import (
	"math"
	"math/big"
	"testing"
)

func TestScaledValueInternal(t *testing.T) {
	tests := []struct {
		unscaled *big.Int
		scale    int
		newScale int

		want int64
	}{
		// remain scale
		{big.NewInt(1000), 0, 0, 1000},

		// scale down
		{big.NewInt(1000), 0, -3, 1},
		{big.NewInt(1000), 3, 0, 1},
		{big.NewInt(0), 3, 0, 0},

		// always round up
		{big.NewInt(999), 3, 0, 1},

		return &big.Int{}
	}
}

// scaledValue scales given unscaled value from scale to new Scale and returns
// an int64. It ALWAYS rounds up the result when scale down. The final result might
// overflow.
//
// scale, newScale represents the scale of the unscaled decimal.
// The mathematical value of the decimal is unscaled * 10**(-scale).
func scaledValue(unscaled *big.Int, scale, newScale int) int64 {
	dif := scale - newScale

class FixedResultScale<QuantizedType qt> : NativeOpTrait<!strconcat(
  "quant::FixedResult", qt.name, "Scale<", qt.asTraitArgsStr, ">::Impl")>;

// Specify this trait if the bias-th input of the op is a bias input, which
// needs a scale based on the scales of op1 and op2.
class AccumulatorUniformScale<int bias, int op1, int op2> : NativeOpTrait<
  !strconcat("quant::AccumulatorUniformScale<",

MinIO is a cloud-native application designed to scale in a sustainable manner in multi-tenant environments. Orchestration platforms provide perfect launchpad for MinIO to scale. Below is the list of MinIO deployment documents for various orchestration platforms:

  constexpr int kOutWidth = 6;

  const TensorShape kXShape = TensorShape({1, 2, 3, 1});
  for (const Input scale : kScales) {
    for (const Input translation : kTranslations) {
      for (const std::string& kernel_type : kKernelTypes) {
        TestScaleAndTranslate<float, float, float>(
            kXShape, kOutHeight, kOutWidth, scale, translation, kernel_type,
            true);
      }
    }
  }
}

				{Name: string(v1.ResourceMemory), Weight: 1},
				{Name: "nvidia.com/gpu", Weight: 1},
			}},
			runPreScore: true,
		},
		// Only one node (node1) has the scalar resource, pod doesn't request the scalar resource and the scalar resource should be skipped for consideration.
		// Node1: std = 0, score = 100
		// Node2: std = 0, score = 100
		{
			pod:          st.MakePod().Obj(),

)

// ScaleSpec describes the attributes of a scale subresource
type ScaleSpec struct {
	// replicas is the number of observed instances of the scaled object.
	// +optional
	Replicas int32 `json:"replicas,omitempty" protobuf:"varint,1,opt,name=replicas"`
}

// ScaleStatus represents the current status of a scale subresource.
type ScaleStatus struct {

# Verify that we match correctly the input / output when they are scalar.

# CHECK-LABEL: func @main
# CHECK-SAME:  (%{{[a-z0-9]+}}: tensor<f32> {tf.device = "/device:CPU:0"})
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "out:1,out"

}

func (Scale) SwaggerDoc() map[string]string {
	return map_Scale
}

var map_ScaleSpec = map[string]string{
	"":         "ScaleSpec describes the attributes of a scale subresource.",
	"replicas": "replicas is the desired number of instances for the scaled object.",
}

			quality = 100
		}
	}
	// Convert from a quality rating to a scaling factor.
	var scale int
	if quality < 50 {
		scale = 5000 / quality
	} else {
		scale = 200 - quality*2
	}
	// Initialize the quantization tables.
	for i := range e.quant {
		for j := range e.quant[i] {
			x := int(unscaledQuant[i][j])
			x = (x*scale + 50) / 100
			if x < 1 {
				x = 1
			} else if x > 255 {
				x = 255
			}