Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 186346967

1 files changed, 51 insertions, 51 deletions

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 13f38dfb32..3f742091f5 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -278,6 +278,57 @@ func FakeQuantWithMinMaxVarsPerChannelGradient(scope *Scope, gradients tf.Output
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
+// FakeQuantWithMinMaxVarsPerChannelAttr is an optional argument to FakeQuantWithMinMaxVarsPerChannel.
+type FakeQuantWithMinMaxVarsPerChannelAttr func(optionalAttr)
+
+// FakeQuantWithMinMaxVarsPerChannelNumBits sets the optional num_bits attribute to value.
+// If not specified, defaults to 8
+func FakeQuantWithMinMaxVarsPerChannelNumBits(value int64) FakeQuantWithMinMaxVarsPerChannelAttr {
+	return func(m optionalAttr) {
+		m["num_bits"] = value
+	}
+}
+
+// FakeQuantWithMinMaxVarsPerChannelNarrowRange sets the optional narrow_range attribute to value.
+// If not specified, defaults to false
+func FakeQuantWithMinMaxVarsPerChannelNarrowRange(value bool) FakeQuantWithMinMaxVarsPerChannelAttr {
+	return func(m optionalAttr) {
+		m["narrow_range"] = value
+	}
+}
+
+// Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`,
+//
+// `[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` of shape `[d]`
+// to 'outputs' tensor of same shape as `inputs`.
+//
+// `[min; max]` define the clamping range for the `inputs` data.
+// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]`
+// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and
+// then de-quantized and output as floats in `[min; max]` interval.
+// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive.
+//
+// This operation has a gradient and thus allows for training `min` and `max`
+// values.
+func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsPerChannelAttr) (outputs tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "FakeQuantWithMinMaxVarsPerChannel",
+		Input: []tf.Input{
+			inputs, min, max,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Partitions `data` into `num_partitions` tensors using indices from `partitions`.
 //
 // For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]`
@@ -15312,57 +15363,6 @@ func TruncatedNormal(scope *Scope, shape tf.Output, dtype tf.DataType, optional
 	return op.Output(0)
 }
 
-// FakeQuantWithMinMaxVarsPerChannelAttr is an optional argument to FakeQuantWithMinMaxVarsPerChannel.
-type FakeQuantWithMinMaxVarsPerChannelAttr func(optionalAttr)
-
-// FakeQuantWithMinMaxVarsPerChannelNumBits sets the optional num_bits attribute to value.
-// If not specified, defaults to 8
-func FakeQuantWithMinMaxVarsPerChannelNumBits(value int64) FakeQuantWithMinMaxVarsPerChannelAttr {
-	return func(m optionalAttr) {
-		m["num_bits"] = value
-	}
-}
-
-// FakeQuantWithMinMaxVarsPerChannelNarrowRange sets the optional narrow_range attribute to value.
-// If not specified, defaults to false
-func FakeQuantWithMinMaxVarsPerChannelNarrowRange(value bool) FakeQuantWithMinMaxVarsPerChannelAttr {
-	return func(m optionalAttr) {
-		m["narrow_range"] = value
-	}
-}
-
-// Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`,
-//
-// `[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` of shape `[d]`
-// to 'outputs' tensor of same shape as `inputs`.
-//
-// `[min; max]` define the clamping range for the `inputs` data.
-// `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]`
-// when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and
-// then de-quantized and output as floats in `[min; max]` interval.
-// `num_bits` is the bitwidth of the quantization; between 2 and 8, inclusive.
-//
-// This operation has a gradient and thus allows for training `min` and `max`
-// values.
-func FakeQuantWithMinMaxVarsPerChannel(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsPerChannelAttr) (outputs tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "FakeQuantWithMinMaxVarsPerChannel",
-		Input: []tf.Input{
-			inputs, min, max,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // RandomShuffleAttr is an optional argument to RandomShuffle.
 type RandomShuffleAttr func(optionalAttr)