}
		return anonDrives
	}

	go func() {
		defer xioutil.SafeClose(healthInfoCh)

		partialWrite(healthInfo) // Write first message with only version and deployment id populated
		getAndWritePlatformInfo()
		getAndWriteCPUs()
		getAndWritePartitions()
		getAndWriteNetInfo()
		getAndWriteOSInfo()
		getAndWriteMemInfo()
		getAndWriteProcInfo()
		getAndWriteMinioConfig()

		objects[i] = deleteObjectsReq.Objects[i].ObjectV
	}

	// Make sure to update context to print ObjectNames for multi objects.
	ctx = updateReqContext(ctx, objects...)

	// Call checkRequestAuthType to populate ReqInfo.AccessKey before GetBucketInfo()
	// Ignore errors here to preserve the S3 error behavior of GetBucketInfo()
	checkRequestAuthType(ctx, r, policy.DeleteObjectAction, bucket, "")

	for _, v := range policy.DefaultPolicies {
		if _, ok := policies[v.Name]; !ok {
			policies[v.Name] = defaultPolicyDoc(v.Definition)
		}
	}
}

// LoadIAMCache reads all IAM items and populates a new iamCache object and
// replaces the in-memory cache object.
func (store *IAMStoreSys) LoadIAMCache(ctx context.Context, firstTime bool) error {
	bootstrapTraceMsgFirstTime := func(s string) {
		if firstTime {

	// Do the same for inline data
	dirs, err := xl.data.list()
	if err != nil {
		return err
	}

	// Clear and repopulate
	for k := range foundDirs {
		delete(foundDirs, k)
	}

	// Populate into map
	for _, k := range dirs {
		foundDirs[k] = struct{}{}
	}

	// Delete all directories we expect to be there.
	for _, dir := range wantDirs {
		delete(foundDirs, dir)
	}

Conversely, when comparing types, a known type argument
(a type argument for which a map entry already exists)
takes the place of its corresponding type parameter.
As type inference progresses, the map is populated more and more
until all equations have been considered, or until unification fails.
Type inference succeeds if no unification step fails and the map has
an entry for each type parameter.
</p>

<p>

        Cloud TPUs.
    *   Support for `.fit`, `.evaluate`, `.predict` on TPU using numpy data, in
        addition to `tf.data.Dataset`.
    *   Keras reference implementations for many popular models are available in
        the TensorFlow
        [Model Garden](https://github.com/tensorflow/models/tree/master/official).
*   `tf.data`
    *   Changes rebatching for `tf.data datasets` + DistributionStrategy for