return
			}
		case "Distribution":
			var zb0002 uint32
			zb0002, err = dc.ReadArrayHeader()
			if err != nil {
				err = msgp.WrapError(err, "Distribution")
				return
			}
			if cap(z.Distribution) >= int(zb0002) {
				z.Distribution = (z.Distribution)[:zb0002]
			} else {
				z.Distribution = make([]int, zb0002)
			}
			for za0001 := range z.Distribution {

			// consider the offline disks as consistent.
			continue
		}
		if !meta.Deleted {
			if len(meta.Erasure.Distribution) != len(onlineDisks) {
				// Erasure distribution seems to have lesser
				// number of items than number of online disks.
				inconsistent++
				continue
			}
			if meta.Erasure.Distribution[i] != meta.Erasure.Index {

          python-version: "3.10"
      - name: Install build dependencies
        run: pip install build
      - name: Build source distribution
        env:
          TIANGOLO_BUILD_PACKAGE: ${{ matrix.package }}
        run: python -m build --sdist
      - name: Decompress source distribution
        run: |
          cd dist
          tar xvf fastapi*.tar.gz
      - name: Install test dependencies
        run: |

/**
 * Utilities for benchmarks.
 *
 * <p>In many cases, we wish to vary the order of magnitude of the input as much as we want to vary
 * the input itself, so most methods which generate values use an exponential distribution varying
 * the order of magnitude of the generated values uniformly at random.
 *
 * @author Louis Wasserman
 */
final class MathBenchmarking {
  static final int ARRAY_SIZE = 0x10000;

	}
	if len(unMarshalXLMeta.Erasure.Distribution) != len(jsoniterXLMeta.Erasure.Distribution) {
		t.Errorf("Expected the size of Erasure Distribution to be %d, but got %d.", len(unMarshalXLMeta.Erasure.Distribution), len(jsoniterXLMeta.Erasure.Distribution))
	} else {
		for i := 0; i < len(unMarshalXLMeta.Erasure.Distribution); i++ {
			if unMarshalXLMeta.Erasure.Distribution[i] != jsoniterXLMeta.Erasure.Distribution[i] {

/**
 * Utilities for benchmarks.
 *
 * <p>In many cases, we wish to vary the order of magnitude of the input as much as we want to vary
 * the input itself, so most methods which generate values use an exponential distribution varying
 * the order of magnitude of the generated values uniformly at random.
 *
 * @author Louis Wasserman
 */
final class MathBenchmarking {
  static final int ARRAY_SIZE = 0x10000;

      - name: 'Set up JDK ${{ matrix.java }}'
        uses: actions/setup-java@8df1039502a15bceb9433410b1a100fbe190c53b # v4.5.0

        with:
          java-version: ${{ matrix.java }}
          distribution: 'zulu'
          cache: 'maven'
      - name: 'Install'
        shell: bash

doc/install.html by running godoc --http=:6060 and then visiting http://localhost:6060/doc/install.html. Unless otherwise noted, the Go source files are distributed under the BSD-style license found in the LICENSE file. -- Binary Distribution Notes If you have just untarred a binary Go distribution, you need to set the environment variable $GOROOT to the full path of the go directory (the one containing this README). You can omit the variable if you unpack it into /usr/local/go, or if you rebuild from sources by...

		"Total cluster buckets count")
	usageObjectsDistributionMD = NewGaugeMD(usageSizeDistribution,
		"Cluster object size distribution", "range")
	usageVersionsDistributionMD = NewGaugeMD(usageVersionCountDistribution,
		"Cluster object version count distribution", "range")
)

// loadClusterUsageObjectMetrics - reads cluster usage metrics.
//
// This is a `MetricsLoaderFn`.

func testShuffleDisks(t *testing.T, z *erasureServerPools) {
	disks := z.serverPools[0].GetDisks(0)()
	distribution := []int{16, 14, 12, 10, 8, 6, 4, 2, 1, 3, 5, 7, 9, 11, 13, 15}
	shuffledDisks := shuffleDisks(disks, distribution)
	// From the "distribution" above you can notice that:
	// 1st data block is in the 9th disk (i.e distribution index 8)