From a55ed5bd3c429dfff20654ccbacec2521c1b20d2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 31 Aug 2018 07:45:54 -0700
Subject: Go: Update generated wrapper functions for TensorFlow ops.
 PiperOrigin-RevId: 211084266

---
 tensorflow/go/op/wrappers.go | 120 +++++++++++++++++++++----------------------
 1 file changed, 60 insertions(+), 60 deletions(-)

(limited to 'tensorflow/go')

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index a9747d4104..5ebd409b15 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -4674,51 +4674,6 @@ func CholeskyGrad(scope *Scope, l tf.Output, grad tf.Output) (output tf.Output)
 	return op.Output(0)
 }
 
-// Computes the mean along sparse segments of a tensor.
-//
-// Like `SparseSegmentMean`, but allows missing ids in `segment_ids`. If an id is
-// misisng, the `output` tensor at that position will be zeroed.
-//
-// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of
-// segments.
-//
-// Arguments:
-//
-//	indices: A 1-D tensor. Has same rank as `segment_ids`.
-//	segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
-//	num_segments: Should equal the number of distinct segment IDs.
-//
-// Returns Has same shape as data, except for dimension 0 which has size
-// `num_segments`.
-func SparseSegmentMeanWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "SparseSegmentMeanWithNumSegments",
-		Input: []tf.Input{
-			data, indices, segment_ids, num_segments,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Computes hyperbolic cosine of x element-wise.
-func Cosh(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Cosh",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Creates a dataset that emits each dim-0 slice of `components` once.
 func TensorSliceDataset(scope *Scope, components []tf.Output, output_shapes []tf.Shape) (handle tf.Output) {
 	if scope.Err() != nil {
@@ -8945,21 +8900,6 @@ func ReadVariableOp(scope *Scope, resource tf.Output, dtype tf.DataType) (value
 	return op.Output(0)
 }
 
-// Computes tan of x element-wise.
-func Tan(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Tan",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Updates the tree ensemble by either adding a layer to the last tree being grown
 //
 // or by starting a new tree.
@@ -9000,6 +8940,21 @@ func BoostedTreesUpdateEnsemble(scope *Scope, tree_ensemble_handle tf.Output, fe
 	return scope.AddOperation(opspec)
 }
 
+// Computes tan of x element-wise.
+func Tan(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Tan",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // EncodeJpegAttr is an optional argument to EncodeJpeg.
 type EncodeJpegAttr func(optionalAttr)
 
@@ -20421,6 +20376,51 @@ func RandomUniformInt(scope *Scope, shape tf.Output, minval tf.Output, maxval tf
 	return op.Output(0)
 }
 
+// Computes hyperbolic cosine of x element-wise.
+func Cosh(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Cosh",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Computes the mean along sparse segments of a tensor.
+//
+// Like `SparseSegmentMean`, but allows missing ids in `segment_ids`. If an id is
+// misisng, the `output` tensor at that position will be zeroed.
+//
+// Read @{$math_ops#Segmentation$the section on segmentation} for an explanation of
+// segments.
+//
+// Arguments:
+//
+//	indices: A 1-D tensor. Has same rank as `segment_ids`.
+//	segment_ids: A 1-D tensor. Values should be sorted and can be repeated.
+//	num_segments: Should equal the number of distinct segment IDs.
+//
+// Returns Has same shape as data, except for dimension 0 which has size
+// `num_segments`.
+func SparseSegmentMeanWithNumSegments(scope *Scope, data tf.Output, indices tf.Output, segment_ids tf.Output, num_segments tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "SparseSegmentMeanWithNumSegments",
+		Input: []tf.Input{
+			data, indices, segment_ids, num_segments,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // CudnnRNNParamsSizeAttr is an optional argument to CudnnRNNParamsSize.
 type CudnnRNNParamsSizeAttr func(optionalAttr)
 
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