No formatting on generation, update explicit API settings

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

tensorflow-kotlin-parent/tensorflow-core-kotlin/pom.xml

@@ -107,12 +107,21 @@
         <artifactId>kotlin-maven-plugin</artifactId>
         <version>${kotlin.version}</version>
         <configuration>
-          <args>
-            <arg>-Xopt-in=kotlin.contracts.ExperimentalContracts</arg>
-            <arg>-Xexplicit-api=strict</arg>
-          </args>
         </configuration>
         <executions>
+          <execution>
+            <id>compile</id>
+            <goals>
+              <goal>compile</goal>
+            </goals>
+            <configuration>
+              <args>
+                <arg>-Xopt-in=kotlin.contracts.ExperimentalContracts</arg>
+                <arg>-Xexplicit-api=strict</arg>
+              </args>
+            </configuration>
+          </execution>
+
           <execution>
             <id>kapt</id>
             <goals>

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

@@ -17,6 +17,9 @@
 //
 package org.tensorflow.op.kotlin
 
+import kotlin.Boolean
+import kotlin.Float
+import kotlin.Long
 import org.tensorflow.Operand
 import org.tensorflow.op.Scope
 import org.tensorflow.op.audio.AudioSpectrogram
@@ -26,9 +29,6 @@ import org.tensorflow.op.audio.Mfcc
 import org.tensorflow.types.TFloat32
 import org.tensorflow.types.TInt32
 import org.tensorflow.types.TString
-import kotlin.Boolean
-import kotlin.Float
-import kotlin.Long
 
 /**
  * An API for building `audio` operations as [Op][org.tensorflow.op.Op]s
@@ -54,24 +54,24 @@ public class AudioOps(
      *  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.
@@ -94,27 +94,27 @@ public class AudioOps(
         windowSize: Long,
         stride: Long,
         magnitudeSquared: Boolean? = null
-    ): AudioSpectrogram = java.audioSpectrogram(
+    ): AudioSpectrogram = java.audioSpectrogram(    
         input,
         windowSize,
         stride,
         *listOfNotNull(
-            magnitudeSquared?.let { org.tensorflow.op.audio.AudioSpectrogram.magnitudeSquared(it) }
+            magnitudeSquared?.let{ org.tensorflow.op.audio.AudioSpectrogram.magnitudeSquared(it) }
         ).toTypedArray()
-    )
+        )
 
     /**
      * 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
@@ -137,21 +137,21 @@ public class AudioOps(
         contents: Operand<TString>,
         desiredChannels: Long? = null,
         desiredSamples: Long? = null
-    ): DecodeWav = java.decodeWav(
+    ): DecodeWav = java.decodeWav(    
         contents,
         *listOfNotNull(
-            desiredChannels?.let { org.tensorflow.op.audio.DecodeWav.desiredChannels(it) },
-            desiredSamples?.let { org.tensorflow.op.audio.DecodeWav.desiredSamples(it) }
+            desiredChannels?.let{ org.tensorflow.op.audio.DecodeWav.desiredChannels(it) },
+            desiredSamples?.let{ org.tensorflow.op.audio.DecodeWav.desiredSamples(it) }
         ).toTypedArray()
-    )
+        )
 
     /**
      * 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 `&#91;length, channels&#93;`.
      *  `sample_rate` is a scalar Tensor holding the rate to use (e.g. 44100).
      *
@@ -161,9 +161,9 @@ public class AudioOps(
      * @see org.tensorflow.op.AudioOps.encodeWav
      */
     public fun encodeWav(audio: Operand<TFloat32>, sampleRate: Operand<TInt32>): EncodeWav =
-        java.encodeWav(
-            audio,
-            sampleRate
+            java.encodeWav(    
+        audio,
+        sampleRate
         )
 
     /**
@@ -208,14 +208,14 @@ public class AudioOps(
         lowerFrequencyLimit: Float? = null,
         filterbankChannelCount: Long? = null,
         dctCoefficientCount: Long? = null
-    ): Mfcc = java.mfcc(
+    ): Mfcc = java.mfcc(    
         spectrogram,
         sampleRate,
         *listOfNotNull(
-            upperFrequencyLimit?.let { org.tensorflow.op.audio.Mfcc.upperFrequencyLimit(it) },
-            lowerFrequencyLimit?.let { org.tensorflow.op.audio.Mfcc.lowerFrequencyLimit(it) },
-            filterbankChannelCount?.let { org.tensorflow.op.audio.Mfcc.filterbankChannelCount(it) },
-            dctCoefficientCount?.let { org.tensorflow.op.audio.Mfcc.dctCoefficientCount(it) }
+            upperFrequencyLimit?.let{ org.tensorflow.op.audio.Mfcc.upperFrequencyLimit(it) },
+            lowerFrequencyLimit?.let{ org.tensorflow.op.audio.Mfcc.lowerFrequencyLimit(it) },
+            filterbankChannelCount?.let{ org.tensorflow.op.audio.Mfcc.filterbankChannelCount(it) },
+            dctCoefficientCount?.let{ org.tensorflow.op.audio.Mfcc.dctCoefficientCount(it) }
         ).toTypedArray()
-    )
+        )
 }

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

@@ -49,7 +49,7 @@ public class BitwiseOps(
      * Elementwise computes the bitwise AND of `x` and `y`.
      *  The result will have those bits set, that are set in both `x` and `y`. The
      *  computation is performed on the underlying representations of `x` and `y`.
-     *
+     *  
      * For example:
      *  ```
      * import tensorflow as tf
@@ -64,7 +64,7 @@ public class BitwiseOps(
      *
      *    res = bitwise_ops.bitwise_and(lhs, rhs)
      *    tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE
-     *
+     *  
      * ```
      *
      * @param <T> data type for `z` output
@@ -75,16 +75,16 @@ public class BitwiseOps(
      * @see org.tensorflow.op.BitwiseOps.bitwiseAnd
      */
     public fun <T : TNumber> bitwiseAnd(x: Operand<T>, y: Operand<T>): BitwiseAnd<T> =
-        java.bitwiseAnd<T>(
-            x,
-            y
+            java.bitwiseAnd<T>(    
+        x,
+        y
         )
 
     /**
      * Elementwise computes the bitwise OR of `x` and `y`.
      *  The result will have those bits set, that are set in `x`, `y` or both. The
      *  computation is performed on the underlying representations of `x` and `y`.
-     *
+     *  
      * For example:
      *  ```
      * import tensorflow as tf
@@ -99,7 +99,7 @@ public class BitwiseOps(
      *
      *    res = bitwise_ops.bitwise_or(lhs, rhs)
      *    tf.assert_equal(tf.cast(res,  tf.float32), exp)  # TRUE
-     *
+     *  
      * ```
      *
      * @param <T> data type for `z` output
@@ -110,16 +110,16 @@ public class BitwiseOps(
      * @see org.tensorflow.op.BitwiseOps.bitwiseOr
      */
     public fun <T : TNumber> bitwiseOr(x: Operand<T>, y: Operand<T>): BitwiseOr<T> =
-        java.bitwiseOr<T>(
-            x,
-            y
+            java.bitwiseOr<T>(    
+        x,
+        y
         )
 
     /**
      * Elementwise computes the bitwise XOR of `x` and `y`.
      *  The result will have those bits set, that are different in `x` and `y`. The
      *  computation is performed on the underlying representations of `x` and `y`.
-     *
+     *  
      * For example:
      *  ```
      * import tensorflow as tf
@@ -134,7 +134,7 @@ public class BitwiseOps(
      *
      *    res = bitwise_ops.bitwise_xor(lhs, rhs)
      *    tf.assert_equal(tf.cast(res, tf.float32), exp) # TRUE
-     *
+     *  
      * ```
      *
      * @param <T> data type for `z` output
@@ -145,9 +145,9 @@ public class BitwiseOps(
      * @see org.tensorflow.op.BitwiseOps.bitwiseXor
      */
     public fun <T : TNumber> bitwiseXor(x: Operand<T>, y: Operand<T>): BitwiseXor<T> =
-        java.bitwiseXor<T>(
-            x,
-            y
+            java.bitwiseXor<T>(    
+        x,
+        y
         )
 
     /**
@@ -156,7 +156,7 @@ public class BitwiseOps(
      *  Flip each bit of supported types.  For example, type `int8` (decimal 2) binary 00000010
      * becomes (decimal -3) binary 11111101.
      *  This operation is performed on each element of the tensor argument `x`.
-     *
+     *  
      * Example:
      *  ```
      * import tensorflow as tf
@@ -192,7 +192,7 @@ public class BitwiseOps(
      *      inverted = bitwise_ops.invert(input_tensor)
      *      expected = tf.constant([dtype.max - x for x in inputs], dtype=tf.float32)
      *      tf.assert_equal(tf.cast(inverted, tf.float32), tf.cast(expected, tf.float32))
-     *
+     *  
      * ```
      *
      * @param <T> data type for `y` output
@@ -201,15 +201,15 @@ public class BitwiseOps(
      * @return a new instance of Invert
      * @see org.tensorflow.op.BitwiseOps.invert
      */
-    public fun <T : TNumber> invert(x: Operand<T>): Invert<T> = java.invert<T>(
+    public fun <T : TNumber> invert(x: Operand<T>): Invert<T> = java.invert<T>(    
         x
-    )
+        )
 
     /**
      * Elementwise computes the bitwise left-shift of `x` and `y`.
      *  If `y` is negative, or greater than or equal to the width of `x` in bits the
      *  result is implementation defined.
-     *
+     *  
      * Example:
      *  ```
      * import tensorflow as tf
@@ -235,7 +235,7 @@ public class BitwiseOps(
      *  rhs = np.array([-1, -5, -3, -14], dtype=np.int8)
      *  bitwise_ops.left_shift(lhs, rhs)
      *  # <tf.Tensor: shape=(4,), dtype=int8, numpy=array([ -2,  64, 101,  32], dtype=int8)>
-     *
+     *  
      * ```
      *
      * @param <T> data type for `z` output
@@ -246,19 +246,19 @@ public class BitwiseOps(
      * @see org.tensorflow.op.BitwiseOps.leftShift
      */
     public fun <T : TNumber> leftShift(x: Operand<T>, y: Operand<T>): LeftShift<T> =
-        java.leftShift<T>(
-            x,
-            y
+            java.leftShift<T>(    
+        x,
+        y
         )
 
     /**
      * Elementwise computes the bitwise right-shift of `x` and `y`.
      *  Performs a logical shift for unsigned integer types, and an arithmetic shift
      *  for signed integer types.
-     *
+     *  
      * If `y` is negative, or greater than or equal to than the width of `x` in bits
      *  the result is implementation defined.
-     *
+     *  
      * Example:
      *  ```
      * import tensorflow as tf
@@ -284,7 +284,7 @@ public class BitwiseOps(
      *  rhs = np.array([-1, -5, -3, -14], dtype=np.int8)
      *  bitwise_ops.right_shift(lhs, rhs)
      *  # <tf.Tensor: shape=(4,), dtype=int8, numpy=array([ -2,  64, 101,  32], dtype=int8)>
-     *
+     *  
      * ```
      *
      * @param <T> data type for `z` output
@@ -295,8 +295,8 @@ public class BitwiseOps(
      * @see org.tensorflow.op.BitwiseOps.rightShift
      */
     public fun <T : TNumber> rightShift(x: Operand<T>, y: Operand<T>): RightShift<T> =
-        java.rightShift<T>(
-            x,
-            y
+            java.rightShift<T>(    
+        x,
+        y
         )
 }