1/types.hal
index 3fa47a6..e4c656d 100644
--- a/neuralnetworks/1.1/types.hal
+++ b/neuralnetworks/1.1/types.hal
@@ -29,87 +29,95 @@
     /**
      * BatchToSpace for N-dimensional tensors.
      *
-     * 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.
+     * 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.
      *
      * This is the reverse of SpaceToBatch.
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: 4
      *
      * Inputs:
-     * 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.
+     * * 0: An n-D tensor, specifying the tensor to be reshaped
+     * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
+     *      sizes for each spatial dimension of the input tensor. All values
+     *      must be >= 1.
      *
      * Outputs:
-     * 0: A tensor of the same type as input0.
+     * * 0: A tensor of the same {@link OperandType} as input0.
      */
     BATCH_TO_SPACE_ND = 29,
 
     /**
      * Element-wise division of two tensors.
      *
-     * 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.
+     * Takes two input tensors of identical {@link OperandType} and compatible
+     * dimensions. The output is the result of dividing the first input tensor
+     * by the second, optionally modified by an activation function.
      *
      * Two dimensions are compatible when:
      *     1. they are equal, or
      *     2. one of them is 1
      *
-     * The size of the output is the maximum size along each dimension of the input operands.
-     * It starts with the trailing dimensions, and works its way forward.
+     * The size of the output is the maximum size along each dimension of the
+     * input operands. It starts with the trailing dimensions, and works its way
+     * forward.
      *
      * Example:
      *     input1.dimension =    {4, 1, 2}
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the first input.
-     * 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: An n-D tensor, specifying the first input.
+     * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+     *      as input0.
+     * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * Outputs:
-     * 0: A tensor of the same type as input0.
+     * * 0: A tensor of the same {@link OperandType} as input0.
      */
     DIV = 30,
 
     /**
      * Computes the mean of elements across dimensions of a tensor.
      *
-     * 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.
+     * 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 dimensions to reduce have 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:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: A tensor, specifying the input.
-     * 1: A 1-D Tensor of type TENSOR_INT32. The dimensions to reduce. If None (the default),
-     *    reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)).
-     * 2: An INT32 value, keep_dims. If positive, retains reduced dimensions with length 1.
+     * * 0: A tensor, specifying the input.
+     * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}. The dimensions
+     *      to reduce. If None (the default), reduces all dimensions. Must be in
+     *      the range [-rank(input_tensor), rank(input_tensor)).
+     * * 2: An {@link OperandType::INT32} scalar, keep_dims. If positive,
+     *      retains reduced dimensions with length 1.
      *
      * Outputs:
-     * 0: A tensor of the same type as input0.
+     * * 0: A tensor of the same {@link OperandType} as input0.
      */
     MEAN = 31,
 
@@ -118,170 +126,193 @@
      *
      * This operation pads a tensor according to the specified paddings.
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 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.
+     * * 0: An n-D tensor, specifying the tensor to be padded.
+     * * 1: A 2-D Tensor of {@link OperandType::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.
+     * * 0: A tensor of the same {@link OperandType} as input0.
      */
     PAD = 32,
 
     /**
      * 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
-     * in the output, the spatial dimensions [1, ..., M] correspond to the position within the grid,
-     * and the batch dimension combines both the position within a spatial block and the original
-     * batch position. Prior to division into blocks, the spatial dimensions of the input are
-     * optionally zero padded according to paddings.
+     * 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 in the output, the spatial dimensions
+     * [1, ..., M] correspond to the position within the grid, and the batch
+     * dimension combines both the position within a spatial block and the
+     * original batch position. Prior to division into blocks, the spatial
+     * dimensions of the input are optionally zero padded according to paddings.
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@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. 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.
+     * * 0: An n-D tensor, specifying the input.
+     * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the block
+     *      sizes for each spatial dimension of the input tensor. All values
+     *      must be >= 1.
+     * * 2: A 2-D Tensor of {@link OperandType::TENSOR_INT32}, the paddings
+     *      for each spatial dimension of the 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.
+     * * 0: A tensor of the same {@link OperandType} as input0.
      */
     SPACE_TO_BATCH_ND = 33,
 
     /**
      * Removes dimensions of size 1 from the shape of a tensor.
      *
-     * 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 the axes (input1).
+     * Given a tensor input, this operation returns a tensor of the same
+     * {@link OperandType} 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 the axes (input1).
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 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.
+     * * 0: An n-D tensor, the tensor to be squeezed.
+     * * 1: An optional 1-D tensor of {@link OperandType::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
-     *    dimensions of size 1 removed.
+     * * 0: A tensor of the same {@link OperandType} as input0. Contains the
+     *      same data as input, but has one or more dimensions of size 1
+     *      removed.
      */
     SQUEEZE = 34,
 
     /**
      * Extracts a strided slice of a tensor.
      *
-     * 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.
+     * 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:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 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. 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. 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.
+     * * 0: An n-D tensor, specifying the tensor to be sliced.
+     * * 1: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the starts of
+     *      the dimensions of the input tensor to be sliced. The length must be
+     *      of rank(input0).
+     * * 2: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the ends of
+     *      the dimensions of the input tensor to be sliced. The length must be
+     *      of rank(input0).
+     * * 3: A 1-D Tensor of {@link OperandType::TENSOR_INT32}, the strides of
+     *      the dimensions of the input tensor to be sliced. The length must be
+     *      of rank(input0).
+     * * 4: An {@link OperandType::INT32} scalar, 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 {@link OperandType::INT32} scalar, 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 {@link OperandType::INT32} scalar, 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.
+     * * 0: A tensor of the same {@link OperandType} as input0.
      */
     STRIDED_SLICE = 35,
 
     /**
      * 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
-     * modified by an activation function.
+     * Takes two input tensors of identical {@link OperandType} and compatible
+     * dimensions. The output is the result of subtracting the second input
+     * tensor from the first one, optionally modified by an activation function.
      *
      * Two dimensions are compatible when:
      *     1. they are equal, or
      *     2. one of them is 1
      *
-     * The size of the output is the maximum size along each dimension of the input operands.
-     * It starts with the trailing dimensions, and works its way forward.
+     * The size of the output is the maximum size along each dimension of the
+     * input operands. It starts with the trailing dimensions, and works its way
+     * forward.
      *
      * Example:
      *     input1.dimension =    {4, 1, 2}
      *     input2.dimension = {5, 4, 3, 1}
      *     output.dimension = {5, 4, 3, 2}
      *
-     * Supported tensor types:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 0: An n-D tensor, specifying the first input.
-     * 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: An n-D tensor, specifying the first input.
+     * * 1: A tensor of the same {@link OperandType}, and compatible dimensions
+     *      as input0.
+     * * 2: An {@link OperandType::INT32} scalar, and has to be one of the
+     *      {@link FusedActivationFunc} values. Specifies the activation to
+     *      invoke on the result.
      *
      * Outputs:
-     * 0: A tensor of the same type as input0.
+     * * 0: A tensor of the same {@link OperandType} as input0.
      */
     SUB = 36,
 
     /**
-     * Transposes the input tensor, permuting the dimensions according to the perm tensor.
+     * Transposes the input tensor, permuting the dimensions according to the
+     * perm tensor.
      *
-     * 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.
+     * 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:
+     * Supported tensor {@link OperandType}:
      * * {@link OperandType::TENSOR_FLOAT32}
      * * {@link OperandType::TENSOR_QUANT8_ASYMM}
      *
      * Supported tensor rank: up to 4
      *
      * Inputs:
-     * 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.
+     * * 0: An n-D tensor, specifying the tensor to be transposed.
+     * * 1: An optional 1-D Tensor of {@link OperandType::TENSOR_INT32},
+     *      the permutation of the dimensions of the input tensor.
      *
      * Outputs:
-     * 0: A tensor of the same type as input0.
+     * * 0: A tensor of the same {@link OperandType} as input0.
      */
     TRANSPOSE = 37,
 };