// in this map are the packages and objects that may be relevant to the
// current analysis unit.
//
// Packages in the map that are only indirectly imported may be
// incomplete (!pkg.Complete()).
//
// This function scales very poorly with packages' transitive object
// references, which can be more than a million for each package near
// the top of a large project. (This was a significant contributor to
// #60621.)

		return emptyProfile(), nil
	}

	valueIndex := -1
	for i, s := range p.SampleType {
		// Samples count is the raw data collected, and CPU nanoseconds is just
		// a scaled version of it, so either one we can find is fine.
		if (s.Type == "samples" && s.Unit == "count") ||
			(s.Type == "cpu" && s.Unit == "nanoseconds") {
			valueIndex = i
			break
		}
	}

	if valueIndex == -1 {

		p := perfCounterNodeStatsClient{}
		newValue := p.convertCPUValue(cpuCores, tc.cpuValue)
		assert.Equal(t, tc.expected, newValue)
	}
}

func TestGetCPUUsageNanoCores(t *testing.T) {
	// Scaled expected unit test values by the frequency the CPU perf counters are polled.
	perfCounterUpdatePeriodSeconds := uint64(perfCounterUpdatePeriod / time.Second)
	testCases := []struct {
		latestValue   uint64

            k <= bias -> TMIN
            k >= bias + TMAX -> TMAX
            else -> k - bias
          }
        if (digit < t) break
        val scaleW = BASE - t
        if (w > Int.MAX_VALUE / scaleW) return false // Prevent overflow.
        w *= scaleW
      }
      bias = adapt(i - oldi, codePoints.size + 1, oldi == 0)
      val deltaN = i / (codePoints.size + 1)

spec:
  behavior:
    scaleDown:
      policies:
      - periodSeconds: 3
        type: typeValue
        value: 2
      selectPolicy: selectPolicyValue
      stabilizationWindowSeconds: 3
    scaleUp:
      policies:
      - periodSeconds: 3
        type: typeValue
        value: 2
      selectPolicy: selectPolicyValue
      stabilizationWindowSeconds: 3
  maxReplicas: 3
  metrics:
  - containerResource:

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

  } else {  // float
    signature->push_back(AnyQuantizedType());
  }
}

LogicalResult DeviceTarget::RegisterKernel(
    llvm::StringRef kernel, const KernelSpecs::Signature& signature,
    const ScaleFn& fn, const ScaleDecomposeFn& dfn) {
  return specs_[kernel].Add(signature, {ScaleConstraintType::CustomScale, fn});
}

namespace ph = std::placeholders;

LogicalResult DeviceTarget::RegisterKernel(

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();

    if [[ "${UPDATE_API_KNOWN_VIOLATIONS:-}" == "true" ]]; then
        update_report="--update-report"
    fi
fi

kube::codegen::gen_openapi \
    --extra-pkgs k8s.io/api/autoscaling/v1 `# needed for Scale type` \
    --output-dir "${SCRIPT_ROOT}/pkg/generated/openapi" \
    --output-pkg "${THIS_PKG}/pkg/generated/openapi" \
    --report-filename "${report_filename:-"/dev/null"}" \
    ${update_report:+"${update_report}"} \