Go: Update generated wrapper functions for TensorFlow ops.
Change: 148820986
diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 7cc11b6..93bd12c 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -601,8 +601,6 @@
type QuantizeAndDequantizeAttr func(optionalAttr)
// QuantizeAndDequantizeSignedInput sets the optional signed_input attribute to value.
-//
-// value: If the quantization is signed or unsigned.
// If not specified, defaults to b:true
func QuantizeAndDequantizeSignedInput(value bool) QuantizeAndDequantizeAttr {
return func(m optionalAttr) {
@@ -611,8 +609,6 @@
}
// QuantizeAndDequantizeNumBits sets the optional num_bits attribute to value.
-//
-// value: The bitwidth of the quantization.
// If not specified, defaults to i:8
func QuantizeAndDequantizeNumBits(value int64) QuantizeAndDequantizeAttr {
return func(m optionalAttr) {
@@ -621,8 +617,6 @@
}
// QuantizeAndDequantizeRangeGiven sets the optional range_given attribute to value.
-//
-// value: If the range is given or should be computed from the tensor.
// If not specified, defaults to b:false
func QuantizeAndDequantizeRangeGiven(value bool) QuantizeAndDequantizeAttr {
return func(m optionalAttr) {
@@ -631,8 +625,6 @@
}
// QuantizeAndDequantizeInputMin sets the optional input_min attribute to value.
-//
-// value: If range is given, this is the min of the range.
// If not specified, defaults to f:0
func QuantizeAndDequantizeInputMin(value float32) QuantizeAndDequantizeAttr {
return func(m optionalAttr) {
@@ -641,8 +633,6 @@
}
// QuantizeAndDequantizeInputMax sets the optional input_max attribute to value.
-//
-// value: If range is given, this is the max of the range.
// If not specified, defaults to f:0
func QuantizeAndDequantizeInputMax(value float32) QuantizeAndDequantizeAttr {
return func(m optionalAttr) {
@@ -650,62 +640,9 @@
}
}
-// Quantizes then dequantizes a tensor.
+// Use QuantizeAndDequantizeV2 instead.
//
-// DEPRECATED at GraphDef version 21: Replaced by QuantizeAndDequantizeV2
-//
-// This op simulates the precision loss from the quantized forward pass by:
-// 1. Quantizing the tensor to fixed point numbers, which should match the target
-// quantization method when it is used in inference.
-// 2. Dequantizing it back to floating point numbers for the following ops, most
-// likely matmul.
-//
-// There are different ways to quantize. This version does not use the full range
-// of the output type, choosing to elide the lowest possible value for symmetry
-// (e.g., output range is -127 to 127, not -128 to 127 for signed 8 bit
-// quantization), so that 0.0 maps to 0.
-//
-// To perform this op, we first find the range of values in our tensor. The range
-// we use is always centered on 0, so we find m such that
-//
-// 1. m = max(abs(input_min), abs(input_max)) if range_given is true,
-// 2. m = max(max(abs(min_elem(input)), abs(max_elem(input))) otherwise.
-//
-// Our input tensor range is then [-m, m].
-//
-// Next, we choose our fixed-point quantization buckets, [min_fixed, max_fixed].
-// If signed_input is true, this is
-//
-// [min_fixed, max_fixed ] =
-// [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1].
-//
-// Otherwise, if signed_input is false, the fixed-point range is
-//
-// [min_fixed, max_fixed] = [0, (1 << num_bits) - 1].
-//
-// From this we compute our scaling factor, s:
-//
-// s = (max_fixed - min_fixed) / (2 * m).
-//
-// Now we can quantize and dequantize the elements of our tensor. An element e
-// is transformed into e':
-//
-// e' = (e * s).round_to_nearest() / s.
-//
-// Note that we have a different number of buckets in the signed vs. unsigned
-// cases. For example, if num_bits == 8, we get 254 buckets in the signed case
-// vs. 255 in the unsigned case.
-//
-// For example, suppose num_bits = 8 and m = 1. Then
-//
-// [min_fixed, max_fixed] = [-127, 127], and
-// s = (127 + 127) / 2 = 127.
-//
-// Given the vector {-1, -0.5, 0, 0.3}, this is quantized to
-// {-127, -63, 0, 38}, and dequantized to {-1, -63.0/127, 0, 38.0/127}.
-//
-// Arguments:
-// input: Tensor to quantize and then dequantize.
+// DEPRECATED at GraphDef version 22: Replaced by QuantizeAndDequantizeV2
func QuantizeAndDequantize(scope *Scope, input tf.Output, optional ...QuantizeAndDequantizeAttr) (output tf.Output) {
if scope.Err() != nil {
return