func NewStandardClaims() *StandardClaims {
	return &StandardClaims{}
}

// SetIssuer sets issuer for these claims
func (c *StandardClaims) SetIssuer(issuer string) {
	c.Issuer = issuer
}

// SetAudience sets audience for these claims
func (c *StandardClaims) SetAudience(aud string) {
	c.Audience = aud
}

// SetExpiry sets expiry in unix epoch secs
func (c *StandardClaims) SetExpiry(t time.Time) {

    /**
     * Gets the servers whose passwords should be decrypted.
     *
     * @return The servers to decrypt, never {@code null}.
     */
    List<Server> getServers();

    /**
     * Sets the servers whose passwords should be decrypted.
     *
     * @param servers The servers to decrypt, may be {@code null}.
     * @return This request, never {@code null}.
     */

		labels := []string{poolIDL, poolLV, setIDL, setLV}
		m.Set(erasureSetReadQuorum, float64(h.ReadQuorum), labels...)
		m.Set(erasureSetWriteQuorum, float64(h.WriteQuorum), labels...)
		m.Set(erasureSetOnlineDrivesCount, float64(h.HealthyDrives), labels...)
		m.Set(erasureSetHealingDrivesCount, float64(h.HealingDrives), labels...)
		m.Set(erasureSetHealth, b2f(h.Healthy), labels...)

		readHealthy := true

        getSet().equals(MinimalSet.from(fewerElements)));
  }

  public void testEquals_largerSet() {
    Collection<E> moreElements = getSampleElements(getNumElements() + 1);
    assertFalse(
        "Sets of different sizes should not be equal.",
        getSet().equals(MinimalSet.from(moreElements)));
  }

  public void testEquals_list() {

        getSet().equals(MinimalSet.from(fewerElements)));
  }

  public void testEquals_largerSet() {
    Collection<E> moreElements = getSampleElements(getNumElements() + 1);
    assertFalse(
        "Sets of different sizes should not be equal.",
        getSet().equals(MinimalSet.from(moreElements)));
  }

  public void testEquals_list() {

   
   
   *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.

        }
    }

    /**
     * Returns the new token.
     *
     * @return The new token.
     */
    public String getNewToken() {
        return newToken;
    }

    /**
     * Sets the new token.
     *
     * @param newToken The new token.
     */
    public void setNewToken(final String newToken) {
        this.newToken = newToken;
    }

    /**
     * Returns the new segmentation.

                return new ArrayList<>();
            }
            return new ArrayList<>(messages);
        }
    }

    /**
     * Sets the message list for this session.
     *
     * @param messages the messages to set
     */
    public void setMessages(final List<ChatMessage> messages) {
        synchronized (messagesLock) {
            if (messages == null) {

     * @return the lease key
     */
    public Smb2LeaseKey getLeaseKey() {
        return leaseKey;
    }

    /**
     * Sets the lease key for this V2 lease request
     * @param leaseKey the lease key to set
     */
    public void setLeaseKey(Smb2LeaseKey leaseKey) {
        this.leaseKey = leaseKey;
    }

    /**
     * Gets the requested lease state flags for V2

     *
     * @return the sortPrefix
     */
    public String getSortPrefix() {
        return sortPrefix;
    }

    /**
     * Sets the sort prefix used for identifying sort parameters in search queries.
     *
     * @param sortPrefix the sortPrefix to set
     */
    public void setSortPrefix(final String sortPrefix) {
        this.sortPrefix = sortPrefix;
    }

    /**