public void remove() {
                      iterator.remove();
                    }

                    @Override
                    public boolean hasNext() {
                      return iterator.hasNext();
                    }

                    @Override
                    public Entry<String, String> next() {
                      return transform(iterator.next());

        tempFile.deleteOnExit();
        markForDeletion(tempFile);

        return tempFile;
    }

    public void cleanUp() throws IOException {
        for (Iterator it = filesToDelete.iterator(); it.hasNext(); ) {
            File file = (File) it.next();

            if (file.exists()) {
                if (file.isDirectory()) {
                    FileUtils.deleteDirectory(file);
                } else {

   
   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.

import com.google.errorprone.annotations.CanIgnoreReturnValue;
import java.lang.reflect.Method;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Enumeration;
import java.util.HashSet;
import java.util.LinkedHashSet;
import java.util.List;
import java.util.Set;
import java.util.logging.Logger;
import junit.framework.Test;
import junit.framework.TestCase;
import junit.framework.TestSuite;

                    public void remove() {
                      iterator.remove();
                    }

                    @Override
                    public boolean hasNext() {
                      return iterator.hasNext();
                    }

                    @Override
                    public Entry<String, String> next() {
                      return transform(iterator.next());

    protected void pushUserProperties(C context) throws Exception {
        ProtoSession protoSession = context.protoSession;
        HashSet<String> sys = new HashSet<>(protoSession.getSystemProperties().keySet());
        if (context.pushedUserProperties == null) {
            context.pushedUserProperties = new HashSet<>();
            protoSession.getUserProperties().entrySet().stream()
                    .filter(k -> !sys.contains(k.getKey()))

   * result of a <i>future</i> call to {@code next()}.
   */
  protected abstract @Nullable T computeNext(T previous);

  @Override
  public final boolean hasNext() {
    return nextOrNull != null;
  }

  @Override
  public final T next() {
    if (nextOrNull == null) {
      throw new NoSuchElementException();
    }
    T oldNext = nextOrNull;

specific versions. This is necessary to ensure reproducibility of the build.
The pinned versions of the full transitive closure of TensorFlow's dependencies
together with their corresponding hashes are specified in
`requirements_lock_<python version>.txt` files (e.g.
`requirements_lock_3_12.txt` for `Python 3.12`).

To update the lock files, make sure

        try {
            return MessageDigest.getInstance(algorithm);
        } catch (final NoSuchAlgorithmException e) {
            throw new NoSuchAlgorithmRuntimeException(e);
        }
    }

    /**
     * Hashes the specified text using the given algorithm and converts it to a string.
     *
     * @param algorithm
     *            The algorithm. Must not be {@literal null} or an empty string.
     * @param text

        assertFalse(set.isEmpty(), "No Artifacts");
        Iterator iter = set.iterator();
        assertTrue(set.size() == 4, "Set size should be 4, is " + set.size());

        while (iter.hasNext()) {
            Artifact artifact = (Artifact) iter.next();
            System.out.println("Artifact: " + artifact.getDependencyConflictId() + " " + artifact.getVersion()