|| actual == Short.class
                            || actual == Byte.class)) {
                return true;
            }
            if (formal == Float.TYPE
                    && (actual == Float.class
                            || actual == Long.class
                            || actual == Integer.class
                            || actual == Short.class

        final AtomicBoolean calledFirst = new AtomicBoolean(false);
        queryProcessor.addFilter(new QueryProcessor.Filter() {
            @Override
            public QueryBuilder execute(QueryContext context, Query query, float boost, FilterChain chain) {
                calledFirst.set(true);
                assertFalse(called.get());
                QueryBuilder builder = chain.execute(context, query, boost);
                assertTrue(called.get());

{
  "fess_config.key_match" : {
    "aliases" : { },
    "mappings" : {
      "key_match" : {
        "properties" : {
          "boost" : {
            "type" : "float"
          },
          "createdBy" : {
            "type" : "keyword"
          },
          "createdTime" : {
            "type" : "long"
          },
          "maxSize" : {
            "type" : "integer"
          },
          "query" : {

* Преобразование данных
* Валидацию данных
* Автоматическую документацию

## Особые типы и валидация { #special-types-and-validation }

Помимо обычных простых типов, таких как `str`, `int`, `float` и т.д., вы можете использовать более сложные простые типы, которые наследуются от `str`.

 *   <li>{@code logger} - Logger instance for logging warnings.</li>
 *   <li>{@code PREFIX} - Prefix for the temporary file name.</li>
 *   <li>{@code SUFFIX} - Suffix for the temporary file name.</li>
 *   <li>{@code done} - Flag indicating whether the file has been retrieved.</li>
 * </ul>
 *
 * <p>Constructors:</p>
 * <ul>

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  float value = 0.0f;
  TF_Tensor* t = TFE_TensorHandleResolve(value_handle, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  ASSERT_EQ(sizeof(float), TF_TensorByteSize(t));
  memcpy(&value, TF_TensorData(t), sizeof(float));
  TF_DeleteTensor(t);
  EXPECT_EQ(12.0, value);

  TFE_DeleteTensorHandle(var_handle);

                            <la:message key="labels.dict_stopwords_title"/>
                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item"><la:link href="/admin/dict">
                                <la:message key="labels.dict_list_link"/>
                            </la:link></li>

     */
    private int allRecordCount;

    /**
     * Total number of pages.
     */
    private int allPageCount;

    /**
     * Flag indicating if a previous page exists.
     */
    private boolean existPrePage;

    /**
     * Flag indicating if a next page exists.
     */
    private boolean existNextPage;

    /**
     * List of page numbers for pagination.
     */

                            <la:message key="labels.dict_stemmeroverride_title"/>
                        </h1>
                    </div>
                    <div class="col-sm-6">
                        <ol class="breadcrumb float-sm-right">
                            <li class="breadcrumb-item"><la:link href="/admin/dict">
                                <la:message key="labels.dict_list_link"/>
                            </la:link></li>

    visibility = ["//visibility:public"],
)

# This flag disables generating tensorflow.lite.python under LiteRT repo.
# Avoid using flag for creating tflite wheels as tensorflow/lite is not yet fully split from tf.
config_setting(
    name = "disable_tf_lite_py",
    define_values = {"disable_tf_lite_py": "true"},
    visibility = ["//visibility:public"],
)

# This flag enables experimental MLIR support.
config_setting(