Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 314783162
Change-Id: I89794559babaed55caf4a430a3a343823d278e59

tensorflow/go/op/wrappers.go

@@ -9487,6 +9487,14 @@ func DebugIdentityV2DebugUrls(value []string) DebugIdentityV2Attr {
 	}
 }
 
+// DebugIdentityV2CircularBufferSize sets the optional circular_buffer_size attribute to value.
+// If not specified, defaults to 1000
+func DebugIdentityV2CircularBufferSize(value int64) DebugIdentityV2Attr {
+	return func(m optionalAttr) {
+		m["circular_buffer_size"] = value
+	}
+}
+
 // Debug Identity V2 Op.
 //
 // Provides an identity mapping from input to output, while writing the content of
@@ -26646,19 +26654,23 @@ func FakeQuantWithMinMaxArgsNarrowRange(value bool) FakeQuantWithMinMaxArgsAttr
 
 // Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
 //
-// Attributes `[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 16, inclusive.
+// Attributes
+//
+// *   `[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 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) `,
+//
+// *   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.
@@ -37416,24 +37428,29 @@ func FakeQuantWithMinMaxVarsPerChannelNarrowRange(value bool) FakeQuantWithMinMa
 	}
 }
 
-// Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`,
+// Fake-quantize the 'inputs' tensor of type float via per-channel floats
 //
-// `[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` of shape `[d]`
-// to 'outputs' tensor of same shape as `inputs`.
+// Fake-quantize the `inputs` tensor of type float per-channel 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 16, inclusive.
+// Attributes
+//
+// *   `[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 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) `,
+//
+// *   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`
@@ -45265,23 +45282,28 @@ func FakeQuantWithMinMaxVarsNarrowRange(value bool) FakeQuantWithMinMaxVarsAttr
 	}
 }
 
-// Fake-quantize the 'inputs' tensor of type float via global float scalars `min`
+// Fake-quantize the 'inputs' tensor of type float via global float scalars
 //
-// and `max` to 'outputs' tensor of same shape as `inputs`.
+// Fake-quantize the `inputs` tensor of type float via global float scalars
+// `min` and `max` 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 16, inclusive.
+// Attributes
+//
+// *   `[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 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) `,
+//
+// *   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`