* that minimizes the need for remapping as {@code buckets} grows. That is, {@code
   * consistentHash(h, n)} equals:
   *
   * <ul>
   *   <li>{@code n - 1}, with approximate probability {@code 1/n}
   *   <li>{@code consistentHash(h, n - 1)}, otherwise (probability {@code 1 - 1/n})
   * </ul>
   *

   * that minimizes the need for remapping as {@code buckets} grows. That is, {@code
   * consistentHash(h, n)} equals:
   *
   * <ul>
   *   <li>{@code n - 1}, with approximate probability {@code 1/n}
   *   <li>{@code consistentHash(h, n - 1)}, otherwise (probability {@code 1 - 1/n})
   * </ul>
   *

			Selector: &istio_type_v1beta1.WorkloadSelector{
				MatchLabels: map[string]string{
					"gateway.networking.k8s.io/gateway-name": "default",
				},
			},
			Image: &istioio_networking_v1beta1.ProxyImage{
				ImageType: "distroless",
			},
		},
	}); err != nil {
		t.Fatalf("failed to create ProxyConfigs: %s", err)
	}
	proxyConfig := model.GetProxyConfigs(store, mesh.DefaultMeshConfig())
	tests := []struct {

        pattern ^(docker|{{ fluentd_container_runtime_service }}|kubelet|node-problem-detector)\.service$
      </exclude>
    </filter>
    # END_NODE_JOURNAL
  monitoring.conf: |-
    # This source is used to acquire approximate process start timestamp,
    # which purpose is explained before the corresponding output plugin.
    <source>
      @type exec
      command /bin/sh -c 'date +%s'
      tag process_start

        pattern ^(docker|{{ fluentd_container_runtime_service }}|kubelet|node-problem-detector)\.service$
      </exclude>
    </filter>
    # END_NODE_JOURNAL
  monitoring.conf: |-
    # This source is used to acquire approximate process start timestamp,
    # which purpose is explained before the corresponding output plugin.
    <source>
      @type exec
      command /bin/sh -c 'date +%s'
      tag process_start

						for _, have := range counts {
							err := float64(have) - want
							χ2 += err * err
						}
						χ2 /= want
						samples[i] = χ2
					}

					// Check that our samples approximate the appropriate normal distribution.
					dof := float64(nfact - 1)
					expected := &statsResults{mean: dof, stddev: math.Sqrt(2 * dof)}
					errorScale := max(1.0, expected.stddev)

// incrementally shortened q for a total of n + (n-1) + (n-2) ... + 2 + 1, or n(n+1)/2 divW()'s.
// Recursive conversion divides q by its approximate square root, yielding two parts, each half
// the size of q. Using the iterative method on both halves means 2 * (n/2)(n/2 + 1)/2 divW()'s

			s = fmt.Sprintf("%g", x)
		}
		return s
	}

	// Out of float64 range. Do approximate manual to decimal
	// conversion to avoid precise but possibly slow Float
	// formatting.
	// f = mant * 2**exp
	var mant big.Float
	exp := f.MantExp(&mant) // 0.5 <= |mant| < 1.0

	// approximate float64 mantissa m and decimal exponent d
	// f ~ m * 10**d

// integer arithmetic.  Let's instead compute 2^e/c
// for a value of e TBD (^ = exponentiation).  Then
//   ⎣x / c⎦ = ⎣x * (2^e/c) / 2^e⎦.
// Dividing by 2^e is easy.  2^e/c isn't an integer, unfortunately.
// So we must approximate it.  Let's call its approximation m.
// We'll then compute
//   ⎣x * m / 2^e⎦
// Which we want to be equal to ⎣x / c⎦ for 0 <= x < 2^n-1
// where n is the word size.
// Setting x = c gives us c * m >= 2^e.

						for _, have := range counts {
							err := float64(have) - want
							χ2 += err * err
						}
						χ2 /= want
						samples[i] = χ2
					}

					// Check that our samples approximate the appropriate normal distribution.
					dof := float64(nfact - 1)
					expected := &statsResults{mean: dof, stddev: math.Sqrt(2 * dof)}
					errorScale := max(1.0, expected.stddev)