import com.google.common.util.concurrent.Service.State;

/**
 * Superinterface of {@link ServiceManager} to introduce a bridge method for {@code
 * servicesByState()}, to ensure binary compatibility with older Guava versions that specified
 * {@code servicesByState()} to return {@code ImmutableMultimap}.
 */
@J2ktIncompatible
@GwtIncompatible
interface ServiceManagerBridge {
  ImmutableMultimap<State, Service> servicesByState();

                        level: "warning",
                        message: "Authorization may be unsafe, passed state was changed in server. The passed state wasn't returned from auth server."
                    });
                }

                if (qp.code) {
                    delete oauth2.state;
                    oauth2.auth.code = qp.code;

     * the lock to check again. Since an IDLE state was observed inside the preceding synchronized
     * block, and reference field assignment is atomic, this may save reacquiring the lock when
     * another thread or the worker task has cleared the count and set the state.
     *
     * <p>When {@link #executor} is a directExecutor(), the value written to

     * the lock to check again. Since an IDLE state was observed inside the preceding synchronized
     * block, and reference field assignment is atomic, this may save reacquiring the lock when
     * another thread or the worker task has cleared the count and set the state.
     *
     * <p>When {@link #executor} is a directExecutor(), the value written to

                    final String code = request.getParameter("code");
                    final String reqState = request.getParameter("state");
                    if (logger.isDebugEnabled()) {
                        logger.debug("code: {}, state(request): {}, state(session): {}", code, reqState, sesState);
                    }
                    if (sesState.equals(reqState) && StringUtil.isNotBlank(code)) {

abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing...

		n := cnt[key]
		cnt[key]++
		key = fmt.Sprintf("sys_%s_%d", key, n)
		res[key] = t.Stats()
	}

	for _, t := range audit {
		key := strings.ToLower(t.Type().String())
		n := cnt[key]
		cnt[key]++
		key = fmt.Sprintf("audit_%s_%d", key, n)
		res[key] = t.Stats()
	}

	return res
}

// AddSystemTarget adds a new logger target to the
// list of enabled loggers

	# slow header writing
	send_put_object_request 20 0 && exit -1
	"${PWD}/mc" stat minio/testbucket/testobject && exit -1

	# quick header write and slow bodywrite
	send_put_object_request 0 40 && exit -1
	"${PWD}/mc" stat minio/testbucket/testobject && exit -1

	# quick header and body write
	send_put_object_request 1 1 || exit -1
	"${PWD}/mc" stat minio/testbucket/testobject || exit -1
}

function main() {

        assertEquals(expectedIndexNumber, info2.getIndexNumber());
    }

    @Test
    @DisplayName("Test encode with initial state")
    void testEncodeWithInitialState() {
        // Test encode without prior decode (initial state with indexNumber = 0)
        byte[] destinationBuffer = new byte[8];
        int bytesEncoded = fileInternalInfo.encode(destinationBuffer, 0);

        // Verify

        // this getUnchecked() call shouldn't be a cache miss; verified below
        assertThat(cache.getUnchecked(entry.getKey())).isEqualTo(entry.getValue());
      }
      assertThat(cache.stats().missCount()).isEqualTo(WARMUP_SIZE);
      checkValidState(cache);
    }
  }

  public void testPut_populated() {
    for (LoadingCache<Object, Object> cache : caches()) {
      // don't let the entries get GCed