This setting will be removed in Go 1.27.

Go 1.22 changed how the runtime interacts with transparent huge pages on Linux.
In particular, a common default Linux kernel configuration can result in
significant memory overheads, and Go 1.22 no longer works around this default.
To work around this issue without adjusting kernel settings, transparent huge
pages can be disabled for Go memory with the

   * scattered in memory and thus don't interfere much with each
   * other. But Atomic objects residing in arrays will tend to be
   * placed adjacent to each other, and so will most often share
   * cache lines (with a huge negative performance impact) without
   * this precaution.
   *
   * In part because Cells are relatively large, we avoid creating
   * them until they are needed.  When there is no contention, all

// 0xFFFFFFFF, but not before.
func TestZip64DirectoryOffset(t *testing.T) {
	if testing.Short() {
		t.Skip("skipping in short mode")
	}
	t.Parallel()
	const filename = "huge.txt"
	gen := func(wantOff uint64) func(*Writer) {
		return func(w *Writer) {
			w.testHookCloseSizeOffset = func(size, off uint64) {
				if off != wantOff {

   * scattered in memory and thus don't interfere much with each
   * other. But Atomic objects residing in arrays will tend to be
   * placed adjacent to each other, and so will most often share
   * cache lines (with a huge negative performance impact) without
   * this precaution.
   *
   * In part because Cells are relatively large, we avoid creating
   * them until they are needed.  When there is no contention, all

	s.Split(c.split)
	for s.Scan() {
	}
	if s.Err() != nil {
		t.Fatal("after scan:", s.Err())
	}
	if c != 0 {
		t.Fatalf("stopped with %d left to process", c)
	}
}

// Make sure we can read a huge token if a big enough buffer is provided.
func TestHugeBuffer(t *testing.T) {
	text := strings.Repeat("x", 2*MaxScanTokenSize)
	s := NewScanner(strings.NewReader(text + "\n"))
	s.Buffer(make([]byte, 100), 3*MaxScanTokenSize)

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.
* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

    @ValueSource(ints = { 0, 1, 10, 100, 1000, 4096 })
    void testDecodeWithVariousAceCounts(int aceCount) throws SMBProtocolDecodingException {
        // This test is theoretical as we can't create huge buffers
        // but tests the boundary conditions
        if (aceCount <= 10) { // Only test small counts practically
            byte[] buffer = new byte[2048];

     *   lock.unlock();
     * }
     *
     * If we only held the int[] stripes, translating it on the fly to L's, the original locks might
     * be garbage collected after locking them, ending up in a huge mess.
     */
    @SuppressWarnings("unchecked") // we carefully replaced all keys with their respective L's
    List<L> asStripes = (List<L>) result;
    return Collections.unmodifiableList(asStripes);
  }

It used custom types in its declarations instead of standard Python types, but it was still a huge step forward.

It also was one of the first frameworks to generate a custom schema declaring the whole API in JSON.