// if rate<1 treat as unknown and skip scaling.
		return count, size
	}

	avgSize := float64(size) / float64(count)
	scale := 1 / (1 - math.Exp(-avgSize/float64(rate)))

	return int64(float64(count) * scale), int64(float64(size) * scale)
}

// parseContention parses a mutex or contention profile. There are 2 cases:
// "--- contentionz " for legacy C++ profiles (and backwards compatibility)

		"more steps":        {"/api/version/resource/name/subresource", true},
		"group list":        {"/apis/batch/v1/job", true},
		"group get":         {"/apis/batch/v1/job/foo", true},
		"group subresource": {"/apis/batch/v1/job/foo/scale", true},

		"bad root":                     {"/not-api/version/resource", false},
		"group without enough steps":   {"/apis/extensions/v1beta1", false},
		"group without enough steps 2": {"/apis/extensions/v1beta1/", false},

			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
			}

		"to facilitate comparison with original graph."),
	"unit": helpText(
		"Measurement units to display",
		"Scale the sample values to this unit.",
		"For time-based profiles, use seconds, milliseconds, nanoseconds, etc.",
		"For memory profiles, use megabytes, kilobytes, bytes, etc.",
		"Using auto will scale each value independently to the most natural unit."),
	"compact_labels": "Show minimal headers",

	normScale := make([]float64, len(baseVals))
	for i := range baseVals {
		if srcVals[i] == 0 {
			normScale[i] = 0.0
		} else {
			normScale[i] = float64(baseVals[i]) / float64(srcVals[i])
		}
	}
	p.ScaleN(normScale)
	return nil
}

func isZeroSample(s *Sample) bool {
	for _, v := range s.Value {
		if v != 0 {
			return false
		}
	}
	return true
}

type profileMerger struct {

  int activation_number_of_bits_;
};

// Returns a function that returns the quantized type of a bias input.
// The scale of bias is a multiplication of given scale and scales from the
// quantization type of other operands.
inline quant::AccumulatorScaleFunc GetUniformQuantizedTypeForBiasWithScale(
    double scale) {
  return [=](const std::vector<quant::QuantParams>& quant_params,
             const int adjusted_quant_dim,

				p.errorf("%s doesn't support scaled register format", p.arch.Name)
			} else {
				a.Scale = int16(scale)
			}
		}
		p.get(')')
	} else if scale != 0 {
		if p.arch.Family == sys.ARM64 {
			p.errorf("arm64 doesn't support scaled register format")
		}
		// First (R) was missing, all we have is (R*scale).
		a.Reg = 0
		a.Index = r1
		a.Scale = int16(scale)
	}
}

public final class GcFinalization {
  private GcFinalization() {}

  /**
   * 10 seconds ought to be long enough for any object to be GC'ed and finalized. Unless we have a
   * gigantic heap, in which case we scale by heap size.
   */
  private static long timeoutSeconds() {
    // This class can make no hard guarantees.  The methods in this class are inherently flaky, but

      else
        llvm::errs() << "_";
      llvm::errs() << ",";
    }
    llvm::errs() << ")\n";
  });
}

// A heuristic to get quantization parameters satisfies the same scale
// constraints:
// - If there are immutable states,
//   - use the single input, or,
//   - use the single output, or,
//   - use the first one in the collection,
// - use the single input if it is ready, or,

var map_ContainerResourceMetricSource = map[string]string{