System.arraycopy(src, srcIndex + namesLength, this.macAddress, 0, 6);
        srcIndex += readNodeNameArray(src, srcIndex);
        this.stats = new byte[statsLength];
        System.arraycopy(src, srcIndex, this.stats, 0, statsLength);
        srcIndex += statsLength;
        return srcIndex - start;
    }

    private int readNodeNameArray(final byte[] src, int srcIndex) {

		adminRouter.Methods(http.MethodGet).Path(adminVersion + "/tier/{tier}").HandlerFunc(adminMiddleware(adminAPI.VerifyTierHandler))
		// Tier stats
		adminRouter.Methods(http.MethodGet).Path(adminVersion + "/tier-stats").HandlerFunc(adminMiddleware(adminAPI.TierStatsHandler))

		// Cluster Replication APIs

        return parentKey + nestedKeySeparator + currentKey;
    }

    /**
     * Sets the maximum depth for nested structure extraction.
     *
     * @param maxDepth the maximum depth
     */
    public void setMaxDepth(final int maxDepth) {
        this.maxDepth = maxDepth;
    }

    /**
     * Sets the field separator for key-value pairs.
     *
     * @param fieldSeparator the field separator

    /**
     * Sets the index name.
     * @param index The index name.
     */
    public void setIndex(final String index) {
        this.index = index;
    }

    /**
     * Sets the size of results.
     * @param size The size.
     */
    public void setSize(final int size) {
        this.size = size;
    }

    /**
     * Sets the query string.

    /**
     * Gets the project to resolve dependencies for.
     *
     * @return The project to resolve dependencies for or {@code null} if not set.
     */
    MavenProject getMavenProject();

    /**
     * Sets the project to resolve dependencies for.
     *
     * @param project The project to resolve dependencies for, may be {@code null}.
     * @return This request for chaining, never {@code null}.
     */

   
   
   *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.

    }

    /**
     * Gets the component name.
     *
     * @return The component name
     */
    public String getComponentName() {
        return componentName;
    }

    /**
     * Sets the component name.
     *
     * @param componentName The component name to set
     */
    public void setComponentName(final String componentName) {
        this.componentName = componentName;
    }

// consistently for a given object name.
func TestHashedLayer(t *testing.T) {
	// Test distribution with 16 sets.
	var objs [16]*erasureObjects
	for i := range objs {
		objs[i] = &erasureObjects{}
	}

	sets := &erasureSets{sets: objs[:], distributionAlgo: "CRCMOD"}

	testCases := []struct {
		objectName  string
		expectedObj *erasureObjects
	}{

     */
    public String getEncoding() {
        return encoding;
    }

    /**
     * Sets the encoding for the response content.
     *
     * @param encoding the encoding to set.
     */
    public void setEncoding(final String encoding) {
        this.encoding = encoding;
    }

    /**
     * Sets the exception that occurred while accessing the content.
     *
     * @param e the exception to set.

  }

  /**
   * Atomically sets the element at position {@code i} to the given value.
   *
   * @param i the index
   * @param newValue the new value
   */
  public final void set(int i, double newValue) {
    long next = doubleToRawLongBits(newValue);
    longs.set(i, next);
  }

  /**
   * Eventually sets the element at position {@code i} to the given value.
   *