*/
  @Override
  public ImmutableSet<K> keySet() {
    ImmutableSet<K> result = keySet;
    return (result == null) ? keySet = createKeySet() : result;
  }

  /*
   * This could have a good default implementation of return new ImmutableKeySet<K, V>(this),
   * but ProGuard can't figure out how to eliminate that default when RegularImmutableMap
   * overrides it.
   */
  abstract ImmutableSet<K> createKeySet();

	})
	if err != nil {
		return types.NamespacedName{}, err
	}
	pods := slices.Reference(podsr.Items)
	if len(pods) > 0 {
		// We need to pass in a sorter, and the one used by `kubectl logs` is good enough.
		sortBy := func(pods []*corev1.Pod) sort.Interface { return podutils.ByLogging(pods) }
		sort.Sort(sortBy(pods))
		return config.NamespacedName(pods[0]), nil
	}

	bucket, path  string
	recursive     bool

	// Only return results with this prefix.
	filterPrefix string

	// Forward to this prefix before returning results.
	forwardTo string

	// Minimum number of good disks to continue.
	// An error will be returned if this many disks returned an error.
	minDisks       int
	reportNotFound bool

	// perDiskLimit will limit each disk to return n objects.

Containers are mainly a tool to simplify the process of **building and deploying** an application, but they don't enforce a particular approach to handle these **deployment concepts**, and there are several possible strategies.

The **good news** is that with each different strategy there's a way to cover all of the deployment concepts. 🎉

Let's review these **deployment concepts** in terms of containers:

* HTTPS
* Running on startup
* Restarts

	letterIdxMax  = 63 / letterIdxBits   // # of letter indices fitting in 63 bits
)

// Random number state.
// We generate random temporary file names so that there's a good
// chance the file doesn't exist yet.
var (
	randN  uint32
	randmu sync.Mutex
)

// Temp files created in default Tmp dir
var globalTestTmpDir = os.TempDir()

			return fmt.Errorf("bucket replication config validation error: %#v", apiErr)
		}
		err = newReplicationConfig.Validate(bucket, sameTarget)
		if err != nil {
			return err
		}
		// Config looks good, so we save it.
		replCfgData, err := xml.Marshal(newReplicationConfig)
		if err != nil {
			return err
		}

		_, err = globalBucketMetadataSys.Update(ctx, bucket, bucketReplicationConfig, replCfgData)

        return this.bimap.equals(that.bimap);
      }
      return false;
    }

    @Override
    public int hashCode() {
      return bimap.hashCode();
    }

    // There's really no good way to implement toString() without printing the entire BiMap, right?
    @Override
    public String toString() {
      return "Maps.asConverter(" + bimap + ")";
    }

    which is a NumPy-compatible API for writing TF programs. This module
    provides class `ndarray`, which mimics the `ndarray` class in NumPy, and
    wraps an immutable `tf.Tensor` under the hood. A subset of NumPy functions
    (e.g. `numpy.add`) are provided. Their inter-operation with TF facilities is
    seamless in most cases. See

### Migration

Check out the [Pydantic migration guide](https://docs.pydantic.dev/2.0/migration/).