Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 199351707

tensorflow/go/op/wrappers.go

@@ -21947,46 +21947,6 @@ func MatrixExponential(scope *Scope, input tf.Output) (output tf.Output) {
 	return op.Output(0)
 }
 
-// Computes the matrix logarithm of one or more square matrices:
-//
-//
-// log(exp(A)) = A
-//
-// This op is only defined for complex matrices. If A is positive-definite and
-// real, then casting to a complex matrix, taking the logarithm and casting back
-// to a real matrix will give the correct result.
-//
-// This function computes the matrix logarithm using the Schur-Parlett algorithm.
-// Details of the algorithm can be found in Section 11.6.2 of:
-// Nicholas J. Higham, Functions of Matrices: Theory and Computation, SIAM 2008.
-// ISBN 978-0-898716-46-7.
-//
-// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
-// form square matrices. The output is a tensor of the same shape as the input
-// containing the exponential for all input submatrices `[..., :, :]`.
-//
-// Arguments:
-//	input: Shape is `[..., M, M]`.
-//
-// Returns Shape is `[..., M, M]`.
-//
-// @compatibility(scipy)
-// Equivalent to scipy.linalg.logm
-// @end_compatibility
-func MatrixLogarithm(scope *Scope, input tf.Output) (output tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "MatrixLogarithm",
-		Input: []tf.Input{
-			input,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // QueueDequeueUpToV2Attr is an optional argument to QueueDequeueUpToV2.
 type QueueDequeueUpToV2Attr func(optionalAttr)
 
@@ -24398,6 +24358,46 @@ func NonMaxSuppressionV2(scope *Scope, boxes tf.Output, scores tf.Output, max_ou
 	return op.Output(0)
 }
 
+// Computes the matrix logarithm of one or more square matrices:
+//
+//
+// log(exp(A)) = A
+//
+// This op is only defined for complex matrices. If A is positive-definite and
+// real, then casting to a complex matrix, taking the logarithm and casting back
+// to a real matrix will give the correct result.
+//
+// This function computes the matrix logarithm using the Schur-Parlett algorithm.
+// Details of the algorithm can be found in Section 11.6.2 of:
+// Nicholas J. Higham, Functions of Matrices: Theory and Computation, SIAM 2008.
+// ISBN 978-0-898716-46-7.
+//
+// The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions
+// form square matrices. The output is a tensor of the same shape as the input
+// containing the exponential for all input submatrices `[..., :, :]`.
+//
+// Arguments:
+//	input: Shape is `[..., M, M]`.
+//
+// Returns Shape is `[..., M, M]`.
+//
+// @compatibility(scipy)
+// Equivalent to scipy.linalg.logm
+// @end_compatibility
+func MatrixLogarithm(scope *Scope, input tf.Output) (output tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "MatrixLogarithm",
+		Input: []tf.Input{
+			input,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // EncodeProtoAttr is an optional argument to EncodeProto.
 type EncodeProtoAttr func(optionalAttr)
 
@@ -29425,6 +29425,26 @@ func Snapshot(scope *Scope, input tf.Output) (output tf.Output) {
 	return op.Output(0)
 }
 
+// Returns a tensor of zeros with the same shape and type as x.
+//
+// Arguments:
+//	x: a tensor of type T.
+//
+// Returns a tensor of the same shape and type as x but filled with zeros.
+func ZerosLike(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "ZerosLike",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // AbortAttr is an optional argument to Abort.
 type AbortAttr func(optionalAttr)
 
@@ -30690,23 +30710,3 @@ func GuaranteeConst(scope *Scope, input tf.Output) (output tf.Output) {
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
-
-// Returns a tensor of zeros with the same shape and type as x.
-//
-// Arguments:
-//	x: a tensor of type T.
-//
-// Returns a tensor of the same shape and type as x but filled with zeros.
-func ZerosLike(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "ZerosLike",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}