// AddPodToMesh adds a pod to mesh by
// 1. Getting the netns
// 2. Adding the pod's IPs to the hostnetns ipsets for node probe checks
// 3. Creating iptables rules inside the pod's netns
// 4. Notifying ztunnel via GRPC to create a proxy for the pod
//
// You may ask why we pass the pod IPs separately from the pod manifest itself (which contains the pod IPs as a field)

// AggregationRule describes how to locate ClusterRoles to aggregate into the ClusterRole
message AggregationRule {
  // ClusterRoleSelectors holds a list of selectors which will be used to find ClusterRoles and create the rules.
  // If any of the selectors match, then the ClusterRole's permissions will be added
  // +optional
  repeated k8s.io.apimachinery.pkg.apis.meta.v1.LabelSelector clusterRoleSelectors = 1;
}

  // previous fields.
  //
  // This is defined since CSI spec 1.4.0 as the largest size
  // that may be used in a
  // CreateVolumeRequest.capacity_range.required_bytes field to
  // create a volume with the same parameters as those in
  // GetCapacityRequest. The corresponding value in the Kubernetes
  // API is ResourceRequirements.Requests in a volume claim.
  //
  // +optional

			if z.checkIfRebalanceDone(poolIdx) {
				return
			}

			fivs, err := entry.fileInfoVersions(bucket)
			if err != nil {
				return
			}

			// We need a reversed order for rebalance,
			// to create the appropriate stack.
			versionsSorter(fivs.Versions).reverse()

			var rebalanced, expired int
			for _, version := range fivs.Versions {

                try {
                    return (Map<String, Object>) SerializeUtil.fromBinaryToObject(data);
                } catch (final Exception e) {
                    throw new CrawlerSystemException("Could not create an instance from bytes.", e);
                }
            }
            return null;
        } catch (final Exception e) {
            throw new CrawlingAccessException("Failed to parse " + url, e);
        }

    WaitOnRunService service = new WaitOnRunService();
    service.startAsync().awaitRunning();
    enterRun.await();
    service.stopAsync().awaitTerminated();
  }

  public void testTimeout() {
    // Create a service whose executor will never run its commands
    Service service =
        new AbstractExecutionThreadService() {
          @Override
          protected void run() throws Exception {}

          @Override

    }
  }

  public void testRejectedExecutionThrownWithMultipleCalls() throws Exception {
    final CountDownLatch latch = new CountDownLatch(1);
    final SettableFuture<?> future = SettableFuture.create();
    final Executor delegate =
        new Executor() {
          @Override
          public void execute(Runnable task) {
            if (future.set(null)) {
              awaitUninterruptibly(latch);

        checkNoConflictInValueBucket(value, entry, nextInValueBucket);
      } catch (BucketOverflowException e) {
        return JdkBackedImmutableBiMap.create(n, entryArray);
      }
      ImmutableMapEntry<K, V> newEntry =
          (nextInValueBucket == null && nextInKeyBucket == null)
              ? RegularImmutableMap.makeImmutable(entry, key, value)

///

## Import Pydantic's `BaseModel`

First, you need to import `BaseModel` from `pydantic`:

{* ../../docs_src/body/tutorial001_py310.py hl[2] *}

## Create your data model

Then you declare your data model as a class that inherits from `BaseModel`.

Use standard Python types for all the attributes:

{* ../../docs_src/body/tutorial001_py310.py hl[5:9] *}

      TFE_Op* op,
      const std::vector<absl::optional<std::vector<int64_t>>>& input_shapes_vec,
      const std::vector<TF_Tensor*>& input_tensors,
      const absl::optional<std::vector<int64_t>>& expected_shape) {
    // Create input_shapes.
    TF_ShapeAndTypeList* input_shapes =
        TF_NewShapeAndTypeList(input_shapes_vec.size());
    for (size_t i = 0; i < input_shapes_vec.size(); ++i) {
      const auto& input_shape = input_shapes_vec[i];