fmt.Fprintf(cmd.OutOrStdout(),
					"Warning: No Kubernetes Services select pod %s (see https://istio.io/docs/setup/kubernetes/additional-setup/requirements/ )\n", // nolint: lll
					kname(pod.ObjectMeta))
			}
			// TODO look for port collisions between services targeting this pod

			kubeClient, err := ctx.CLIClientWithRevision(opts.Revision)
			if err != nil {
				return err
			}

			configClient := client.Istio()

        cliRequest.topDirectory = topDirectory;
        // We're very early in the process, and we don't have the container set up yet,
        // so we rely on the JDK services to eventually look up a custom RootLocator.
        // This is used to compute {@code session.rootDirectory} but all {@code project.rootDirectory}
        // properties will be computed through the RootLocator found in the container.

# WARNING: THESE OPTIONS WONT WORK IF YOU DO NOT HAVE PROPER AUTHENTICATION AND PERMISSIONS

# Use --config=tf_public_cache to try and use the TensorFlow public build cache
# to build TensorFlow. Look at ci/official/envs to find which types of jobs
# push to the cache.  For macOS, use --config=tf_public_macos_cache

			nsUnlocker()
		}
	}()

	// Acquire lock
	if !opts.NoLock {
		lock := er.NewNSLock(bucket, object)
		lkctx, err := lock.GetRLock(ctx, globalOperationTimeout)
		if err != nil {
			return nil, err
		}
		ctx = lkctx.Context()

		// Release lock when the metadata is verified, and reader
		// is ready to be read.
		//
		// This is possible to be lock free because

   * of reads may not be immediately reflected on the algorithm's data structures. These structures
   * are guarded by a lock and operations are applied in batches to avoid lock contention. The
   * penalty of applying the batches is spread across threads so that the amortized cost is slightly
   * higher than performing just the operation without enforcing the capacity constraint.

   * of reads may not be immediately reflected on the algorithm's data structures. These structures
   * are guarded by a lock and operations are applied in batches to avoid lock contention. The
   * penalty of applying the batches is spread across threads so that the amortized cost is slightly
   * higher than performing just the operation without enforcing the capacity constraint.

   * value, you should always choose it explicitly.
   *
   * <p>The current implementation uses the concurrency level to create a fixed number of hashtable
   * segments, each governed by its own write lock. The segment lock is taken once for each explicit
   * write, and twice for each cache loading computation (once prior to loading the new value, and
   * once after loading completes). Much internal cache management is performed at the segment

	defer func() {
		if unlockOnDefer {
			nsUnlocker()
		}
	}()

	// Acquire lock
	if !opts.NoLock {
		lock := z.NewNSLock(bucket, object)
		lkctx, err := lock.GetRLock(ctx, globalOperationTimeout)
		if err != nil {
			return nil, err
		}
		ctx = lkctx.Context()
		nsUnlocker = func() { lock.RUnlock(lkctx) }
		unlockOnDefer = true
	}

	checkPrecondFn := opts.CheckPrecondFn

	if ri == nil {
		return
	}

	ri.mu.Lock()
	defer ri.mu.Unlock()

	if success {
		ri.Bucket = bucket
		ri.Object = info.Name
	}
	ri.countItem(info.Size, info.DeleteMarker, success, attempt)
}

func (ri *batchJobInfo) trackCurrentBucketBatch(bucket string, batch []ObjectInfo) {
	if ri == nil {
		return
	}

	ri.mu.Lock()
	defer ri.mu.Unlock()

	ri.Bucket = bucket

            var startDate = this.startDate;
            if (!this.endDate) {
                this.container.find('.drp-calendar tbody td').each(function(index, el) {

                    //skip week numbers, only look at dates
                    if ($(el).hasClass('week')) return;

                    var title = $(el).attr('data-title');
                    var row = title.substr(1, 1);