{
  "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

		}
	}
	return false
}

// Scale a measurement from a unit to a different unit and returns
// the scaled value and the target unit. The returned target unit
// will be empty if uninteresting (could be skipped).
func Scale(value int64, fromUnit, toUnit string) (float64, string) {
	// Avoid infinite recursion on overflow.
	if value < 0 && -value > 0 {
		v, u := Scale(-value, fromUnit, toUnit)
		return -v, u
	}

// Converts tf.FusedBatchNormV3 into a sequence of more primitive arithmetic
// operations. Specifically, performs the following calculation:
//
//   (x - mean) * scale / sqrt(variance + epsilon) + offset
//
// Let multiplier = scale / sqrt(variance + epsilon),
// to compute
//   (x - mean) * scale / sqrt(variance + epsilon) + offset,
// is then to compute
//   (x * multiplier) + (offset - mean * multiplier).
//

# CHECK-NEXT:          scale: [ 0.015686 ],
# CHECK-NEXT:          zero_point: [ 128 ]
# CHECK-NEXT:        },
# CHECK-NEXT:        has_rank: true
# CHECK-NEXT:      }, {
# CHECK-NEXT:        shape: [ 4 ],
# CHECK-NEXT:        type: UINT8,
# CHECK-NEXT:        buffer: 2,
# CHECK-NEXT:        name: "input1",
# CHECK-NEXT:        quantization: {
# CHECK-NEXT:          scale: [ 0.023529 ],

            "reason": "reasonValue",
            "fieldPath": "fieldPathValue",
            "optionalOldSelf": true
          }
        ]
      }
    },
    "subresources": {
      "status": {},
      "scale": {
        "specReplicasPath": "specReplicasPathValue",
        "statusReplicasPath": "statusReplicasPathValue",
        "labelSelectorPath": "labelSelectorPathValue"
      }
    },
    "versions": [
      {

namespace mlir::quant::stablehlo {

// Returns StableHLO quantization specs for an op.
std::unique_ptr<OpQuantSpec> GetStableHloOpQuantSpec(Operation* op);

// Returns quantization constraints (ex: fixed output, same scale) given
// a StableHLO op.
std::unique_ptr<OpQuantScaleSpec> GetStableHloQuantConstraints(Operation* op);

// Checks if an op is quantizable in StableHLO quantizer. Argument op is not
// necessarily a StableHLO op.

        input_tensor->quantization->scale.clear();
        input_tensor->quantization->zero_point.clear();
        input_tensor->quantization->min.push_back(-1.0);
        input_tensor->quantization->max.push_back(1.0);

        auto& output_tensor = sub_graph->tensors[op->outputs[0]];
        auto shape = output_tensor->shape;
        output_tensor->quantization->scale.clear();