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,

        return {'output': res}

    def data_gen():
      for _ in range(255):
        yield {
            'x': ops.convert_to_tensor(
                np.random.uniform(size=(10)).astype('f4')
            ),
            'y': ops.convert_to_tensor(
                np.random.uniform(size=(10)).astype('f4')
            ),
        }

    root = ModelWithAdd()

    temp_path = self.create_tempdir().full_path

        "\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",

        self.filters_0 = np.random.uniform(
            low=-1.0, high=1.0, size=(4, 3)
        ).astype('f4')
        self.bias_0 = np.random.uniform(low=-1.0, high=1.0, size=(3,)).astype(
            'f4'
        )

        self.filters_1 = np.random.uniform(
            low=-1.0, high=1.0, size=(4, 3)
        ).astype('f4')
        self.bias_1 = np.random.uniform(low=-1.0, high=1.0, size=(3,)).astype(

	// observable side effects, and keeping it in place allows better escape analysis.
	if !ir.Any(*np, func(n ir.Node) bool { return n.Op() != ir.ONEW && callOrChan(n) }) {
		return
	}

	tmp := TempAt(base.Pos, ir.CurFunc, (*np).Type())
	as := ir.NewAssignStmt(base.Pos, tmp, *np)
	as.PtrInit().Append(Stmt(ir.NewDecl(n.Pos(), ir.ODCL, tmp)))
	*np = tmp

	n.PtrInit().Append(Stmt(as))
}

package complexnums

const NN = -1 - 1i
const NP = -1 + 1i
const PN = 1 - 1i