From 623e6671213cd5286f3f053c2dec355282a679fb Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 15 Mar 2019 15:48:17 -0700
Subject: [PATCH] Go: Update generated wrapper functions for TensorFlow ops.
 PiperOrigin-RevId: 238725068

---
 tensorflow/go/op/wrappers.go | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 7a3c03bd60e..51fa2039c81 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -126,6 +126,15 @@ func FakeQuantWithMinMaxVarsPerChannelNarrowRange(value bool) FakeQuantWithMinMa
 // then de-quantized and output as floats in `[min; max]` interval.
 // `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
 //
+// Before quantization, `min` and `max` values are adjusted with the following
+// logic.
+// It is suggested to have `min <= 0 <= max`. If `0` is not in the range of values,
+// the behavior can be unexpected:
+// If `0 < min < max`: `min_adj = 0` and `max_adj = max - min`.
+// If `min < max < 0`: `min_adj = min - max` and `max_adj = 0`.
+// If `min <= 0 <= max`: `scale = (max - min) / (2^num_bits - 1) `,
+// `min_adj = scale * round(min / scale)` and `max_adj = max + min_adj - min`.
+//
 // 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) {
@@ -307,6 +316,15 @@ func FakeQuantWithMinMaxArgsNarrowRange(value bool) FakeQuantWithMinMaxArgsAttr
 // then de-quantized and output as floats in `[min; max]` interval.
 // `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
 //
+// Before quantization, `min` and `max` values are adjusted with the following
+// logic.
+// It is suggested to have `min <= 0 <= max`. If `0` is not in the range of values,
+// the behavior can be unexpected:
+// If `0 < min < max`: `min_adj = 0` and `max_adj = max - min`.
+// If `min < max < 0`: `min_adj = min - max` and `max_adj = 0`.
+// If `min <= 0 <= max`: `scale = (max - min) / (2^num_bits - 1) `,
+// `min_adj = scale * round(min / scale)` and `max_adj = max + min_adj - min`.
+//
 // Quantization is called fake since the output is still in floating point.
 func FakeQuantWithMinMaxArgs(scope *Scope, inputs tf.Output, optional ...FakeQuantWithMinMaxArgsAttr) (outputs tf.Output) {
 	if scope.Err() != nil {
@@ -20642,6 +20660,15 @@ func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr
 // then de-quantized and output as floats in `[min; max]` interval.
 // `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
 //
+// Before quantization, `min` and `max` values are adjusted with the following
+// logic.
+// It is suggested to have `min <= 0 <= max`. If `0` is not in the range of values,
+// the behavior can be unexpected:
+// If `0 < min < max`: `min_adj = 0` and `max_adj = max - min`.
+// If `min < max < 0`: `min_adj = min - max` and `max_adj = 0`.
+// If `min <= 0 <= max`: `scale = (max - min) / (2^num_bits - 1) `,
+// `min_adj = scale * round(min / scale)` and `max_adj = max + min_adj - min`.
+//
 // This operation has a gradient and thus allows for training `min` and `max`
 // values.
 func FakeQuantWithMinMaxVars(scope *Scope, inputs tf.Output, min tf.Output, max tf.Output, optional ...FakeQuantWithMinMaxVarsAttr) (outputs tf.Output) {