Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 201273382

1 files changed, 163 insertions, 163 deletions

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index bff2264c29..b2dbdafc5f 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -3015,6 +3015,36 @@ func Concat(scope *Scope, concat_dim tf.Output, values []tf.Output) (output tf.O
 	return op.Output(0)
 }
 
+// Converts a flat index or array of flat indices into a tuple of
+//
+// coordinate arrays.
+//
+// @compatibility(numpy)
+// Equivalent to np.unravel_index
+// @end_compatibility
+//
+// Arguments:
+//	indices: An 0-D or 1-D `int` Tensor whose elements are indices into the
+// flattened version of an array of dimensions dims.
+//	dims: An 1-D `int` Tensor. The shape of the array to use for unraveling
+// indices.
+//
+// Returns An 2-D (or 1-D if indices is 0-D) tensor where each row has the
+// same shape as the indices array.
+func UnravelIndex(scope *Scope, indices tf.Output, dims tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "UnravelIndex",
+		Input: []tf.Input{
+			indices, dims,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes gradients for SparseSegmentSqrtN.
 //
 // Returns tensor "output" with same shape as grad, except for dimension 0 whose
@@ -3914,24 +3944,6 @@ func AddV2(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 	return op.Output(0)
 }
 
-// Returns x + y element-wise.
-//
-// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Add",
-		Input: []tf.Input{
-			x, y,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // NthElementAttr is an optional argument to NthElement.
 type NthElementAttr func(optionalAttr)
 
@@ -4675,6 +4687,24 @@ func MatrixInverse(scope *Scope, input tf.Output, optional ...MatrixInverseAttr)
 	return op.Output(0)
 }
 
+// Returns x + y element-wise.
+//
+// *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Add",
+		Input: []tf.Input{
+			x, y,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Computes square of x element-wise.
 //
 // I.e., \\(y = x * x = x^2\\).
@@ -7780,121 +7810,6 @@ func StringJoin(scope *Scope, inputs []tf.Output, optional ...StringJoinAttr) (o
 	return op.Output(0)
 }
 
-// LRNGradAttr is an optional argument to LRNGrad.
-type LRNGradAttr func(optionalAttr)
-
-// LRNGradDepthRadius sets the optional depth_radius attribute to value.
-//
-// value: A depth radius.
-// If not specified, defaults to 5
-func LRNGradDepthRadius(value int64) LRNGradAttr {
-	return func(m optionalAttr) {
-		m["depth_radius"] = value
-	}
-}
-
-// LRNGradBias sets the optional bias attribute to value.
-//
-// value: An offset (usually > 0 to avoid dividing by 0).
-// If not specified, defaults to 1
-func LRNGradBias(value float32) LRNGradAttr {
-	return func(m optionalAttr) {
-		m["bias"] = value
-	}
-}
-
-// LRNGradAlpha sets the optional alpha attribute to value.
-//
-// value: A scale factor, usually positive.
-// If not specified, defaults to 1
-func LRNGradAlpha(value float32) LRNGradAttr {
-	return func(m optionalAttr) {
-		m["alpha"] = value
-	}
-}
-
-// LRNGradBeta sets the optional beta attribute to value.
-//
-// value: An exponent.
-// If not specified, defaults to 0.5
-func LRNGradBeta(value float32) LRNGradAttr {
-	return func(m optionalAttr) {
-		m["beta"] = value
-	}
-}
-
-// Gradients for Local Response Normalization.
-//
-// Arguments:
-//	input_grads: 4-D with shape `[batch, height, width, channels]`.
-//	input_image: 4-D with shape `[batch, height, width, channels]`.
-//	output_image: 4-D with shape `[batch, height, width, channels]`.
-//
-// Returns The gradients for LRN.
-func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "LRNGrad",
-		Input: []tf.Input{
-			input_grads, input_image, output_image,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// AnyAttr is an optional argument to Any.
-type AnyAttr func(optionalAttr)
-
-// AnyKeepDims sets the optional keep_dims attribute to value.
-//
-// value: If true, retain reduced dimensions with length 1.
-// If not specified, defaults to false
-func AnyKeepDims(value bool) AnyAttr {
-	return func(m optionalAttr) {
-		m["keep_dims"] = value
-	}
-}
-
-// Computes the "logical or" of elements across dimensions of a tensor.
-//
-// Reduces `input` along the dimensions given in `axis`. Unless
-// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
-// `axis`. If `keep_dims` is true, the reduced dimensions are
-// retained with length 1.
-//
-// Arguments:
-//	input: The tensor to reduce.
-//	axis: The dimensions to reduce. Must be in the range
-// `[-rank(input), rank(input))`.
-//
-// Returns The reduced tensor.
-func Any(scope *Scope, input tf.Output, axis tf.Output, optional ...AnyAttr) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "Any",
-		Input: []tf.Input{
-			input, axis,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // ResourceApplyFtrlAttr is an optional argument to ResourceApplyFtrl.
 type ResourceApplyFtrlAttr func(optionalAttr)
 
@@ -19406,6 +19321,121 @@ func OrderedMapIncompleteSize(scope *Scope, dtypes []tf.DataType, optional ...Or
 	return op.Output(0)
 }
 
+// LRNGradAttr is an optional argument to LRNGrad.
+type LRNGradAttr func(optionalAttr)
+
+// LRNGradDepthRadius sets the optional depth_radius attribute to value.
+//
+// value: A depth radius.
+// If not specified, defaults to 5
+func LRNGradDepthRadius(value int64) LRNGradAttr {
+	return func(m optionalAttr) {
+		m["depth_radius"] = value
+	}
+}
+
+// LRNGradBias sets the optional bias attribute to value.
+//
+// value: An offset (usually > 0 to avoid dividing by 0).
+// If not specified, defaults to 1
+func LRNGradBias(value float32) LRNGradAttr {
+	return func(m optionalAttr) {
+		m["bias"] = value
+	}
+}
+
+// LRNGradAlpha sets the optional alpha attribute to value.
+//
+// value: A scale factor, usually positive.
+// If not specified, defaults to 1
+func LRNGradAlpha(value float32) LRNGradAttr {
+	return func(m optionalAttr) {
+		m["alpha"] = value
+	}
+}
+
+// LRNGradBeta sets the optional beta attribute to value.
+//
+// value: An exponent.
+// If not specified, defaults to 0.5
+func LRNGradBeta(value float32) LRNGradAttr {
+	return func(m optionalAttr) {
+		m["beta"] = value
+	}
+}
+
+// Gradients for Local Response Normalization.
+//
+// Arguments:
+//	input_grads: 4-D with shape `[batch, height, width, channels]`.
+//	input_image: 4-D with shape `[batch, height, width, channels]`.
+//	output_image: 4-D with shape `[batch, height, width, channels]`.
+//
+// Returns The gradients for LRN.
+func LRNGrad(scope *Scope, input_grads tf.Output, input_image tf.Output, output_image tf.Output, optional ...LRNGradAttr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "LRNGrad",
+		Input: []tf.Input{
+			input_grads, input_image, output_image,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// AnyAttr is an optional argument to Any.
+type AnyAttr func(optionalAttr)
+
+// AnyKeepDims sets the optional keep_dims attribute to value.
+//
+// value: If true, retain reduced dimensions with length 1.
+// If not specified, defaults to false
+func AnyKeepDims(value bool) AnyAttr {
+	return func(m optionalAttr) {
+		m["keep_dims"] = value
+	}
+}
+
+// Computes the "logical or" of elements across dimensions of a tensor.
+//
+// Reduces `input` along the dimensions given in `axis`. Unless
+// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+// `axis`. If `keep_dims` is true, the reduced dimensions are
+// retained with length 1.
+//
+// Arguments:
+//	input: The tensor to reduce.
+//	axis: The dimensions to reduce. Must be in the range
+// `[-rank(input), rank(input))`.
+//
+// Returns The reduced tensor.
+func Any(scope *Scope, input tf.Output, axis tf.Output, optional ...AnyAttr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "Any",
+		Input: []tf.Input{
+			input, axis,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Creates a sequence of numbers.
 //
 // This operation creates a sequence of numbers that begins at `start` and
@@ -30680,33 +30710,3 @@ func InplaceSub(scope *Scope, x tf.Output, i tf.Output, v tf.Output) (y tf.Outpu
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
-
-// Converts a flat index or array of flat indices into a tuple of
-//
-// coordinate arrays.
-//
-// @compatibility(numpy)
-// Equivalent to np.unravel_index
-// @end_compatibility
-//
-// Arguments:
-//	indices: An 0-D or 1-D `int` Tensor whose elements are indices into the
-// flattened version of an array of dimensions dims.
-//	dims: An 1-D `int` Tensor. The shape of the array to use for unraveling
-// indices.
-//
-// Returns An 2-D (or 1-D if indices is 0-D) tensor where each row has the
-// same shape as the indices array.
-func UnravelIndex(scope *Scope, indices tf.Output, dims tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "UnravelIndex",
-		Input: []tf.Input{
-			indices, dims,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}