Rebase

Signed-off-by: Ryan Nett <JNett96@gmail.com>

Author: rnett

tensorflow-core/tensorflow-core-api/src/gen/annotations/org/tensorflow/op/Ops.java

@@ -364,10 +364,10 @@ public final class Ops {
 
   public final SignalOps signal;
 
-  public final QuantizationOps quantization;
-
   public final TrainOps train;
 
+  public final QuantizationOps quantization;
+
   private final Scope scope;
 
   private Ops(Scope scope) {
@@ -390,8 +390,8 @@ private Ops(Scope scope) {
     math = new MathOps(this);
     audio = new AudioOps(this);
     signal = new SignalOps(this);
-    quantization = new QuantizationOps(this);
     train = new TrainOps(this);
+    quantization = new QuantizationOps(this);
   }
 
   /**

tensorflow-core/tensorflow-core-generator/src/main/java/org/tensorflow/processor/operator/OperatorProcessor.java

@@ -16,34 +16,13 @@
 
 package org.tensorflow.processor.operator;
 
-import com.github.javaparser.ast.comments.JavadocComment;
-import com.github.javaparser.javadoc.Javadoc;
-import com.google.common.base.CaseFormat;
-import com.google.common.base.Strings;
-import com.google.common.collect.HashMultimap;
-import com.google.common.collect.Multimap;
-import com.squareup.javapoet.ClassName;
 import com.squareup.javapoet.FieldSpec;
 import com.squareup.javapoet.JavaFile;
 import com.squareup.javapoet.MethodSpec;
-import com.squareup.javapoet.ParameterSpec;
-import com.squareup.javapoet.TypeName;
 import com.squareup.javapoet.TypeSpec;
-import com.squareup.javapoet.TypeVariableName;
 import java.io.IOException;
 import java.util.List;
 import javax.lang.model.element.Modifier;
-import javax.lang.model.element.Name;
-import javax.lang.model.element.TypeElement;
-import javax.lang.model.element.TypeParameterElement;
-import javax.lang.model.element.VariableElement;
-import javax.lang.model.type.NoType;
-import javax.lang.model.type.TypeMirror;
-import javax.lang.model.type.TypeVariable;
-import javax.lang.model.util.ElementFilter;
-import javax.lang.model.util.Elements;
-import javax.lang.model.util.Types;
-import javax.tools.Diagnostic.Kind;
 import org.tensorflow.Names;
 
 /**
@@ -59,8 +38,6 @@
  */
 public final class OperatorProcessor extends BaseOperatorProcessor<TypeSpec> {
 
-  private static final TypeName T_DEVICE_SPEC = ClassName.get("org.tensorflow", "DeviceSpec");
-
   @Override
   protected void write(TypeSpec spec) {
     try {

tensorflow-kotlin-parent/tensorflow-core-kotlin/src/gen/annotations/org/tensorflow/op/kotlin/AudioOps.kt

@@ -36,7 +36,7 @@ public class AudioOps(
     /**
      * Get the parent [KotlinOps] object.
      */
-    public val ops: KotlinOps,
+    public val ops: KotlinOps
 ) {
     public val java: org.tensorflow.op.AudioOps = ops.java.audio
 
@@ -47,29 +47,24 @@ public class AudioOps(
 
     /**
      * Produces a visualization of audio data over time.
-     *
      *  Spectrograms are a standard way of representing audio information as a series of
      *  slices of frequency information, one slice for each window of time. By joining
      *  these together into a sequence, they form a distinctive fingerprint of the sound
      *  over time.
-     *
      *  This op expects to receive audio data as an input, stored as floats in the range
      *  -1 to 1, together with a window width in samples, and a stride specifying how
      *  far to move the window between slices. From this it generates a three
      *  dimensional output. The first dimension is for the channels in the input, so a
      *  stereo audio input would have two here for example. The second dimension is time,
      *  with successive frequency slices. The third dimension has an amplitude value for
      *  each frequency during that time slice.
-     *
      *  This means the layout when converted and saved as an image is rotated 90 degrees
      *  clockwise from a typical spectrogram. Time is descending down the Y axis, and
      *  the frequency decreases from left to right.
-     *
      *  Each value in the result represents the square root of the sum of the real and
      *  imaginary parts of an FFT on the current window of samples. In this way, the
      *  lowest dimension represents the power of each frequency in the current window,
      *  and adjacent windows are concatenated in the next dimension.
-     *
      *  To get a more intuitive and visual look at what this operation does, you can run
      *  tensorflow/examples/wav_to_spectrogram to read in an audio file and save out the
      *  resulting spectrogram as a PNG image.
@@ -78,17 +73,20 @@ public class AudioOps(
      * @param windowSize How wide the input window is in samples. For the highest efficiency
      *  this should be a power of two, but other values are accepted.
      * @param stride How widely apart the center of adjacent sample windows should be.
-     * @param options carries optional attributes values
+     * @param options carries optional attribute values
      * @return a new instance of AudioSpectrogram
      * @see org.tensorflow.op.AudioOps.audioSpectrogram
+     * @param magnitudeSquared Sets the magnitudeSquared option.
+     *
      * @param magnitudeSquared Whether to return the squared magnitude or just the
      *  magnitude. Using squared magnitude can avoid extra calculations.
+     * @return this Options instance.
      */
     public fun audioSpectrogram(
         input: Operand<TFloat32>,
         windowSize: Long,
         stride: Long,
-        magnitudeSquared: Boolean? = null,
+        magnitudeSquared: Boolean? = null
     ): AudioSpectrogram = java.audioSpectrogram(
         input,
         windowSize,
@@ -100,33 +98,35 @@ public class AudioOps(
 
     /**
      * Decode a 16-bit PCM WAV file to a float tensor.
-     *
      *  The -32768 to 32767 signed 16-bit values will be scaled to -1.0 to 1.0 in float.
-     *
      *  When desired_channels is set, if the input contains fewer channels than this
      *  then the last channel will be duplicated to give the requested number, else if
      *  the input has more channels than requested then the additional channels will be
      *  ignored.
-     *
      *  If desired_samples is set, then the audio will be cropped or padded with zeroes
      *  to the requested length.
-     *
      *  The first output contains a Tensor with the content of the audio samples. The
      *  lowest dimension will be the number of channels, and the second will be the
      *  number of samples. For example, a ten-sample-long stereo WAV file should give an
      *  output shape of &#91;10, 2].
      *
      * @param contents The WAV-encoded audio, usually from a file.
-     * @param options carries optional attributes values
+     * @param options carries optional attribute values
      * @return a new instance of DecodeWav
      * @see org.tensorflow.op.AudioOps.decodeWav
+     * @param desiredChannels Sets the desiredChannels option.
+     *
      * @param desiredChannels Number of sample channels wanted.
+     * @return this Options instance.
+     * @param desiredSamples Sets the desiredSamples option.
+     *
      * @param desiredSamples Length of audio requested.
+     * @return this Options instance.
      */
     public fun decodeWav(
         contents: Operand<TString>,
         desiredChannels: Long? = null,
-        desiredSamples: Long? = null,
+        desiredSamples: Long? = null
     ): DecodeWav = java.decodeWav(
         contents,
         *listOfNotNull(
@@ -137,16 +137,14 @@ public class AudioOps(
 
     /**
      * Encode audio data using the WAV file format.
-     *
      *  This operation will generate a string suitable to be saved out to create a .wav
      *  audio file. It will be encoded in the 16-bit PCM format. It takes in float
      *  values in the range -1.0f to 1.0f, and any outside that value will be clamped to
      *  that range.
+     *  ``` audio``` is a 2-D float Tensor of shape ``` [length, channels]```.
+     *  ``` sample_rate``` is a scalar Tensor holding the rate to use (e.g. 44100).
      *
-     *  `audio` is a 2-D float Tensor of shape `&#91;length, channels]`.
-     *  `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100).
-     *
-     * @param audio 2-D with shape `&#91;length, channels]`.
+     * @param audio 2-D with shape ` [length, channels]`.
      * @param sampleRate Scalar containing the sample frequency.
      * @return a new instance of EncodeWav
      * @see org.tensorflow.op.AudioOps.encodeWav
@@ -159,7 +157,6 @@ public class AudioOps(
 
     /**
      * Transforms a spectrogram into a form that's useful for speech recognition.
-     *
      *  Mel Frequency Cepstral Coefficients are a way of representing audio data that's
      *  been effective as an input feature for machine learning. They are created by
      *  taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the
@@ -171,23 +168,35 @@ public class AudioOps(
      * @param spectrogram Typically produced by the Spectrogram op, with magnitude_squared
      *  set to true.
      * @param sampleRate How many samples per second the source audio used.
-     * @param options carries optional attributes values
+     * @param options carries optional attribute values
      * @return a new instance of Mfcc
      * @see org.tensorflow.op.AudioOps.mfcc
+     * @param upperFrequencyLimit Sets the upperFrequencyLimit option.
+     *
      * @param upperFrequencyLimit The highest frequency to use when calculating the
      *  ceptstrum.
+     * @return this Options instance.
+     * @param lowerFrequencyLimit Sets the lowerFrequencyLimit option.
+     *
      * @param lowerFrequencyLimit The lowest frequency to use when calculating the
      *  ceptstrum.
+     * @return this Options instance.
+     * @param filterbankChannelCount Sets the filterbankChannelCount option.
+     *
      * @param filterbankChannelCount Resolution of the Mel bank used internally.
+     * @return this Options instance.
+     * @param dctCoefficientCount Sets the dctCoefficientCount option.
+     *
      * @param dctCoefficientCount How many output channels to produce per time slice.
+     * @return this Options instance.
      */
     public fun mfcc(
         spectrogram: Operand<TFloat32>,
         sampleRate: Operand<TInt32>,
         upperFrequencyLimit: Float? = null,
         lowerFrequencyLimit: Float? = null,
         filterbankChannelCount: Long? = null,
-        dctCoefficientCount: Long? = null,
+        dctCoefficientCount: Long? = null
     ): Mfcc = java.mfcc(
         spectrogram,
         sampleRate,