return globalLockServer
	}
	return newlockRESTClient(endpoint)
}

// Returns a lock rest client.
func newlockRESTClient(ep Endpoint) *lockRESTClient {
	return &lockRESTClient{globalLockGrid.Load().Connection(ep.GridHost())}

                return doc
            } catch (ClassDocGenerationException e) {
                throw e
            } catch (Exception e) {
                throw new ClassDocGenerationException("Could not load the class documentation for class '$className'.", e)
            }
        } finally {
            currentlyBuilding.removeLast()
        }
    }

    private File getFileForClass(String className) {

        repository.put('class', value)

        when:
        repository.store(file)
        def newRepo = new SimpleClassMetaDataRepository<TestDomainObject>()
        newRepo.load(file)

        then:
        newRepo.find('class') == value
    }
}

class TestDomainObject implements Attachable<TestDomainObject>, Serializable {
    def value

	req.Header.Set(xhttp.MinIOPeerCall, "true")

	resp, err := httpClient.Do(req)
	if err != nil {
		return err
	}
	xhttp.DrainBody(resp.Body)

	return nil
}

// connect to list of endpoints and load all Erasure disk formats, validate the formats are correct
// and are in quorum, if no formats are found attempt to initialize all of them for the first
// time. additionally make sure to close all the disks used in this attempt.

   
   
   *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.

            .concurrencyLevel(segments)
            .maximumSize(maximumSize)
            .build(
                new CacheLoader<Integer, Integer>() {
                  @Override
                  public Integer load(Integer from) {
                    return (int) misses.incrementAndGet();
                  }
                });

    // To start, fill up the cache.

        try (InputStream is = DefaultRuntimeInformation.class.getResourceAsStream("/" + resource)) {
            if (is != null) {
                props.load(is);
            } else {
                logger.warn("Could not locate " + resource + " on classpath, Maven runtime information not available");
            }
        } catch (IOException e) {

        return new Promise((resolve, reject) => {
            if (img.complete && img.naturalHeight !== 0) {
                resolve();
            } else {
                img.addEventListener('load', () => {
                    if (img.naturalHeight !== 0) {
                        resolve();
                    } else {
                        reject();
                    }
                });

  private GraphConstants() {}

  static final int EXPECTED_DEGREE = 2;

  static final int DEFAULT_NODE_COUNT = 10;
  static final int DEFAULT_EDGE_COUNT = DEFAULT_NODE_COUNT * EXPECTED_DEGREE;

  // Load factor and capacity for "inner" (i.e. per node/edge element) hash sets or maps
  static final float INNER_LOAD_FACTOR = 1.0f;
  static final int INNER_CAPACITY = 2; // ceiling(EXPECTED_DEGREE / INNER_LOAD_FACTOR)

marked `PENDING` or `FAILED` are re-queued for replication.

Replication speed depends on the cluster load, number of objects in the object store as well as storage speed. In addition, any bandwidth limits set via `mc admin bucket remote add` could also contribute to replication speed. The number of workers used for replication defaults to 100. Based on network bandwidth and system load, the number of workers used in replication can be configured using `mc admin config set alias api` to set the...