EXPECT_EQ(string("Function 'MyFunc' has no attr named 'foo_attr'."),
            string(TF_Message(s_)));
  TF_DeleteBuffer(attr_buf);

  // Set attr
  tensorflow::AttrValue attr;
  attr.set_s("test_attr_value");
  string bytes;
  attr.SerializeToString(&bytes);
  TF_FunctionSetAttrValueProto(func_, "test_attr_name", bytes.data(),
                               bytes.size(), s_);

// StorageInfo - combines output of StorageInfo across all erasure coded object sets.
func (s *erasureSets) StorageInfo(ctx context.Context) StorageInfo {
	var storageInfo madmin.StorageInfo

	storageInfos := make([]madmin.StorageInfo, len(s.sets))

	g := errgroup.WithNErrs(len(s.sets))
	for index := range s.sets {
		g.Go(func() error {
			storageInfos[index] = s.sets[index].StorageInfo(ctx)
			return nil
		}, index)
	}

	tmpFormat.Erasure.This = ""
	if len(reference.Erasure.Sets) != len(format.Erasure.Sets) {
		return fmt.Errorf("Expected number of sets %d, got %d", len(reference.Erasure.Sets), len(format.Erasure.Sets))
	}

	// Make sure that the sets match.
	for i := range reference.Erasure.Sets {
		if len(reference.Erasure.Sets[i]) != len(format.Erasure.Sets[i]) {
			return fmt.Errorf("Each set should be of same size, expected %d got %d",

	}
	if len(formatV3.Erasure.Sets) != 1 {
		t.Fatalf("expected single set after migrating from v1 to v3, but found %d", len(formatV3.Erasure.Sets))
	}
	if !reflect.DeepEqual(formatV3.Erasure.Sets[0], m.Erasure.JBOD) {
		t.Fatalf("expected drive uuid: %v, got: %v", m.Erasure.JBOD, formatV3.Erasure.Sets[0])
	}

	m = &formatErasureV1{}
	m.Format = "unknown"

    /**
     * Gets the source of the POM to process.
     *
     * @return The source of the POM or {@code null} if not set.
     */
    ModelSource getModelSource();

    /**
     * Sets the source of the POM to process. Eventually, either {@link #setModelSource(ModelSource)} or
     * {@link #setPomFile(File)} must be set.
     *
     * @param modelSource The source of the POM to process, may be {@code null}.

    /**
     * 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.

	if err != nil {
		t.Fatal(err)
	}

	// Cleanup backend directories.
	defer obj.Shutdown(t.Context())
	defer removeRoots(fsDirs)

	z := obj.(*erasureServerPools)
	sets := z.serverPools[0]
	xl := sets.sets[0]

	origErasureDisks := xl.getDisks()

	testCases := []struct {
		bucket    string
		versioned bool
		object    string
		content   []byte
	}{

    ProjectBuildingRequest setPluginArtifactRepositories(List<ArtifactRepository> pluginArtifactRepositories);

    List<ArtifactRepository> getPluginArtifactRepositories();

    /**
     * Sets the system properties to use for interpolation and profile activation. The system properties are collected
     * from the runtime environment like {@link System#getProperties()} and environment variables.
     *

	return endpoints
}

// Get returns the sets representation of the endpoints
// this function also intelligently decides on what will
// be the right set size etc.
func (s endpointSet) Get() (sets [][]string) {
	k := uint64(0)
	endpoints := s.getEndpoints()
	for i := range s.setIndexes {
		for j := range s.setIndexes[i] {
			sets = append(sets, endpoints[k:s.setIndexes[i][j]+k])
			k = s.setIndexes[i][j] + k