UniformQuantizedPerAxisType CreateI8F32UniformQuantizedPerAxisType(
    Location loc, MLIRContext& context, ArrayRef<double> scales,
    ArrayRef<int64_t> zero_points, int quantization_dimension,
    bool narrow_range = false);

// Creates a `UniformQuantizedPerAxisType` with the given `scales` and
// `zero_points` values. The produced type has f32 as its expressed type and

      SmallVector<double>(scales), SmallVector<int64_t>(zero_points),
      quantization_dimension,
      /*storageTypeMin=*/llvm::minIntN(8) + (narrow_range ? 1 : 0),
      /*storageTypeMax=*/llvm::maxIntN(8));
}

UniformQuantizedPerAxisType CreateI32F32UniformQuantizedPerAxisType(
    const Location loc, MLIRContext& context, const ArrayRef<double> scales,

{
  "kind": "Scale",
  "apiVersion": "apps/v1beta1",
  "metadata": {
    "name": "nameValue",
    "generateName": "generateNameValue",
    "namespace": "namespaceValue",
    "selfLink": "selfLinkValue",
    "uid": "uidValue",
    "resourceVersion": "resourceVersionValue",
    "generation": 7,
    "creationTimestamp": "2008-01-01T01:01:01Z",
    "deletionTimestamp": "2009-01-01T01:01:01Z",
    "deletionGracePeriodSeconds": 10,
    "labels": {

SataQiu <******@****.***> 1713430345 +0800

apiVersion: apps/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

SataQiu <******@****.***> 1713430345 +0800

			sum += scaledImageScore(state, totalNumNodes)
		}
	}
	return sum
}

// scaledImageScore returns an adaptively scaled score for the given state of an image.
// The size of the image is used as the base score, scaled by a factor which considers how much nodes the image has "spread" to.
// This heuristic aims to mitigate the undesirable "node heating problem", i.e., pods get assigned to the same or

    func.func @main(%arg0 : tensor<1xf32>) -> tensor<1xf32> {
      %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
      %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

      %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {
        quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64

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<",

            "type": "typeValue",
            "value": "0",
            "averageValue": "0",
            "averageUtilization": 4
          }
        }
      }
    ],
    "behavior": {
      "scaleUp": {
        "stabilizationWindowSeconds": 3,
        "selectPolicy": "selectPolicyValue",
        "policies": [
          {
            "type": "typeValue",
            "value": 2,