Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 194337205

1 files changed, 119 insertions, 119 deletions

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 4d91f2b68e..83de1c5a92 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -2425,6 +2425,125 @@ func Gather(scope *Scope, params tf.Output, indices tf.Output, optional ...Gathe
 	return op.Output(0)
 }
 
+// Creates a tensor filled with a scalar value.
+//
+// This operation creates a tensor of shape `dims` and fills it with `value`.
+//
+// For example:
+//
+// ```
+// # Output tensor has shape [2, 3].
+// fill([2, 3], 9) ==> [[9, 9, 9]
+//                      [9, 9, 9]]
+// ```
+//
+// Arguments:
+//	dims: 1-D. Represents the shape of the output tensor.
+//	value: 0-D (scalar). Value to fill the returned tensor.
+//
+// @compatibility(numpy)
+// Equivalent to np.full
+// @end_compatibility
+func Fill(scope *Scope, dims tf.Output, value tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Fill",
+		Input: []tf.Input{
+			dims, value,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// EditDistanceAttr is an optional argument to EditDistance.
+type EditDistanceAttr func(optionalAttr)
+
+// EditDistanceNormalize sets the optional normalize attribute to value.
+//
+// value: boolean (if true, edit distances are normalized by length of truth).
+//
+// The output is:
+// If not specified, defaults to true
+func EditDistanceNormalize(value bool) EditDistanceAttr {
+	return func(m optionalAttr) {
+		m["normalize"] = value
+	}
+}
+
+// Computes the (possibly normalized) Levenshtein Edit Distance.
+//
+// The inputs are variable-length sequences provided by SparseTensors
+//   (hypothesis_indices, hypothesis_values, hypothesis_shape)
+// and
+//   (truth_indices, truth_values, truth_shape).
+//
+// The inputs are:
+//
+// Arguments:
+//	hypothesis_indices: The indices of the hypothesis list SparseTensor.
+// This is an N x R int64 matrix.
+//	hypothesis_values: The values of the hypothesis list SparseTensor.
+// This is an N-length vector.
+//	hypothesis_shape: The shape of the hypothesis list SparseTensor.
+// This is an R-length vector.
+//	truth_indices: The indices of the truth list SparseTensor.
+// This is an M x R int64 matrix.
+//	truth_values: The values of the truth list SparseTensor.
+// This is an M-length vector.
+//	truth_shape: truth indices, vector.
+//
+// Returns A dense float tensor with rank R - 1.
+//
+// For the example input:
+//
+//     // hypothesis represents a 2x1 matrix with variable-length values:
+//     //   (0,0) = ["a"]
+//     //   (1,0) = ["b"]
+//     hypothesis_indices = [[0, 0, 0],
+//                           [1, 0, 0]]
+//     hypothesis_values = ["a", "b"]
+//     hypothesis_shape = [2, 1, 1]
+//
+//     // truth represents a 2x2 matrix with variable-length values:
+//     //   (0,0) = []
+//     //   (0,1) = ["a"]
+//     //   (1,0) = ["b", "c"]
+//     //   (1,1) = ["a"]
+//     truth_indices = [[0, 1, 0],
+//                      [1, 0, 0],
+//                      [1, 0, 1],
+//                      [1, 1, 0]]
+//     truth_values = ["a", "b", "c", "a"]
+//     truth_shape = [2, 2, 2]
+//     normalize = true
+//
+// The output will be:
+//
+//     // output is a 2x2 matrix with edit distances normalized by truth lengths.
+//     output = [[inf, 1.0],  // (0,0): no truth, (0,1): no hypothesis
+//               [0.5, 1.0]]  // (1,0): addition, (1,1): no hypothesis
+func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values tf.Output, hypothesis_shape tf.Output, truth_indices tf.Output, truth_values tf.Output, truth_shape tf.Output, optional ...EditDistanceAttr) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "EditDistance",
+		Input: []tf.Input{
+			hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Clips tensor values to a specified min and max.
 //
 // Given a tensor `t`, this operation returns a tensor of the same type and
@@ -13001,39 +13120,6 @@ func IFFT2D(scope *Scope, input tf.Output) (output tf.Output) {
 	return op.Output(0)
 }
 
-// Creates a tensor filled with a scalar value.
-//
-// This operation creates a tensor of shape `dims` and fills it with `value`.
-//
-// For example:
-//
-// ```
-// # Output tensor has shape [2, 3].
-// fill([2, 3], 9) ==> [[9, 9, 9]
-//                      [9, 9, 9]]
-// ```
-//
-// Arguments:
-//	dims: 1-D. Represents the shape of the output tensor.
-//	value: 0-D (scalar). Value to fill the returned tensor.
-//
-// @compatibility(numpy)
-// Equivalent to np.full
-// @end_compatibility
-func Fill(scope *Scope, dims tf.Output, value tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Fill",
-		Input: []tf.Input{
-			dims, value,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // 2D fast Fourier transform.
 //
 // Computes the 2-dimensional discrete Fourier transform over the inner-most
@@ -30624,89 +30710,3 @@ func MatrixSetDiag(scope *Scope, input tf.Output, diagonal tf.Output) (output tf
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
-
-// EditDistanceAttr is an optional argument to EditDistance.
-type EditDistanceAttr func(optionalAttr)
-
-// EditDistanceNormalize sets the optional normalize attribute to value.
-//
-// value: boolean (if true, edit distances are normalized by length of truth).
-//
-// The output is:
-// If not specified, defaults to true
-func EditDistanceNormalize(value bool) EditDistanceAttr {
-	return func(m optionalAttr) {
-		m["normalize"] = value
-	}
-}
-
-// Computes the (possibly normalized) Levenshtein Edit Distance.
-//
-// The inputs are variable-length sequences provided by SparseTensors
-//   (hypothesis_indices, hypothesis_values, hypothesis_shape)
-// and
-//   (truth_indices, truth_values, truth_shape).
-//
-// The inputs are:
-//
-// Arguments:
-//	hypothesis_indices: The indices of the hypothesis list SparseTensor.
-// This is an N x R int64 matrix.
-//	hypothesis_values: The values of the hypothesis list SparseTensor.
-// This is an N-length vector.
-//	hypothesis_shape: The shape of the hypothesis list SparseTensor.
-// This is an R-length vector.
-//	truth_indices: The indices of the truth list SparseTensor.
-// This is an M x R int64 matrix.
-//	truth_values: The values of the truth list SparseTensor.
-// This is an M-length vector.
-//	truth_shape: truth indices, vector.
-//
-// Returns A dense float tensor with rank R - 1.
-//
-// For the example input:
-//
-//     // hypothesis represents a 2x1 matrix with variable-length values:
-//     //   (0,0) = ["a"]
-//     //   (1,0) = ["b"]
-//     hypothesis_indices = [[0, 0, 0],
-//                           [1, 0, 0]]
-//     hypothesis_values = ["a", "b"]
-//     hypothesis_shape = [2, 1, 1]
-//
-//     // truth represents a 2x2 matrix with variable-length values:
-//     //   (0,0) = []
-//     //   (0,1) = ["a"]
-//     //   (1,0) = ["b", "c"]
-//     //   (1,1) = ["a"]
-//     truth_indices = [[0, 1, 0],
-//                      [1, 0, 0],
-//                      [1, 0, 1],
-//                      [1, 1, 0]]
-//     truth_values = ["a", "b", "c", "a"]
-//     truth_shape = [2, 2, 2]
-//     normalize = true
-//
-// The output will be:
-//
-//     // output is a 2x2 matrix with edit distances normalized by truth lengths.
-//     output = [[inf, 1.0],  // (0,0): no truth, (0,1): no hypothesis
-//               [0.5, 1.0]]  // (1,0): addition, (1,1): no hypothesis
-func EditDistance(scope *Scope, hypothesis_indices tf.Output, hypothesis_values tf.Output, hypothesis_shape tf.Output, truth_indices tf.Output, truth_values tf.Output, truth_shape tf.Output, optional ...EditDistanceAttr) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "EditDistance",
-		Input: []tf.Input{
-			hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}