}
	}
}

func buildByImageSize() ByImageSize {
	return []kubecontainer.Image{
		{
			ID:          "1",
			RepoTags:    []string{"foo-tag11", "foo-tag12"},
			RepoDigests: []string{"foo-rd11", "foo-rd12"},
			Size:        1,
		},
		{
			ID:          "2",
			RepoTags:    []string{"foo-tag21", "foo-tag22"},
			RepoDigests: []string{"foo-rd21", "foo-rd22"},

  Tensor values(DT_FLOAT, {vectorSize});
  tags.vec<tstring>()(0) = "tag1";
  tags.vec<tstring>()(1) = "tag2";
  tags.vec<tstring>()(2) = "tag3";
  values.vec<float>()(0) = 1.0f;
  values.vec<float>()(1) = -0.73f;
  values.vec<float>()(2) = 10000.0f;
  TestScalarSummaryOp(&tags, &values, R"(
                      value { tag: 'tag1' simple_value: 1.0 }
                      value { tag: 'tag2' simple_value: -0.73}

[compiler:gccgo] skip 'gccgo has no standard packages'
go build -tags 'tag1 tag2' math
! go build -tags 'tag1,tag2 tag3' math

			t.Errorf("Failed to auto migrate advanced user, got error %v", err)
		}
	}
}

func TestEmbeddedTagSetting(t *testing.T) {
	type Tag1 struct {
		Id int64 `gorm:"autoIncrement"`
	}
	type Tag2 struct {
		Id int64
	}

	type EmbeddedTag struct {
		Tag1 Tag1 `gorm:"Embedded;"`
		Tag2 Tag2 `gorm:"Embedded;EmbeddedPrefix:t2_"`
		Name string
	}

	DB.Migrator().DropTable(&EmbeddedTag{})

```

### Add suggest document

```java
String[][] readings = new String[2][];
readings[0] = new String[] { "kensaku", "fuga" };
readings[1] = new String[] { "enjin", "fuga" };
String[] tags = new String[] { "tag1", "tag2" };
String[] roles = new String[] { "role1", "role2", "role3" };
suggester.indexer().index(new SuggestItem(new String[] { "検索", "エンジン" }, readings, 1, tags, roles, SuggestItem.Kind.DOCUMENT));
```

                createDefaultReadingConverter(), createDefaultNormalizer());
        SuggestItem item = items.get(0);
        assertEquals("検索エンジン", item.getText());
        assertEquals("tag1", item.getTags()[0]);
        assertEquals("role1", item.getRoles()[0]);
    }

    protected ReadingConverter createDefaultReadingConverter() throws IOException {

// caused by the use of field tags with conflicting paths.
type TagPathError struct {
	Struct       reflect.Type
	Field1, Tag1 string
	Field2, Tag2 string
}

func (e *TagPathError) Error() string {
	return fmt.Sprintf("%s field %q with tag %q conflicts with field %q with tag %q", e.Struct, e.Field1, e.Tag1, e.Field2, e.Tag2)
}

const (
	initNilPointers     = true
	dontInitNilPointers = false
)

        ElevateWord elevateWord3 = new ElevateWord("c", 100.0f, Collections.singletonList("c"), Collections.singletonList("content"),
                Collections.singletonList("tag1"), Collections.singletonList("role1"));

        settings.elevateWord().add(elevateWord1);
        settings.elevateWord().add(elevateWord2);
        settings.elevateWord().add(elevateWord3);

optimization and deployment.

## C++ APIs

```bash
tf-to-stablehlo-translate \
    --input-path=/path/to/model \
    [--exported-model-signatures=signature1,signature2] \
    [--tag-names=tag1,tag2] \
    [--input-arg-shapes-str=arg-name:shape,...] \
    [--e] \
    [--output-filename=/path/to/output.mlir]
```

* `--input-path`: The path to the input TensorFlow SavedModel or MLIR module

		return p
	}
	return ""
}

// Tags - a rule can either have tag under <filter></filter> or under
// <filter><and></and></filter>. This method returns all the tags from the
// rule in the format tag1=value1&tag2=value2
func (r Rule) Tags() string {
	if !r.Filter.Tag.IsEmpty() {
		return r.Filter.Tag.String()
	}
	if len(r.Filter.And.Tags) != 0 {
		var buf bytes.Buffer
		for _, t := range r.Filter.And.Tags {