*
   * This method loads entire response body into memory. If the response body is very large this
   * may trigger an [OutOfMemoryError]. Prefer to stream the response body if this is a
   * possibility for your response.
   */
  @Throws(IOException::class)
  fun bytes() = commonBytes()

  /**
   * Returns the response as a [ByteString].
   *

    throw new AssertionError();
  }

  /** Loads Finalizer.class. */
  interface FinalizerLoader {

    /**
     * Returns Finalizer.class or null if this loader shouldn't or can't load it.
     *
     * @throws SecurityException if we don't have the appropriate privileges
     */
    @CheckForNull
    Class<?> loadFinalizer();
  }

  /**

    throw new AssertionError();
  }

  /** Loads Finalizer.class. */
  interface FinalizerLoader {

    /**
     * Returns Finalizer.class or null if this loader shouldn't or can't load it.
     *
     * @throws SecurityException if we don't have the appropriate privileges
     */
    @CheckForNull
    Class<?> loadFinalizer();
  }

  /**

        Doc(
            """
            Load and use Google Fonts.
            """
        ),
    ] = True,
) -> HTMLResponse:
    """
    Generate and return the HTML response that loads ReDoc for the alternative
    API docs (normally served at `/redoc`).

    You would only call this function yourself if you needed to override some parts,
    for example the URLs to use to load ReDoc's JavaScript and CSS.

	buckets     []string
}

// NewMetricsGroup creates a new MetricsGroup. To create a metrics group for
// metrics with a `bucket` dimension (label), use `NewBucketMetricsGroup`.
//
// The `loader` function loads metrics from the cache and the system.
func NewMetricsGroup(path collectorPath, descriptors []MetricDescriptor,
	loader MetricsLoaderFn,
) *MetricsGroup {
	mg := &MetricsGroup{
		CollectorPath: path,

### 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. 🚨

func (b *BucketMetadata) SetCreatedAt(createdAt time.Time) {
	if b.Created.IsZero() {
		b.Created = UTCNow()
	}
	if !createdAt.IsZero() {
		b.Created = createdAt.UTC()
	}
}

// Load - loads the metadata of bucket by name from ObjectLayer api.
// If an error is returned the returned metadata will be default initialized.
func readBucketMetadata(ctx context.Context, api ObjectLayer, name string) (BucketMetadata, error) {

    // sepLoader is the loader that we will use to load the parallel FinalizableReferenceQueue (FRQ)
    // and friends, and that we will eventually expect to see garbage-collected. The assumption
    // is that the ClassLoader of this test is a URLClassLoader, and that it loads FRQ itself
    // rather than delegating to a parent ClassLoader. If this assumption is violated the test will

    // sepLoader is the loader that we will use to load the parallel FinalizableReferenceQueue (FRQ)
    // and friends, and that we will eventually expect to see garbage-collected. The assumption
    // is that the ClassLoader of this test is a URLClassLoader, and that it loads FRQ itself
    // rather than delegating to a parent ClassLoader. If this assumption is violated the test will

  public void testGetChecked_classUnloading() throws Exception {
    WeakReference<?> classUsedByGetChecked = doTestClassUnloading();
    GcFinalization.awaitClear(classUsedByGetChecked);
  }

  /**
   * Loads {@link WillBeUnloadedException} in a separate {@code ClassLoader}, calls {@code
   * getChecked(future, WillBeUnloadedException.class)}, and returns the loader. The caller can then