Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 216224026

1 files changed, 114 insertions, 114 deletions

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 5d17605e37..fe99915a6c 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -7221,6 +7221,45 @@ func MultiDeviceIteratorGetNextFromShard(scope *Scope, multi_device_iterator tf.
 	return components
 }
 
+// Deprecated. Use TensorArrayGradV3
+//
+// DEPRECATED at GraphDef version 26: Use TensorArrayWriteV3
+func TensorArrayWriteV2(scope *Scope, handle tf.Output, index tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "TensorArrayWriteV2",
+		Input: []tf.Input{
+			handle, index, value, flow_in,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Writes the given dataset to the given file using the TFRecord format.
+//
+// Arguments:
+//	input_dataset: A variant tensor representing the dataset to write.
+//	filename: A scalar string tensor representing the filename to use.
+//	compression_type: A scalar string tensor containing either (i) the empty string (no
+// compression), (ii) "ZLIB", or (iii) "GZIP".
+//
+// Returns the created operation.
+func DatasetToTFRecord(scope *Scope, input_dataset tf.Output, filename tf.Output, compression_type tf.Output) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "DatasetToTFRecord",
+		Input: []tf.Input{
+			input_dataset, filename, compression_type,
+		},
+	}
+	return scope.AddOperation(opspec)
+}
+
 // Computes rectified linear 6: `min(max(features, 0), 6)`.
 func Relu6(scope *Scope, features tf.Output) (activations tf.Output) {
 	if scope.Err() != nil {
@@ -8251,44 +8290,6 @@ func BiasAddGrad(scope *Scope, out_backprop tf.Output, optional ...BiasAddGradAt
 	return op.Output(0)
 }
 
-// Bucketizes 'input' based on 'boundaries'.
-//
-// For example, if the inputs are
-//     boundaries = [0, 10, 100]
-//     input = [[-5, 10000]
-//              [150,   10]
-//              [5,    100]]
-//
-// then the output will be
-//     output = [[0, 3]
-//               [3, 2]
-//               [1, 3]]
-//
-// Arguments:
-//	input: Any shape of Tensor contains with int or float type.
-//	boundaries: A sorted list of floats gives the boundary of the buckets.
-//
-// Returns Same shape with 'input', each value of input replaced with bucket index.
-//
-// @compatibility(numpy)
-// Equivalent to np.digitize.
-// @end_compatibility
-func Bucketize(scope *Scope, input tf.Output, boundaries []float32) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"boundaries": boundaries}
-	opspec := tf.OpSpec{
-		Type: "Bucketize",
-		Input: []tf.Input{
-			input,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // FusedBatchNormV2Attr is an optional argument to FusedBatchNormV2.
 type FusedBatchNormV2Attr func(optionalAttr)
 
@@ -10980,6 +10981,44 @@ func Tan(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
+// Bucketizes 'input' based on 'boundaries'.
+//
+// For example, if the inputs are
+//     boundaries = [0, 10, 100]
+//     input = [[-5, 10000]
+//              [150,   10]
+//              [5,    100]]
+//
+// then the output will be
+//     output = [[0, 3]
+//               [3, 2]
+//               [1, 3]]
+//
+// Arguments:
+//	input: Any shape of Tensor contains with int or float type.
+//	boundaries: A sorted list of floats gives the boundary of the buckets.
+//
+// Returns Same shape with 'input', each value of input replaced with bucket index.
+//
+// @compatibility(numpy)
+// Equivalent to np.digitize.
+// @end_compatibility
+func Bucketize(scope *Scope, input tf.Output, boundaries []float32) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"boundaries": boundaries}
+	opspec := tf.OpSpec{
+		Type: "Bucketize",
+		Input: []tf.Input{
+			input,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // EncodeJpegAttr is an optional argument to EncodeJpeg.
 type EncodeJpegAttr func(optionalAttr)
 
@@ -21413,43 +21452,6 @@ func QuantizedResizeBilinear(scope *Scope, images tf.Output, size tf.Output, min
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// Computes the minimum along segments of a tensor.
-//
-// Read
-// [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
-// for an explanation of segments.
-//
-// Computes a tensor such that
-// \\(output_i = \min_j(data_j)\\) where `min` is over `j` such
-// that `segment_ids[j] == i`.
-//
-// If the min is empty for a given segment ID `i`, `output[i] = 0`.
-//
-// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
-// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMin.png" alt>
-// </div>
-//
-// Arguments:
-//
-//	segment_ids: A 1-D tensor whose size is equal to the size of `data`'s
-// first dimension.  Values should be sorted and can be repeated.
-//
-// Returns Has same shape as data, except for dimension 0 which
-// has size `k`, the number of segments.
-func SegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "SegmentMin",
-		Input: []tf.Input{
-			data, segment_ids,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // SdcaOptimizerAttr is an optional argument to SdcaOptimizer.
 type SdcaOptimizerAttr func(optionalAttr)
 
@@ -21924,6 +21926,43 @@ func QuantizeDownAndShrinkRange(scope *Scope, input tf.Output, input_min tf.Outp
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
+// Computes the minimum along segments of a tensor.
+//
+// Read
+// [the section on segmentation](https://tensorflow.org/api_guides/python/math_ops#Segmentation)
+// for an explanation of segments.
+//
+// Computes a tensor such that
+// \\(output_i = \min_j(data_j)\\) where `min` is over `j` such
+// that `segment_ids[j] == i`.
+//
+// If the min is empty for a given segment ID `i`, `output[i] = 0`.
+//
+// <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
+// <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMin.png" alt>
+// </div>
+//
+// Arguments:
+//
+//	segment_ids: A 1-D tensor whose size is equal to the size of `data`'s
+// first dimension.  Values should be sorted and can be repeated.
+//
+// Returns Has same shape as data, except for dimension 0 which
+// has size `k`, the number of segments.
+func SegmentMin(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "SegmentMin",
+		Input: []tf.Input{
+			data, segment_ids,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes the sum along segments of a tensor.
 //
 // Read
@@ -29878,28 +29917,6 @@ func Cross(scope *Scope, a tf.Output, b tf.Output) (product tf.Output) {
 	return op.Output(0)
 }
 
-// Writes the given dataset to the given file using the TFRecord format.
-//
-// Arguments:
-//	input_dataset: A variant tensor representing the dataset to write.
-//	filename: A scalar string tensor representing the filename to use.
-//	compression_type: A scalar string tensor containing either (i) the empty string (no
-// compression), (ii) "ZLIB", or (iii) "GZIP".
-//
-// Returns the created operation.
-func DatasetToTFRecord(scope *Scope, input_dataset tf.Output, filename tf.Output, compression_type tf.Output) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "DatasetToTFRecord",
-		Input: []tf.Input{
-			input_dataset, filename, compression_type,
-		},
-	}
-	return scope.AddOperation(opspec)
-}
-
 // AvgPool3DAttr is an optional argument to AvgPool3D.
 type AvgPool3DAttr func(optionalAttr)
 
@@ -31692,23 +31709,6 @@ func TensorArraySizeV3(scope *Scope, handle tf.Output, flow_in tf.Output) (size
 	return op.Output(0)
 }
 
-// Deprecated. Use TensorArrayGradV3
-//
-// DEPRECATED at GraphDef version 26: Use TensorArrayWriteV3
-func TensorArrayWriteV2(scope *Scope, handle tf.Output, index tf.Output, value tf.Output, flow_in tf.Output) (flow_out tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "TensorArrayWriteV2",
-		Input: []tf.Input{
-			handle, index, value, flow_in,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // SparseReduceMaxAttr is an optional argument to SparseReduceMax.
 type SparseReduceMaxAttr func(optionalAttr)