func test6(iter int) {

	const maxI = 500
	var x int
	m := &x

	// var fn *str
	for i := 0; i < maxI; i++ {
		var fn *str  // this makes it work, because fn stays off heap
		fn = &str{m} // ERROR "&str{...} does not escape"
		recur1(0, fn)
	}

	if *m != maxI {

  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<15.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %rst = "tf.FakeQuantWithMinMaxVars"(%in, %mini, %maxi) {num_bits = 3, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %rst : tensor<8xf32>

        JavaVersion maxi = getMaximumJavaVersionFor(kotlinVersionNumber)
        if (maxi != null) {
            assumeTrue("KGP $kotlinVersionNumber maximum supported Java version is $maxi, current is $current", current <= maxi)
        }
    }

    static boolean hasConfigurationCacheWarnings(VersionNumber kotlinVersion) {

  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<255.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %rst = "tf.FakeQuantWithMinMaxVars"(%in, %mini, %maxi) {num_bits = 5, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %rst : tensor<8xf32>

        assumeTrue("AGP $agpVersion minimum supported Java version is $mini, current is $current", current >= mini)
        JavaVersion maxi = getMaximumJavaVersionFor(agpVersionNumber)
        if (maxi != null) {
            assumeTrue("AGP $agpVersion maximum supported Java version is $maxi, current is $current", current <= maxi)
        }
    }

    static JavaVersion getMinimumJavaVersionFor(String agpVersion) {

  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<15.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %rst = "tfl.custom_tf"(%in, %mini, %maxi) ({
  ^bb0(%arg1: tensor<8xf32>, %arg2: tensor<f32>, %arg3: tensor<f32>):

  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<255.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %rst = "tfl.custom_tf"(%in, %mini, %maxi) ({
  ^bb0(%arg1: tensor<8xf32>, %arg2: tensor<f32>, %arg3: tensor<f32>):

	minI := -int64(1) << (n - 1)
	maxI := int64(1) << (n - 1)
	for c := int64(1); c < maxI; c++ {
		if !sdivisibleOK(n, int64(c)) {
			continue
		}
		k := sdivisible(n, int64(c)).k
		m := sdivisible(n, int64(c)).m
		a := sdivisible(n, int64(c)).a
		max := sdivisible(n, int64(c)).max
		mask := ^uint64(0) >> (64 - n)
		for i := minI; i < maxI; i++ {
			want := i%c == 0

	type inputT struct {
		Maxi int64
		Mini int64
		Maxu uint64
		Maxf float64
		Minf float64
		Maxc complex128
		Minc complex128
	}
	var it inputT
	var err error
	b := new(bytes.Buffer)
	enc := NewEncoder(b)
	dec := NewDecoder(b)

	// int8
	b.Reset()
	it = inputT{
		Maxi: math.MaxInt8 + 1,
	}
	type outi8 struct {
		Maxi int8
		Mini int8
	}
	var o1 outi8

		recordDelimiter string
		fieldDelimiter  string
		header          bool
		wantColumns     []string
		wantTenFields   string
		totalFields     int
	}{
		{
			file:            "nyc-taxi-data-100k.csv",
			recordDelimiter: "\n",
			fieldDelimiter:  ",",
			header:          true,