// Timed Get
  // There are a few design constraints to consider
  // * We want to be responsive to small timeouts, unpark() has non trivial latency overheads (I
  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the
  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of

		state.currentState.registers = make([][]SlotID, len(state.registers))
	} else {
		state.currentState.registers = state.currentState.registers[:len(state.registers)]
	}

	// A relatively small slice, but used many times as the return from processValue.
	state.changedVars = newSparseSet(numVars)
	state.changedSlots = newSparseSet(numSlots)

//
// There are several steps between the time that an M experiences contention and
// when that contention may be added to the profile. This comes from our
// constraints: We need to keep the critical section of each lock small,
// especially when those locks are contended. The reporting code cannot acquire
// new locks until the M has released all other locks, which means no memory
// allocations and encourages use of (temporary) M-local storage.
//

          all_valid = False
        else:
          ver = int(sm_compute_match.group(2))
          if ver < 30:
            print(
                'ERROR: TensorFlow only supports small CUDA compute'
                ' capabilities of sm_30 and higher. Please re-specify the list'
                ' of compute capabilities excluding version %s.' % ver)
            all_valid = False
          if ver < 35:

//
// To avoid requesting more OS memory while there are unswept spans, when a
// goroutine needs another span, it first attempts to reclaim that much memory
// by sweeping. When a goroutine needs to allocate a new small-object span, it
// sweeps small-object spans for the same object size until it frees at least
// one object. When a goroutine needs to allocate large-object span from heap,
// it sweeps spans until it frees at least that many pages into heap. There is

	// Largest time is 2540400h10m10.000000000s
	w := len(buf)

	u := uint64(d)
	neg := d < 0
	if neg {
		u = -u
	}

	if u < uint64(Second) {
		// Special case: if duration is smaller than a second,
		// use smaller units, like 1.2ms
		var prec int
		w--
		buf[w] = 's'
		w--
		switch {
		case u == 0:
			buf[w] = '0'
			return w
		case u < uint64(Microsecond):
			// print nanoseconds

		"0xc0.0xa8.0x8c.0xff",
		// IPv4 in class B form
		"192.168.12345",
		// IPv4 in class B form, with a small enough number to be
		// parseable as a regular dotted decimal field.
		"127.0.1",
		// IPv4 in class A form
		"192.1234567",
		// IPv4 in class A form, with a small enough number to be
		// parseable as a regular dotted decimal field.
		"127.1",
		// IPv4 field has value >255
		"192.168.300.1",

	cst := newClientServerTest(t, mode, HandlerFunc(func(w ResponseWriter, r *Request) {
		w.Header().Set("Content-Encoding", "foo")
		ServeFile(w, r, "testdata/file")

		// Because the testdata is so small, it would fit in
		// both the h1 and h2 Server's write buffers. For h1,
		// sendfile is used, though, forcing a header flush at
		// the io.Copy. http2 doesn't do a header flush so

	exe, err := os.Executable()
	if err != nil {
		t.Fatal(err)
	}

	heap := 1 << 30
	if runtime.GOOS == "android" {
		// Use smaller size for Android to avoid crash.
		heap = 100 << 20
	}
	if runtime.GOOS == "windows" && runtime.GOARCH == "arm" {
		// Use smaller heap for Windows/ARM to avoid crash.
		heap = 100 << 20
	}
	if testing.Short() {
		heap = 100 << 20
	}
	// This makes fork slower.

    return success();
  }
};

// Rewrites `TensorListResize` op into a functional `If` op and several basic
// TF ops to match the op semantics of Tensorflow. Basically, it does:
// 1) If the requested size is smaller or equal than the input tensorlist's
// size, rewrite it to a Slice op so that only the first 'size' rows are
// returned. 2) If the requested size is larger than the input tensorlist's