/**
     * Gets the global settings file.
     *
     * @return The global settings file or {@code null} if none.
     */
    File getGlobalSettingsFile();

    /**
     * Sets the global settings file. A non-existent settings file is equivalent to empty settings. If both user
     * settings and global settings are given, the user settings take precedence.
     *

  @Override
  NavigableSet<Integer> createUnfiltered(Iterable<Integer> contents) {
    return Sets.newTreeSet(contents);
  }

  @Override
  NavigableSet<Integer> filter(
      NavigableSet<Integer> elements, Predicate<? super Integer> predicate) {
    return Sets.filter(elements, predicate);
  }

	trustDomain string
	SecretMeta
}

// Name sets the name field on a secretItemBuilder
func (s *secretItemBuilder) Name(name string) SecretItemBuilder {
	s.name = name
	return s
}

// Data sets the data field on a secretItemBuilder
func (s *secretItemBuilder) Data(data string) SecretItemBuilder {
	s.data = data
	return s
}

// Source sets the source field on a secretItemBuilder

    assertThat(network).isNotEqualTo(g2);
  }

  // Node sets are the same, but edge sets differ.
  @Test
  public void equivalent_edgeSetsDiffer() {
    network.addEdge(N1, N2, E12);

    MutableNetwork<Integer, String> g2 = createNetwork(edgeType);
    g2.addEdge(N1, N2, E13);

    assertThat(network).isNotEqualTo(g2);
  }

  // Node/edge sets are the same, but node/edge connections differ due to edge type.
  @Test

    assertThat(network).isNotEqualTo(g2);
  }

  // Node sets are the same, but edge sets differ.
  @Test
  public void equivalent_edgeSetsDiffer() {
    network.addEdge(N1, N2, E12);

    MutableNetwork<Integer, String> g2 = createNetwork(edgeType);
    g2.addEdge(N1, N2, E13);

    assertThat(network).isNotEqualTo(g2);
  }

  // Node/edge sets are the same, but node/edge connections differ due to edge type.
  @Test

   
   
   *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.

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.google.common.collect;

import static com.google.common.collect.Sets.newHashSet;

import com.google.common.base.Predicate;
import com.google.common.collect.FilteredCollectionsTestUtil.AbstractFilteredSetTest;
import java.util.Set;

    /**
     * Gets the bean to configure. Eventually, a valid request must have a bean set.
     *
     * @return The bean to configure, or {@code null} if none.
     */
    Object getBean();

    /**
     * Sets the bean to configure. Eventually, a valid request must have a bean set.
     *
     * @param bean The bean to configure, may be {@code null}.
     * @return This request for chaining, never {@code null}.
     */

    private File projectDirectory;

    @Override
    public List<String> getActiveProfileIds() {
        return activeProfileIds;
    }

    /**
     * Sets the identifiers of those profiles that should be activated by explicit demand.
     *
     * @param activeProfileIds The identifiers of those profiles to activate, may be {@code null}.
     * @return This context, never {@code null}.

      this.taskRunner = okHttpClient.taskRunner
    }

    /**
     * Sets the dispatcher used to set policy and execute asynchronous requests. Must not be null.
     */
    fun dispatcher(dispatcher: Dispatcher) =
      apply {
        this.dispatcher = dispatcher
      }

    /**
     * Sets the connection pool used to recycle HTTP and HTTPS connections.
     *