}

// load the cache content with name from minioMetaBackgroundOpsBucket.
// Only backend errors are returned as errors.
// The loader is optimistic and has no locking, but tries 5 times before giving up.
// If the object is not found, a nil error with empty data usage cache is returned.
func (d *dataUsageCache) load(ctx context.Context, store objectIO, name string) error {

    </default>
  </defaults>

  <classes>
    <class rootElement="true" xml.tagName="extensions" xsd.compositor="sequence">
      <name>CoreExtensions</name>
      <description>Extensions to load.</description>
      <version>1.0.0+</version>
      <fields>
        <field>
          <name>extensions</name>
          <description>A set of build extensions to use from this project.</description>

   
   
   *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)

# Comma-separated list of files to include.
# Each item may be enclosed in quotes to gracefully include spaces. Items are trimmed before being loaded.
# If the first character of an item is a question mark, the load will silently fail if the file does not exist.
${includes} = ?"${maven.user.conf}/maven-system.properties", \
              ?"${maven.project.conf}/maven-system.properties"

#
# Settings
#

#

# Comma-separated list of files to include.
# Each item may be enclosed in quotes to gracefully include spaces. Items are trimmed before being loaded.
# If the first character of an item is a question mark, the load will silently fail if the file does not exist.
${includes} = ?"${maven.user.conf}/maven-user.properties", \
              ?"${maven.project.conf}/maven-user.properties"

#
# Maven Cache Configuration
#

            }
          }
        };

    CacheLoader<Integer, AtomicInteger> loader =
        new CacheLoader<Integer, AtomicInteger>() {
          @Override
          public AtomicInteger load(Integer key) {
            applyCount.incrementAndGet();
            return new AtomicInteger();
          }
        };

    LoadingCache<Integer, AtomicInteger> cache =
        CacheBuilder.newBuilder()