MOVOU	X15, 240(DI)
	SUBQ	$256, BX
	ADDQ	$256, DI
	CMPQ	BX, $256
	JAE	loop
	JMP	tail
#endif

loop_preheader_avx2:
	VPXOR X0, X0, X0
	// For smaller sizes MOVNTDQ may be faster or slower depending on hardware.
	// For larger sizes it is always faster, even on dual Xeons with 30M cache.
	// TODO take into account actual LLC size. E. g. glibc uses LLC size/2.
	CMPQ    BX, $0x2000000
	JAE	loop_preheader_avx2_huge

//    Ignored for MLIR input.
//  input_arg_shapes_str:  A string representation of input argument shapes for
//    'main' entry-point, separating tensors with ':', dimension with ',', and
//    using '?' for unknown sizes. For example, 'input-arg-shapes=1,2::1,?'
//    expresses argument shapes [1,2], [] and [1,?].
//  is_input_mlir_module: If true, `input_path` is treated as an MLIR
//    module instead of a SavedModel.
//
// Returns:

            | foo       | bar         |
            | very long | short       |
            | tiny      | even longer |
        """.stripIndent(true).trim()
    }

    def "fails when header and row sizes differ"() {
        given:
        def header = ["a"]
        def rows = [
            new StyledTable.Row(["b"], StyledTextOutput.Style.Normal),
            new StyledTable.Row(["c"], StyledTextOutput.Style.Normal),

func TestStackCheckOutput(t *testing.T) {
	testenv.MustHaveGoBuild(t)
	t.Parallel()

	cmd := testenv.Command(t, testenv.GoToolPath(t), "build", "-o", os.DevNull, "./testdata/stackcheck")
	// The rules for computing frame sizes on all of the
	// architectures are complicated, so just do this on amd64.
	cmd.Env = append(os.Environ(), "GOARCH=amd64", "GOOS=linux")
	outB, err := cmd.CombinedOutput()

	if err == nil {

	maxBytes := 1 + 5*traceBytesPerNumber + len(typName)

	// Estimate the size of this record. This
	// bound is pretty loose, but avoids counting
	// lots of varint sizes.
	//
	// Add 1 because we might also write a traceAllocFreeTypesBatch byte.
	var flushed bool
	w, flushed = w.ensure(1 + maxBytes)
	if flushed {
		// Annotate the batch as containing types.

//	memoryAllocator := runtime.AllocatorPool.Get().(*runtime.Allocator)
//	defer runtime.AllocatorPool.Put(memoryAllocator)
//
// A note for future:
//
//	consider introducing multiple pools for storing buffers of different sizes
//	perhaps this could allow us to be more efficient.
var AllocatorPool = sync.Pool{
	New: func() interface{} {
		return &Allocator{}
	},
}

// Allocator knows how to allocate memory

import com.google.common.annotations.VisibleForTesting;
import org.gradle.internal.jvm.Jvm;

import java.util.Arrays;
import java.util.Locale;

/**
 * Helper to compute maximum heap sizes.
 */
public class MaximumHeapHelper {

    /**
     * Get the default maximum heap.
     *
     * Different JVMs on different systems may use a different default for maximum heap when unset.

func _[_ t | []t]() {}
func _[_ []t | []t]() {}
func _[_ [1]t | [1]t]() {}
func _[_ t[t] | t[t]]() {}

// Single-expression type parameter lists and those that don't start
// with a (type parameter) name are considered array sizes.
// The term must be a valid expression (it could be a type incl. a
// tilde term) but the type-checker will complain.
type (
        _[t] t
        _[t|t] t

// Analyze costs for tensorflow operations.
//
// The current heuristic used is quite simple, which is to calculate the total
// size of input tensors. The exception is that ops whose cost is irrelevant to
// input sizes, such as tf.Shape and tf.Reshape, are whitelisted to have cheap
// cost. This cost analysis is expected to be used conservatively (eg. use a low
// threshold to decide whether a cost is cheap or expensive), as it might not be

	}
}

func BenchmarkSearchWrappers(b *testing.B) {
	for i := 0; i < b.N; i++ {
		runSearchWrappers()
	}
}

// Abstract exhaustive test: all sizes up to 100,
// all possible return values. If there are any small
// corner cases, this test exercises them.
func TestSearchExhaustive(t *testing.T) {
	for size := 0; size <= 100; size++ {