This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses in Nested Data Structures

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

- **Flexibility and Control:** Java serialization offers limited control over the serialization process, such as excluding certain fields, customizing naming conventions, and handling complex data structures more gracefully.

- **Security:** Java serialization poses security risks, especially related to deserialization vulnerabilities.

///
/// All pointers defined in this structure point to memory allocated by the DSO
/// using an allocator provided by core TensorFlow when calling `TF_InitPlugin`.
///
/// IMPORTANT: To maintain binary compatibility, the layout of this structure
/// must not change! In the unlikely case that a new type of file needs to be
/// supported, add the new ops and metadata at the end of the structure.
typedef struct TF_FilesystemPluginOps {

    String SELF_COMBINATION_MERGE = "merge";

    String SELF_COMBINATION_REMOVE = "remove";

    /**
     * In case of complex XML structures, combining can be done based on id.
     */
    String ID_COMBINATION_MODE_ATTRIBUTE = "combine.id";

    /**
     * In case of complex XML structures, combining can be done based on keys.
     * This is a comma separated list of attribute names.
     */

 * or implied. See the License for the specific language governing permissions and limitations under
 * the License.
 */

// TODO(user): when things stabilize, flesh this out
/**
 * Hash functions and related structures.
 *
 * <p>See the Guava User Guide article on <a
 * href="https://github.com/google/guava/wiki/HashingExplained">hashing</a>.
 */
@CheckReturnValue
@ParametersAreNonnullByDefault
package com.google.common.hash;

import java.util.ArrayList;
import java.util.Collection;

/**
 * This is the main graph data structure used by the RepositorySystem to present tree and graph objects.
 *
 *
 */
@Deprecated
public class MetadataGraph {
    /** all graph nodes */
    Collection<MetadataGraphNode> nodes;

    /** entry point for tree-like structures */
    MetadataGraphNode entry;

    public MetadataGraph(MetadataGraphNode entry) {

A execution command of Generate task
which generates classes corresponding your tables,
for example, entities, condition-beans to specified
directories by DBFlute properties on "dfprop" directory.

Generated structures (directories and classes) are like this:
/- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
allcommon : classes bridging to DBFlute Runtime
bsbhv     : base behaviors
bsentity  : base entities

- **Flexibility and Control:**
Java serialization offers limited control over the serialization process, such as excluding certain fields, customizing naming conventions, and handling complex data structures more gracefully.

- **Security:**
Java serialization poses security risks, especially related to deserialization vulnerabilities.

- **Version Compatibility:**

 * underlying (implementation specific) data structures to be shared.
 *
 * <p>The size of the data structure used by ArrayBasedCharEscaper and ArrayBasedUnicodeEscaper is
 * proportional to the highest valued character that has a replacement. For example a replacement
 * map containing the single character '{@literal \}u1000' will require approximately 16K of memory.

    return set;
  }

  /**
   * Creates a {@code CompactLinkedHashSet} instance, with a high enough "initial capacity" that it
   * <i>should</i> hold {@code expectedSize} elements without rebuilding internal data structures.
   *
   * @param expectedSize the number of elements you expect to add to the returned set
   * @return a new, empty {@code CompactLinkedHashSet} with enough capacity to hold {@code