%0 = "tf.Conv2D"(%arg0, %arg1) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    %1 = "tf.BiasAdd"(%0, %arg2) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>

  %1 = "quantfork.dcast"(%0) : (tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>) -> tensor<*xf32>
  %2 = "tf.MaxPool"(%1) {data_format = "NHWC", device = "", explicit_paddings = [], ksize = [1, 2, 2, 1], padding = "VALID", strides = [1, 2, 2, 1]} : (tensor<*xf32>) -> tensor<*xf32>
  %3 = "quantfork.qcast"(%2) {volatile} : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>

// points at opBytes{0x83, 00, 0x05,0x81, 00, 0x01, 0x03}) are:
//
//	Yi8, Yml -> Zibo_m, z (0x83, 00)
//	Yi32, Yax -> Zil_, z+2 (0x05)
//	Yi32, Yml -> Zilo_m, z+2+1 (0x81, 0x00)
//	Yrl, Yml -> Zr_m, z+2+1+2 (0x01)
//	Yml, Yrl -> Zm_r, z+2+1+2+1 (0x03)
//
// The Pconstant in the optab line controls the prefix bytes to emit.  That's
// relatively straightforward as this program goes.
//

  // CHECK-NEXT: [[RESULT0:%.*]] = "tf.Conv2D"([[INPUT]], [[FILTER]]) <{dilations = [1, 2, 2, 1], padding = "VALID", strides = [1, 1, 1, 1]}> : (tensor<1x128x128x3xf32>, tensor<5x5x3x8xf32>) -> tensor<1x120x120x8xf32>
  // CHECK-NEXT: [[RESULT1:%.*]] = "tf.Conv2D"([[INPUT]], [[FILTER]]) <{dilations = [1, 2, 2, 1], padding = "VALID", strides = [1, 1, 1, 1]}> : (tensor<1x128x128x3xf32>, tensor<5x5x3x8xf32>) -> tensor<1x120x120x8xf32>

  %1 = "tf.Conv2D"(%0, %cst) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xbf16>, tensor<2x3x3x2xbf16>) -> tensor<1x3x2x2xbf16>
  %2 = "tf.Cast"(%1) {Truncate = false} : (tensor<1x3x2x2xbf16>) -> tensor<1x3x2x2xf32>

# Services and configs in ns1 namespace
apiVersion: networking.istio.io/v1alpha3
kind: ServiceEntry
metadata:
  name: http1
  namespace: ns1
spec:
  hosts:
  - http1.ns1.svc.cluster.local
  addresses:
  - 2.1.1.1
  ports:
  - number: 8080
    name: http
    protocol: HTTP2
  location: MESH_INTERNAL
  resolution: STATIC
  endpoints:
  - address: 100.1.1.1
    labels:
      version: v1
    ports:

    %0 = "tf.Conv2D"(%arg0, %arg1) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    %1 = "tf.BiasAdd"(%0, %arg2) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>

  const std::vector<int> ksize{1, 2, 2, 1};
  const std::vector<int> strides{1, 2, 2, 1};
  auto y = MaxPool(scope_, x, ksize, strides, "VALID");
  Tensor x_init_value = Tensor(DT_FLOAT, x_shape);
  SetRandomValuesForMaxPooling<float>(&x_init_value);
  RunTest(x, x_init_value, y, y_shape);
}

TEST_F(NNGradTest, MaxPoolGradV2Helper) {
  TensorShape x_shape({1, 2, 2, 1});
  TensorShape y_shape({1, 1, 1, 1});

// Copyright 2021 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package bits_test

import (
	"fmt"
	"math/bits"
)

func ExampleAdd32() {
	// First number is 33<<32 + 12
	n1 := []uint32{33, 12}
	// Second number is 21<<32 + 23
	n2 := []uint32{21, 23}
	// Add them together without producing carry.
	d1, carry := bits.Add32(n1[1], n2[1], 0)

golang.org/toolchain v0.0.1-go1.22.1.linux-amd64
written by hand
-- .info --
{"Version":"v0.0.1-go1.22.1.linux-amd64"}
-- .mod --
golang.org/toolchain
-- go.mod --