Regenerate files for TensorFlow 2.5.0

Author: saudet

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

@@ -55,16 +55,13 @@
  * @see {@link Ops}
  */
 public final class DataOps {
-  public final DataExperimentalOps experimental;
-
   private final Scope scope;
 
   private final Ops ops;
 
   DataOps(Ops ops) {
     this.scope = ops.scope();
     this.ops = ops;
-    experimental = new DataExperimentalOps(ops);
   }
 
   /**

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

@@ -155,7 +155,9 @@ public <T extends TNumber> AdjustSaturation<T> adjustSaturation(Operand<T> image
    *  representing a single score corresponding to each box (each row of boxes).
    * @param maxOutputSizePerClass A scalar integer tensor representing the maximum number of
    *  boxes to be selected by non max suppression per class
-   * @param maxTotalSize A scalar representing maximum number of boxes retained over all classes.
+   * @param maxTotalSize An int32 scalar representing the maximum number of boxes retained over all
+   *  classes. Note that setting this value to a large number may result in OOM error
+   *  depending on the system workload.
    * @param iouThreshold A 0-D float tensor representing the threshold for deciding whether
    *  boxes overlap too much with respect to IOU.
    * @param scoreThreshold A 0-D float tensor representing the threshold for deciding when to remove

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

@@ -138,12 +138,13 @@ public DecodeCsv decodeCsv(Operand<TString> records, Iterable<Operand<?>> record
 
   /**
    * Convert JSON-encoded Example records to binary protocol buffer strings.
-   *  This op translates a tensor containing Example records, encoded using
-   *  the  <a href="https://developers.google.com/protocol-buffers/docs/proto3#json">standard JSON
-   *  mapping</a> ,
-   *  into a tensor containing the same records encoded as binary protocol
-   *  buffers. The resulting tensor can then be fed to any of the other
-   *  Example-parsing ops.
+   *  Note: This is <strong>not</strong> a general purpose JSON parsing op.
+   *  <p>This op converts JSON-serialized
+   *  {@code tf.train.Example} (created with {@code json_format.MessageToJson}, following the
+   *   <a href="https://developers.google.com/protocol-buffers/docs/proto3#json">standard JSON mapping</a> )
+   *  to a binary-serialized {@code tf.train.Example} (equivalent to
+   *  {@code Example.SerializeToString()}) suitable for conversion to tensors with
+   *  {@code tf.io.parse_example}.
    *
    * @param jsonExamples Each string is a JSON object serialized according to the JSON
    *  mapping of the Example proto.

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

@@ -95,25 +95,25 @@ public final class LinalgOps {
    *  <p>For example:
    *  <pre>
    *  # if 'input' is [[ 0,  1,  2, 3]
-   *                   [-1,  0,  1, 2]
-   *                   [-2, -1,  0, 1]
-   *                   [-3, -2, -1, 0]],
+   *  #                [-1,  0,  1, 2]
+   *  #                [-2, -1,  0, 1]
+   *  #                [-3, -2, -1, 0]],
    *
-   *  tf.matrix_band_part(input, 1, -1) ==&gt; [[ 0,  1,  2, 3]
+   *  tf.linalg.band_part(input, 1, -1) ==&gt; [[ 0,  1,  2, 3]
    *                                         [-1,  0,  1, 2]
    *                                         [ 0, -1,  0, 1]
    *                                         [ 0,  0, -1, 0]],
    *
-   *  tf.matrix_band_part(input, 2, 1) ==&gt; [[ 0,  1,  0, 0]
+   *  tf.linalg.band_part(input, 2, 1) ==&gt; [[ 0,  1,  0, 0]
    *                                        [-1,  0,  1, 0]
    *                                        [-2, -1,  0, 1]
    *                                        [ 0, -2, -1, 0]]
    *  </pre>
    *  <p>Useful special cases:
    *  <pre>
-   *   tf.matrix_band_part(input, 0, -1) ==&gt; Upper triangular part.
-   *   tf.matrix_band_part(input, -1, 0) ==&gt; Lower triangular part.
-   *   tf.matrix_band_part(input, 0, 0) ==&gt; Diagonal.
+   *   tf.linalg.band_part(input, 0, -1) ==&gt; Upper triangular part.
+   *   tf.linalg.band_part(input, -1, 0) ==&gt; Lower triangular part.
+   *   tf.linalg.band_part(input, 0, 0) ==&gt; Diagonal.
    *  </pre>
    *
    * @param <T> data type for {@code band} output

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

@@ -1960,7 +1960,7 @@ public <T extends TType> Sinh<T> sinh(Operand<T> x) {
   }
 
   /**
-   * Computes softplus: {@code log(exp(features) + 1)}.
+   * The Softplus operation
    *
    * @param <T> data type for {@code activations} output
    * @param features the features value

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

@@ -954,8 +954,26 @@ public <T extends TNumber> Dilation2dBackpropInput<T> dilation2dBackpropInput(Op
   }
 
   /**
-   * Computes exponential linear: {@code exp(features) - 1} if &lt; 0, {@code features} otherwise.
-   *  See  <a href="http://arxiv.org/abs/1511.07289">Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
+   * Computes the exponential linear function.
+   *  The ELU function is defined as:
+   *  <ul>
+   *  <li>$ e ^ x - 1 $ if $ x &lt; 0 $</li>
+   *  <li>$ x $ if $ x &gt;= 0 $</li>
+   *  </ul>
+   *  <p>Examples:
+   *  <blockquote>
+   *  <blockquote>
+   *  <blockquote>
+   *  <p>tf.nn.elu(1.0)
+   *  &lt;tf.Tensor: shape=(), dtype=float32, numpy=1.0&gt;
+   *  tf.nn.elu(0.0)
+   *  &lt;tf.Tensor: shape=(), dtype=float32, numpy=0.0&gt;
+   *  tf.nn.elu(-1000.0)
+   *  &lt;tf.Tensor: shape=(), dtype=float32, numpy=-1.0&gt;
+   *  </blockquote>
+   *  </blockquote>
+   *  </blockquote>
+   *  <p>See  <a href="http://arxiv.org/abs/1511.07289">Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
    *  </a>
    *
    * @param <T> data type for {@code activations} output
@@ -1753,8 +1771,8 @@ public <U extends TNumber> QuantizedReluX<U> quantizedReluX(Operand<? extends TN
    *  <blockquote>
    *  <blockquote>
    *  <blockquote>
-   *  <p>tf.nn.relu([-2., 0., -0., 3.]).numpy()
-   *  array([ 0.,  0., -0.,  3.], dtype=float32)
+   *  <p>tf.nn.relu([-2., 0., 3.]).numpy()
+   *  array([0., 0., 3.], dtype=float32)
    *  </blockquote>
    *  </blockquote>
    *  </blockquote>