*
     * @return the highlighting type
     */
    public String getType() {
        return type;
    }

    /**
     * Sets the highlighting type with fluent interface.
     *
     * @param type the highlighting type to set
     * @return this HighlightInfo instance for method chaining
     */
    public HighlightInfo type(final String type) {
        this.type = type;
        return this;

        }

        /**
         * Sets the text content of the XML node.
         *
         * @param value the text content of the XML node
         * @return this builder instance
         */
        public Builder value(String value) {
            this.value = value;
            return this;
        }

        /**
         * Sets the namespace URI of the XML node.
         *

import com.google.common.annotations.GwtCompatible;
import com.google.common.collect.testing.SampleElements.Ints;
import java.util.List;
import java.util.Set;
import org.jspecify.annotations.NullMarked;

/**
 * Create integer sets for collection tests.
 *
 * @author Gregory Kick
 */
@GwtCompatible
@NullMarked
public abstract class TestIntegerSetGenerator implements TestSetGenerator<Integer> {

     * @return A <code>boolean</code> indicating whether the flag is set.
     */
    public boolean getFlag(final int flag) {
        return (getFlags() & flag) != 0;
    }

    /**
     * Sets or clears the specified flag.
     *
     * @param flag The flag to set/clear (i.e.,
     * <code>NTLMSSP_NEGOTIATE_OEM</code>).
     * @param value Indicates whether to set (<code>true</code>) or

    /**
     * Gets the global toolchains source.
     *
     * @return The global toolchains source or {@code null} if none.
     */
    Source getGlobalToolchainsSource();

    /**
     * Sets the global toolchains source. If both user toolchains and a global toolchains are given, the user toolchains
     * take precedence.
     *

   
   
   *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.

     * This method sets the job executor and then calls the abstract execute method.
     *
     * @param jobExecutor the job executor to use for execution
     * @return the execution result message or summary
     */
    public String execute(final JobExecutor jobExecutor) {
        jobExecutor(jobExecutor);
        return execute();
    }

    /**
     * Sets the job executor for this job.
     *

import com.google.common.annotations.GwtCompatible;
import com.google.common.collect.testing.SampleElements.Enums;
import java.util.List;
import java.util.Set;
import org.jspecify.annotations.NullMarked;

/**
 * An abstract TestSetGenerator for generating sets containing enum values.
 *
 * @author Kevin Bourrillion
 */
@GwtCompatible
@NullMarked

      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.
     *

        SmbException ex = new SmbException(0, root);
        assertSame(root, ex.getRootCause());
    }

    /**
     * Verify that the string-ctor sets status to {@code NT_STATUS_UNSUCCESSFUL}.
     */
    @Test
    @DisplayName("String constructor sets NT_STATUS_UNSUCCESSFUL status")
    void testStringConstructor() {
        SmbException ex = new SmbException("custom message");