const auto& shape = reader->GetVariableToShapeMap().at(name);
  int rank = shape.dims();
  if (num_dims != rank) {
    status->status = InvalidArgument("Expected rank is ", num_dims,
                                     " but actual rank is ", rank);
    return;
  }
  for (int i = 0; i < num_dims; i++) {
    dims[i] = shape.dim_size(i);
  }
}

labels.facet_filetype_pdf=filetype:pdf\t\
labels.facet_filetype_txt=filetype:txt\t\
labels.facet_filetype_others=filetype:others\n\

# ranking
rank.fusion.window_size=200
rank.fusion.rank_constant=20
rank.fusion.threads=-1
rank.fusion.score_field=rf_score

# acl
smb.role.from.file=true
smb.available.sid.types=1,2,4:2,5:1
file.role.from.file=true
ftp.role.from.file=true

# backup

  }

  tensorflow::shape_inference::ShapeHandle shape = ic->output(output.index);

  int rank = -1;
  if (ic->RankKnown(shape)) {
    rank = ic->Rank(shape);
  }

  if (num_dims != rank) {
    status->status = InvalidArgument("Expected rank is ", num_dims,
                                     " but actual rank is ", rank);
    return;
  }

  if (num_dims == 0) {
    // Output shape is a scalar.

    /** The key of the configuration. e.g. 200 */
    String RANK_FUSION_window_size = "rank.fusion.window_size";

    /** The key of the configuration. e.g. 20 */
    String RANK_FUSION_rank_constant = "rank.fusion.rank_constant";

    /** The key of the configuration. e.g. -1 */
    String RANK_FUSION_THREADS = "rank.fusion.threads";

    /** The key of the configuration. e.g. rf_score */

        as `tf.tpu.experimental.embedding.serving_embedding_lookup` which can
        take arbitrary rank of dense and sparse tensor. For ragged tensor,
        though the input tensor remains to be rank 2, the activations now can be
        rank 2 or above by specifying the output shape in the feature config or
        via the build method.

*   Add

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(dims[0], returned_dims[0]);
  EXPECT_EQ(dims[1], returned_dims[1]);

  // Try to set 'unknown' with unknown rank on the shape and see that
  // it doesn't change.
  TF_GraphSetTensorShape(graph, feed_out_0, /*dims=*/nullptr, -1, s);
  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

					<groupId>org.opensearch.plugin</groupId>
					<artifactId>aggs-matrix-stats-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>rank-eval-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>lang-expression</artifactId>
				</exclusion>
				<exclusion>

func initCallhome(ctx context.Context, objAPI ObjectLayer) {
	if !globalCallhomeConfig.Enabled() {
		return
	}

	go func() {
		r := rand.New(rand.NewSource(time.Now().UnixNano()))
		// Leader node (that successfully acquires the lock inside runCallhome)
		// will keep performing the callhome. If the leader goes down for some reason,
		// the lock will be released and another node will acquire it and take over
		// because of this loop.
		for {

package cmd

import (
	"context"
	"fmt"
	"io"
	"math/rand"
	"sync"
	"time"

	"github.com/minio/pkg/v2/sync/errgroup"
)

func (er erasureObjects) getOnlineDisks() (newDisks []StorageAPI) {
	disks := er.getDisks()
	var wg sync.WaitGroup
	var mu sync.Mutex
	r := rand.New(rand.NewSource(time.Now().UnixNano()))
	for _, i := range r.Perm(len(disks)) {
		i := i
		wg.Add(1)

	var wg sync.WaitGroup
	disks := er.getDisks()
	infos := make([]DiskInfo, len(disks))
	r := rand.New(rand.NewSource(time.Now().UnixNano()))
	for _, i := range r.Perm(len(disks)) {
		i := i
		wg.Add(1)
		go func() {
			defer wg.Done()

			disk := disks[i]
			if disk == nil {