- Fix a bug in kube-proxy that caused it to crash when using load balancers with a different IP family ([#87117](https://github.com/kubernetes/kubernetes/pull/87117), [@aojea](https://github.com/aojea)) [SIG Network]

}

// LoadBalancerIngress represents the status of a load-balancer ingress point:
// traffic intended for the service should be sent to an ingress point.
message LoadBalancerIngress {
  // IP is set for load-balancer ingress points that are IP based
  // (typically GCE or OpenStack load-balancers)
  // +optional
  optional string ip = 1;

  // Hostname is set for load-balancer ingress points that are DNS based

        File pom0 = new File(localRepo, "p0/pom.xml");

        File pom0Basedir = pom0.getParentFile();

        File pom1 = new File(pom0Basedir, "p1/pom.xml");

        // load everything...
        MavenProject project0 = getProjectWithDependencies(pom0);
        MavenProject project1 = getProjectWithDependencies(pom1);

        assertEquals(pom0Basedir, project1.getParent().getBasedir());

    // Round down to the nearest power of 2.
    expectedEntries = max(expectedEntries, 2);
    int tableSize = Integer.highestOneBit(expectedEntries);
    // Check to make sure that we will not exceed the maximum load factor.
    if (expectedEntries > (int) (loadFactor * tableSize)) {
      tableSize <<= 1;
      return (tableSize > 0) ? tableSize : MAX_TABLE_SIZE;
    }
    return tableSize;
  }

BMP, GIF, WAV, RAW, RAW\_PADDED, CSV and PROTO formats. All other input formats,
including tensorflow-io should be sandboxed if used to process untrusted data.

For example, if an attacker were to upload a malicious video file, they could
potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.

  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)

    @PostConstruct
    public void init() {
        if (logger.isDebugEnabled()) {
            logger.debug("Initialize {}", this.getClass().getSimpleName());
        }
        load();
    }

    @Override
    public int load() {
        final List<LabelType> labelTypeList = ComponentUtil.getComponent(LabelTypeService.class).getLabelTypeList();
        buildLabelTypeItems(labelTypeList);

    </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.

	requestFormDataSize = 64 * humanize.MiByte

	// For any HTTP request, request body should be not more than 16GiB + requestFormDataSize
	// where, 16GiB is the maximum allowed object size for object upload.
	requestMaxBodySize = globalMaxObjectSize + requestFormDataSize

	// Maximum size for http headers - See: https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingMetadata.html
	maxHeaderSize = 8 * 1024