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.

    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.
     */

///
/// 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 {

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) {

 * 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;

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.

well defined data structure. To understand `xl.meta` here are the few things to start with

`xl.meta` carries first 8 bytes an XL header which describes the current format and the format version,
allowing the unmarshaller's to automatically use the right data structures to parse the subsequent content in the stream.

### v1.0

| Entry     | Encoding    | Content

  // Get theta shape and store in theta_dims.
  int num_dims = TF_NumDims(theta_tensor);
  vector<int64_t> theta_dims(num_dims);
  GetDims(theta_tensor, theta_dims.data());

  // Initialize auxilary data structures.
  vector<float> thetaPlus_data(num_elems);
  vector<float> thetaMinus_data(num_elems);
  AbstractTensorHandle* f_outputs[1];

  // Numerical Grad Check
  for (int i = 0; i < num_elems; i++) {