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):

        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 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(

    first_mid = self._lower_bound + self._bin_width / 2
    last_mid = first_mid + (self._num_bins - 1) * self._bin_width
    self._hist_mids = np.linspace(first_mid, last_mid, self._num_bins)

  def _get_dequantized_hist_mids_after_quantize(
      self, quant_min: float, quant_max: float
  ) -> np.ndarray:
    """Quantizes and dequantizes hist_mids using quant_min and quant_max.

	}

	f := &File{
		Meta:  make(map[string]string),
		Count: make(map[string]uint64),
	}
	np := round(len(hdrPrefix), 4)
	hdrLen := *(*uint32)(unsafe.Pointer(&data[np]))
	if hdrLen > pageSize {
		return corrupt()
	}
	meta := data[np+4 : hdrLen]
	if i := bytes.IndexByte(meta, 0); i >= 0 {
		meta = meta[:i]
	}
	m := &mappedFile{
		meta:    string(meta),
		hdrLen:  hdrLen,

        "\n",
        "def calibration_dataset():\n",
        "  rng = np.random.default_rng(seed=1235)\n",
        "  for _ in range(2):\n",
        "    yield {\n",
        "        'lhs_operand': rng.uniform(low=-1.0, high=1.0, size=input_shape).astype(\n",
        "            np.float32\n",
        "        )\n",
        "    }\n",