Lifecycle transition rules can be applied to buckets (both versioned and un-versioned) by specifying the tier name defined above as the transition storage class for the lifecycle rule.

## Implementation

    extends AbstractMapBasedMultimap<K, V> implements SetMultimap<K, V> {
  /**
   * Creates a new multimap that uses the provided map.
   *
   * @param map place to store the mapping from each key to its corresponding values
   */
  protected AbstractSetMultimap(Map<K, Collection<V>> map) {
    super(map);
  }

  @Override
  abstract Set<V> createCollection();

 * Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public
 * License along with this library; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 */
package jcifs;


import java.net.InetAddress;
import java.net.UnknownHostException;


/**
 * 
 * This is an internal API for resolving names
 * 

 * Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public
 * License along with this library; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 */
package jcifs.tests;


import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.fail;

    assert response.headers["WWW-Authenticate"] == 'Bearer scope="me"'


@needs_py39
def test_token_inactive_user(client: TestClient):
    access_token = get_access_token(
        username="alice", password="secretalice", scope="me", client=client
    )
    response = client.get(
        "/users/me", headers={"Authorization": f"Bearer {access_token}"}
    )
    assert response.status_code == 400, response.text

				enableSha256:    opts.enableSha256,
				enableMultipart: opts.enableMultipart,
				creds:           opts.creds,
			}

			results := globalNotificationSys.SpeedTest(ctx, sopts)
			sort.Slice(results, func(i, j int) bool {
				return results[i].Endpoint < results[j].Endpoint
			})

			totalPut := uint64(0)
			totalGet := uint64(0)
			for _, result := range results {
				totalPut += result.Uploads

```Python hl_lines="2  7 8 9 10  23 24 25"
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: bool = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):

* **Deep Learning**: Esse é um subcampo do Machine Learning, então o mesmo se aplica. A diferença é que não há apenas uma grande folha de papel com números para multiplicar, mas um grande conjunto de folhas de papel, e em muitos casos, você utiliza um processador especial para construir e/ou usar modelos.

       *   *other* paths we process will be descendants of that. That leaves only the original path
       *   argument for us to consider. And the only place we call pathNotFound is from
       *   throwDeleteFailed, and the other place that we call throwDeleteFailed inside
       *   deleteDirectoryContents is when an exception is thrown during the recursive steps. Any

       *   *other* paths we process will be descendants of that. That leaves only the original path
       *   argument for us to consider. And the only place we call pathNotFound is from
       *   throwDeleteFailed, and the other place that we call throwDeleteFailed inside
       *   deleteDirectoryContents is when an exception is thrown during the recursive steps. Any