}
    }

    private void loadTestProperties() {
        // Load system.properties from test classpath
        try (InputStream is = Thread.currentThread().getContextClassLoader().getResourceAsStream("system.properties")) {
            if (is != null) {
                Properties props = new Properties();
                props.load(is);

# Description:
# TensorFlow is a computational framework, primarily for use in machine
# learning applications.

load("@rules_cc//cc:cc_import.bzl", "cc_import")
load("@bazel_skylib//lib:selects.bzl", "selects")
load("@bazel_skylib//rules:common_settings.bzl", "bool_flag", "bool_setting")
load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda")
load(
    "//tensorflow:tensorflow.bzl",
    "VERSION",
    "VERSION_MAJOR",
    "check_deps",

	if objAPI == nil {
		return errServerNotInitialized
	}

	sys.objAPI = objAPI

	// Load bucket metadata sys.
	sys.init(ctx, buckets)
	return nil
}

// concurrently load bucket metadata to speed up loading bucket metadata.
func (sys *BucketMetadataSys) concurrentLoad(ctx context.Context, buckets []string) {
	g := errgroup.WithNErrs(len(buckets))

	return readServerConfig(GlobalContext, objAPI, nil)
}

// loadConfig - loads a new config from disk, overrides params
// from env if found and valid
// data is optional. If nil it will be loaded from backend.
func loadConfig(objAPI ObjectLayer, data []byte) error {
	bootstrapTraceMsg("load the configuration")
	srvCfg, err := readServerConfig(GlobalContext, objAPI, data)
	if err != nil {
		return err

				break
			}
		}
		mappedPolicy, ok := cache.iamUserPolicyMap.Load(name)
		if !ok {
			mappedPolicy, ok = cache.iamSTSPolicyMap.Load(name)
		}
		if !ok {
			// Attempt to load parent user mapping for STS accounts
			store.loadMappedPolicy(context.TODO(), name, stsUser, false, cache.iamSTSPolicyMap)
			mappedPolicy, ok = cache.iamSTSPolicyMap.Load(name)
			if !ok {
				return u, errNoSuchUser
			}
		}

			group := path.Dir(item)
			if err := iamOS.loadGroup(ctx, group, cache.iamGroupsMap); err != nil && err != errNoSuchGroup {
				return fmt.Errorf("unable to load the group: %w", err)
			}
		}
		if took := time.Since(groupsLoadStartTime); took > maxIAMLoadOpTime {
			logger.Info("Groups load took %.2fs (for %d items)", took.Seconds(), len(groupsList))
		}
	}

	bootstrapTraceMsgFirstTime("loading user policy mapping")

MINIO_IDENTITY_OPENID_REDIRECT_URI="http://127.0.0.1:10000/oauth_callback"
MINIO_IDENTITY_OPENID_CLAIM_NAME="groups"
```
</details>

### Redirection from OpenID Provider

func (sys *IAMSys) Initialized() bool {
	if sys == nil {
		return false
	}
	sys.Lock()
	defer sys.Unlock()
	return sys.store != nil
}

// Load - loads all credentials, policies and policy mappings.
func (sys *IAMSys) Load(ctx context.Context, firstTime bool) error {
	loadStartTime := time.Now()
	err := sys.store.LoadIAMCache(ctx, firstTime)
	if err != nil {

#
# The cascade of load() statements and tf_workspace?() calls works around the
# restriction that load() statements need to be at the top of .bzl files.
# E.g. we can not retrieve a new repository with http_archive and then load()
# a macro from that repository in the same file.
load("@//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

### Server Memory { #server-memory }

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨