// The type checker reports an error if an importer is needed
	// but none was installed.
	Importer Importer

	// If Sizes != nil, it provides the sizing functions for package unsafe.
	// Otherwise SizesFor("gc", "amd64") is used instead.
	Sizes Sizes

	// If DisableUnusedImportCheck is set, packages are not checked
	// for unused imports.
	DisableUnusedImportCheck bool

	cap := v.Cap()
	max := cap
	for _, size := range sizes {
		if size > max {
			max = size
		}
	}
	if len(sizes) == 0 || max > maxCapacity {
		max = maxCapacity
	}
	if max <= cap {
		return
	}
	if v.Len() > 0 {
		extra := reflect.MakeSlice(v.Type(), v.Len(), max)
		reflect.Copy(extra, v)
		v.Set(extra)
	} else {
		extra := reflect.MakeSlice(v.Type(), 0, max)

    override fun close() {
      if (closed) return
      closed = true
      detachTimeout(timeout)
      state = STATE_READ_RESPONSE_HEADERS
    }
  }

  /**
   * An HTTP body with alternating chunk sizes and chunk bodies. It is the caller's responsibility
   * to buffer chunks; typically by using a buffered sink with this sink.
   */
  private inner class ChunkedSink : Sink {

      // side_input_scale and not relevant in this case.
      auto sizes = mlir::DenseI32ArrayAttr::get(context, {1, 1, 1, 0});
      auto attr_name =
          StringAttr::get(context, mlir::OpTrait::AttrSizedOperandSegments<
                                       void>::getOperandSegmentSizeAttr());
      attrs.push_back(NamedAttribute(attr_name, sizes));
    }

// This is a subset of the tests in range.go for range over integers,
// with extra tests, and without the need for -goexperiment=range.

package p

// test framework assumes 64-bit int/uint sizes by default
const (
	maxInt  = 1<<63 - 1
	maxUint = 1<<64 - 1
)

type MyInt int32

func _() {
	for range -1 {
	}
	for range 0 {
	}
	for range 1 {
	}
	for range uint8(1) {
	}

// NOLINTNEXTLINE
opt<std::string> input_shapes(
    "tf-input-shapes",
    llvm::cl::desc(
        "Input tensor shapes. Shapes for different tensors are separated by "
        "':', and dimension sizes for the same tensor are separated by ','"),
    llvm::cl::init(""));

// NOLINTNEXTLINE
opt<std::string> output_arrays(
    "tf-output-arrays", llvm::cl::desc("Output tensor names, separated by ','"),

	// length in octets of the RSA modulus n." 🙄
	//
	// This is extremely annoying, as all other encrypt and decrypt inputs are
	// always the exact same size as the modulus. Since it only happens for
	// weird modulus sizes, fix it by padding inefficiently.
	if emLen, k := len(em), priv.Size(); emLen < k {
		emNew := make([]byte, k)
		copy(emNew[k-emLen:], em)
		em = emNew
	}

	return decrypt(priv, em, withCheck)
}

// utilization distribution for that window size.
//
// Render plot progressively so rough outline is visible quickly even
// for very complex MUTs. Start by computing just a few window sizes
// and then add more window sizes.
//
// Consider using sampling to compute an approximate MUT. This would
// work by sampling the mutator utilization at randomly selected
// points in time in the trace to build an empirical distribution. We

			for i := 0; i < b.N; i++ {
				n.setBytes(buf[:l])
			}
		})
	}
}

func TestNatDiv(t *testing.T) {
	sizes := []int{
		1, 2, 5, 8, 15, 25, 40, 65, 100,
		200, 500, 800, 1500, 2500, 4000, 6500, 10000,
	}
	for _, i := range sizes {
		for _, j := range sizes {
			a := rndNat1(i)
			b := rndNat1(j)
			// the test requires b >= 2
			if len(b) == 1 && b[0] == 1 {
				b[0] = 2
			}

		}

		for _, denylist := range denylistedFlags {
			if strings.Contains(string(out), denylist) {
				t.Fatalf("binary contains unexpected flags: %v", string(out))
			}
		}
	})
}

// Test that binary sizes do not bloat
func TestBinarySizes(t *testing.T) {
	cases := map[string]struct {
		minMb int64
		maxMb int64
	}{
		// TODO: shrink the ranges here once the active work to reduce binary size is complete