}

	return fc(stmt)
}

// DataTypeOf return field's db data type
func (m Migrator) DataTypeOf(field *schema.Field) string {
	fieldValue := reflect.New(field.IndirectFieldType)
	if dataTyper, ok := fieldValue.Interface().(GormDataTypeInterface); ok {
		if dataType := dataTyper.GormDBDataType(m.DB, field); dataType != "" {
			return dataType
		}
	}

	return m.Dialector.DataTypeOf(field)
}

	relationshipFields := []*Field{}
	for _, field := range schema.Fields {
		if field.DataType != "" && field.HasDefaultValue && field.DefaultValueInterface == nil {
			schema.FieldsWithDefaultDBValue = append(schema.FieldsWithDefaultDBValue, field)
		}

		if !embedded {
			if field.DataType == "" && field.GORMDataType == "" && (field.Creatable || field.Updatable || field.Readable) {

						columnType)
				}
			case "name":
				dataType := DB.Dialector.DataTypeOf(stmt.Schema.LookUpField(columnType.Name()))
				if !strings.Contains(strings.ToUpper(dataType), strings.ToUpper(columnType.DatabaseTypeName())) {
					t.Fatalf("column name type should be correct, name: %v, length: %v, expects: %v, column: %#v",
						columnType.Name(), columnType.DatabaseTypeName(), dataType, columnType)
				}

	for _, ref := range relation.References {
		f := joinSchema.LookUpField(ref.ForeignKey.DBName)
		if f == nil {
			return fmt.Errorf("missing field %s for join table", ref.ForeignKey.DBName)
		}

		f.DataType = ref.ForeignKey.DataType
		f.GORMDataType = ref.ForeignKey.GORMDataType
		if f.Size == 0 {
			f.Size = ref.ForeignKey.Size
		}
		ref.ForeignKey = f
	}

    private final boolean includesDependencies;
    private final Set<PathType> pathTypes;
    private final Map<String, String> properties;

    public DefaultType(
            String id,
            Language language,
            String extension,
            String classifier,
            boolean includesDependencies,
            PathType... pathTypes) {
        this.id = requireNonNull(id, "id");

        tf.constant.
    *   Add experimental IndexedDatasets.
    *   Add selective registration target using the lite proto runtime.
    *   Add simple Tensor and DataType classes to TensorFlow Lite Java
    *   Add support for bitcasting to/from uint32 and uint64.
    *   Added a subclass of Estimator that can be created from a SavedModel
        (SavedModelEstimator).