initializedChildren, initializedDepth, initializedExits bool
}

func min8(a, b int8) int8 {
	if a < b {
		return a
	}
	return b
}

func max8(a, b int8) int8 {
	if a > b {
		return a
	}
	return b
}

const (
	blDEFAULT = 0
	blMin     = blDEFAULT
	blCALL    = 1
	blRET     = 2
	blEXIT    = 3
)

	type inputT struct {
		Maxi int64
		Mini int64
		Maxu uint64
		Maxf float64
		Minf float64
		Maxc complex128
		Minc complex128
	}
	var it inputT
	var err error
	b := new(bytes.Buffer)
	enc := NewEncoder(b)
	dec := NewDecoder(b)

	// int8
	b.Reset()
	it = inputT{
		Maxi: math.MaxInt8 + 1,
	}
	type outi8 struct {
		Maxi int8
		Mini int8
	}
	var o1 outi8

        if (!attributes.isEmpty()) {
            Integer max = attributes.stream().map(attr -> attr.getName().length()).max(Integer::compare).orElse(0);
            printSection("Attributes", () -> attributes.forEach(attr -> writeAttribute(max, attr, false)));
        }
    }

    private void writeAttribute(Integer max, ReportAttribute attr, boolean includePrecedence) {
        indent(true);

  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<255.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %minc = "tf.Cast"(%mini) : (tensor<f32>) -> tensor<f32>
  %maxc = "tf.Cast"(%maxi) : (tensor<f32>) -> tensor<f32>

  %min = arith.constant dense<0.0> : tensor<f32>
  %max = arith.constant dense<15.0> : tensor<f32>
  %mini = "tf.Identity"(%min) : (tensor<f32>) -> tensor<f32>
  %maxi = "tf.Identity"(%max) : (tensor<f32>) -> tensor<f32>
  %minc = "tf.Cast"(%mini) : (tensor<f32>) -> tensor<f32>
  %maxc = "tf.Cast"(%maxi) : (tensor<f32>) -> tensor<f32>

      maxs[channel_index] = std::max(maxs[channel_index], ele_value);
    }
    // Expand range to include 0.
    for (int i = 0; i < dim_size; ++i) {
      maxs[i] = std::max(maxs[i], 0.0);
      mins[i] = std::min(mins[i], 0.0);
    }
    if (symmetric) {
      for (int i = 0; i < dim_size; ++i) {
        maxs[i] = std::max(std::abs(mins[i]), std::abs(maxs[i]));
        mins[i] = -maxs[i];

        mins.push_back(rmin);
        maxs.push_back(rmax);
      }
      quant_type = quantfork::fakeQuantAttrsToType(
          op.getLoc(), num_bits, *op.getAxis(), mins, maxs, narrow_range,
          expressed, is_signed);
      if (legacy_float_scale) {
        quant_type = DownCastScale(quant_type, mins, maxs, op->getLoc());
      }
    } else if (auto stats =

      SmallVector<double, 4> mins(1, std::numeric_limits<double>::max());
      SmallVector<double, 4> maxs(1, std::numeric_limits<double>::min());
      // Computes the effective min/max values of the attribute values.
      quant::ExtractMinMaxFromAttr(attr, /*dim_size=*/1, /*slice_size=*/1,
                                   /*symmetric=*/true, mins, maxs);
      double scale = maxs[0] / -llvm::minIntN(tensor_property.number_of_bits);

	// contributions to the distribution must be beyond the
	// desired quantile and hence cannot affect it.
	//
	// First, find the highest desired distribution quantile.
	maxQ := quantiles[0]
	for _, q := range quantiles {
		if q > maxQ {
			maxQ = q
		}
	}
	// The distribution's mass is in units of time (it's not
	// normalized because this would make it more annoying to

			scavenged: []BitRange{{1, 10}},
			min:       m,
			max:       3 * m,
			want:      BitRange{128, 3 * uint(m)},
		}
		tests["Max0"+suffix] = test{
			scavenged: []BitRange{{0, PallocChunkPages - uint(m)}},
			min:       m,
			max:       0,
			want:      BitRange{PallocChunkPages - uint(m), uint(m)},
		}
		if m <= 8 {
			tests["OneFree"] = test{