continue
		}
		// First store completed. Check type and overwrite data.
		if typ != np.typ {
			panic("sync/atomic: swap of inconsistently typed value into Value")
		}
		op := (*efaceWords)(unsafe.Pointer(&old))
		op.typ, op.data = np.typ, SwapPointer(&vp.data, np.data)
		return old
	}
}

// CompareAndSwap executes the compare-and-swap operation for the [Value].
//

		return np, grid.NewRemoteErr(err)
	}

	return
}

// LoadUserHandler - reloads a user on the server.
func (s *peerRESTServer) LoadUserHandler(mss *grid.MSS) (np grid.NoPayload, nerr *grid.RemoteErr) {
	objAPI := newObjectLayerFn()
	if objAPI == nil {
		return np, grid.NewRemoteErr(errServerNotInitialized)
	}

	accessKey := mss.Get(peerRESTUser)
	if accessKey == "" {

    self.assertAllClose(new_outputs_1, new_outputs_2)

  @parameterized.named_parameters(
      ('use_constant_with_int32_input', np.int32, False),
      ('use_variable_with_int32_input', np.int32, True),
      ('use_constant_with_int64_input', np.int64, False),
      ('use_variable_with_int64_input', np.int64, True),
  )
  @test_util.run_v2_only
  def test_gather_model(self, input_type, use_variable):

var Funcs = func(codecs runtimeserializer.CodecFactory) []interface{} {
	return []interface{}{
		func(np *networking.NetworkPolicyPeer, c fuzz.Continue) {
			c.FuzzNoCustom(np) // fuzz self without calling this function again
			// TODO: Implement a fuzzer to generate valid keys, values and operators for
			// selector requirements.
			if np.IPBlock != nil {
				np.IPBlock = &networking.IPBlock{
					CIDR:   "192.168.1.0/24",

  @test_util.deprecated_graph_mode_only
  def test_replace_tensors_by_numpy_ndarrays_is_noop_when_no_tensor(self):
    # Fill the representative dataset with np.ndarrays only.
    repr_ds: repr_dataset.RepresentativeDataset = [
        {
            'input_tensor': np.random.uniform(low=-1.0, high=1.0, size=(4, 3)),
        }
        for _ in range(8)
    ]

    with self.session() as sess:

        self.filters = np.stack(
            [
                np.random.uniform(
                    low=-(i + 1), high=(i + 1), size=filter_shape[:-1]
                ).astype('f4')
                for i in range(self.out_channel_size)
            ],
            axis=-1,
        )

        self.bias = np.random.uniform(
            low=0, high=10, size=(self.out_channel_size)

type Node struct {
	s           string
	left, right *Node
}

func walk(np **Node) int { // ERROR "leaking param content: np"
	n := *np
	w := len(n.s)
	if n == nil {
		return 0
	}
	wl := walk(&n.left)
	wr := walk(&n.right)
	if wl < wr {
		n.left, n.right = n.right, n.left // ERROR "ignoring self-assignment"
		wl, wr = wr, wl
	}
	*np = n
	return w + wl + wr
}

type Pointery2 struct {
	p *Pointery2
	x [1024]int
}

// This type and the following one will have the same size.
type Vanilla struct {
	np uintptr
	x  [1024]int
}

type Vanilla2 struct {
	np uintptr
	x  [1023]int
	y  int
}

type Single struct {
	np uintptr
	x  [1023]int
}

var G int

//go:noinline
func clobber() {
	G++
}

func ABC(i, j int) int {
	r := 0

    statistics = calib_stats_pb2.CalibrationStatistics()
    statistics.histogram_statistics.lower_bound = 0.0
    statistics.histogram_statistics.bin_width = 1.0

    hist_freq = np.zeros(501, dtype=np.int32)

    # Advanced calibration methods that use histograms detect outliers, so they
    # don't use the outliers as min/max values.
    hist_freq[0] = 1
    hist_freq[-1] = 1

        self.embedding_w = np.random.randn(1024, 3, 4, 3).astype('f4')
        self.embedding_w = np.minimum(np.maximum(self.embedding_w, -4), 4)

        self.conv_filters = np.random.uniform(
            low=-10, high=10, size=filter_shape
        ).astype('f4')

        second_conv_filter_shape = (3, 3, filter_shape[-1], 1)
        self.second_conv_filters = np.random.uniform(