_ = .0
	_ = .123
	_ = .0123

	_ = 0e0
	_ = 123e+0
	_ = 0123e-1

	_ = 0e-0
	_ = 123e+0
	_ = 0123e123

	_ = 0.e+1
	_ = 123.e-10
	_ = 0123.e123

	_ = .0e-1
	_ = .123e+10
	_ = .0123e123

	_ = 0.0
	_ = 123.123
	_ = 0123.0123

	_ = 0.0e1
	_ = 123.123e-10
	_ = 0123.0123e+456

	_ = 1_2_3.
	_ = 0_123.

	_ = 0_0e0
	_ = 1_2_3e0
	_ = 0_123e0

	_ = 0e-0_0

	_ = .0
	_ = .123
	_ = .0123

	_ = 0e0
	_ = 123e+0
	_ = 0123E-1

	_ = 0e-0
	_ = 123E+0
	_ = 0123E123

	_ = 0.e+1
	_ = 123.E-10
	_ = 0123.e123

	_ = .0e-1
	_ = .123E+10
	_ = .0123E123

	_ = 0.0
	_ = 123.123
	_ = 0123.0123

	_ = 0.0e1
	_ = 123.123E-10
	_ = 0123.0123e+456

	_ = 1_2_3.
	_ = 0_123.

	_ = 0_0e0
	_ = 1_2_3e0
	_ = 0_123e0

	_ = 0e-0_0

  %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>>

# The original version of this file is located at /manifests/helm-profiles directory.
# If you want to make a change in this file, edit the original one and run "make gen".

meshConfig:
  # 1.22 behavioral changes
  defaultConfig:
    proxyMetadata:

    %0:2 = tf_executor.graph {
      %outputs:2, %control = tf_executor.island wraps "tf.MaxPoolWithArgmax"(%arg0) {T = f32, Targmax = i32, include_batch_in_index = false, ksize = [1, 2, 2, 1], padding = "SAME", strides = [1, 2, 2, 1]} : (tensor<2x4x2x2xf32>) -> (tensor<2x2x1x2xf32>, tensor<2x2x1x2xi32>)
      tf_executor.fetch %outputs#1, %outputs#0 : tensor<2x2x1x2xi32>, tensor<2x2x1x2xf32>
    }

  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});

}

// This type and the following one will have the same size.
type Vanilla struct {
	np uintptr
	x  [1024]int
}

type Vanilla2 struct {
	np uintptr
	x  [1023]int
	y  int
}

type Single struct {
	np uintptr
	x  [1023]int
}

var G int

//go:noinline
func clobber() {
	G++
}

func ABC(i, j int) int {
	r := 0

	// here v2 and v3 can be overlapped.
	clobber()

        !descriptor.hasResult()
    }

    def "can mark as listed using version strings"() {
        when:
        descriptor.listed(['1.2', '1.3'])

        then:
        descriptor.state == Listed
        descriptor.authoritative
        descriptor.versions == ['1.2', '1.3'] as Set
    }

    def "can mark as failed"() {
        org.gradle.internal.Factory<String> broken = { "too bad" }

  // 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>

// This is a hack, but it prevents go from climbing further and trying to
// reconcile the various deps across the "real" modules and this one.

go 1.22.0