Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 186346967

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 13f38df..3f74209 100644 (file)
--- 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)