apiVersion: apps/v1beta2
kind: Scale
metadata:
  annotations:
    annotationsKey: annotationsValue
  creationTimestamp: "2008-01-01T01:01:01Z"
  deletionGracePeriodSeconds: 10
  deletionTimestamp: "2009-01-01T01:01:01Z"
  finalizers:
  - finalizersValue
  generateName: generateNameValue
  generation: 7
  labels:
    labelsKey: labelsValue
  managedFields:
  - apiVersion: apiVersionValue
    fieldsType: fieldsTypeValue

apiVersion: extensions/v1beta1
kind: Scale
metadata:
  annotations:
    annotationsKey: annotationsValue
  creationTimestamp: "2008-01-01T01:01:01Z"
  deletionGracePeriodSeconds: 10
  deletionTimestamp: "2009-01-01T01:01:01Z"
  finalizers:
  - finalizersValue
  generateName: generateNameValue
  generation: 7
  labels:
    labelsKey: labelsValue
  managedFields:
  - apiVersion: apiVersionValue
    fieldsType: fieldsTypeValue

// was lost. (1.1e5).AsScale(5) would return 1.1e5, but (1.1e5).AsScale(6) would return 1e6.
func (a int64Amount) AsScale(scale Scale) (int64Amount, bool) {
	if a.scale >= scale {
		return a, true
	}
	result, exact := negativeScaleInt64(a.value, scale-a.scale)
	return int64Amount{value: result, scale: scale}, exact
}

f32], _support_output_type_float_in_quantized_op = true, detections_per_class = 100 : i64, device = "", h_scale = 5.000000e+00 : f32, max_classes_per_detection = 1 : i64, max_detections = 20 : i64, nms_iou_threshold = 6.000000e-01 : f32, nms_score_threshold = 3.000000e-01 : f32, num_classes = 90 : i64, use_regular_nms = false, w_scale = 5.000000e+00 : f32, x_scale = 1.000000e+01 : f32, y_scale = 1.000000e+01 : f32} : (tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>) -> (te...

type PCIter struct {
	p       []byte
	PC      uint32
	NextPC  uint32
	PCScale uint32
	Value   int32
	start   bool
	Done    bool
}

// NewPCIter creates a PCIter with a scale factor for the PC step size.
func NewPCIter(pcScale uint32) *PCIter {
	it := new(PCIter)
	it.PCScale = pcScale
	return it
}

// Next advances it to the Next pc.
func (it *PCIter) Next() {

    for (RequantizeState& rescale : rescales) {
      if (rescale.params == quantized_type) {
        rescale.users.emplace_back(op, operand_index);
        return true;
      }
    }
    RequantizeState& rescale = rescales.emplace_back();
    rescale.pos = RequantizeState::ON_OUTPUT;
    rescale.params = quantized_type;
    rescale.users.emplace_back(op, operand_index);
    return true;
  }

	}
	return q.i.Sign()
}

// AsScale returns the current value, rounded up to the provided scale, and returns
// false if the scale resulted in a loss of precision.
func (q *Quantity) AsScale(scale Scale) (CanonicalValue, bool) {
	if q.d.Dec != nil {
		return q.d.AsScale(scale)
	}
	return q.i.AsScale(scale)
}

// RoundUp updates the quantity to the provided scale, ensuring that the value is at

  // TFR_ConstantTensorOp (
  //   ConstantOp (ConstAttr<F32Attr (in_scale[0] * in_scale[1] /
  //   out_scale))
  // )
  // Currently, all decompositions using this pattern (Conv2D, FC) have the
  // following preconditions:
  // * out_scale: float scalar attribute
  // * in_scale[0] (input scale): float scalar, given by tf.Const -> tfr.cast
  // * in_scale[1] (filter scale): float scalar/vector

  auto &state = GetResultQuantState(op, res_index);
  if (state.params == params) {
    return false;
  }
  if (!state.IsEmpty()) {
    auto &rescale = GetResultRequantizeState(op, res_index);
    rescale.params = params;
    rescale.pos = RequantizeState::ON_INPUT;
    return false;
  }
  state.params = params;
  return true;
}

    if (multiplier_attr.isSplat()) {
      double new_scale = multiplier_array.front() * uniform_type.getScale();
      new_qtype = UniformQuantizedType::get(
          uniform_type.getFlags(), uniform_type.getStorageType(),
          uniform_type.getExpressedType(), new_scale,
          uniform_type.getZeroPoint(), uniform_type.getStorageTypeMin(),