add(primToWrap, wrapToPrim, byte.class, Byte.class);
    add(primToWrap, wrapToPrim, char.class, Character.class);
    add(primToWrap, wrapToPrim, double.class, Double.class);
    add(primToWrap, wrapToPrim, float.class, Float.class);
    add(primToWrap, wrapToPrim, int.class, Integer.class);
    add(primToWrap, wrapToPrim, long.class, Long.class);
    add(primToWrap, wrapToPrim, short.class, Short.class);

}

func (t ScanToken) String() string {
	switch t {
	case scanner.EOF:
		return "EOF"
	case scanner.Ident:
		return "identifier"
	case scanner.Int:
		return "integer constant"
	case scanner.Float:
		return "float constant"
	case scanner.Char:
		return "rune constant"
	case scanner.String:
		return "string constant"
	case scanner.RawString:
		return "raw string constant"
	case scanner.Comment:
		return "comment"

    "aliases" : { },
    "mappings" : {
      "data_config" : {
        "properties" : {
          "available" : {
            "type" : "boolean"
          },
          "boost" : {
            "type" : "float"
          },
          "createdBy" : {
            "type" : "keyword"
          },
          "createdTime" : {
            "type" : "long"
          },
          "description" : {
            "type" : "text"

   */
  ObjectCountHashMap(int capacity) {
    this(capacity, DEFAULT_LOAD_FACTOR);
  }

  ObjectCountHashMap(int expectedSize, float loadFactor) {
    init(expectedSize, loadFactor);
  }

  void init(int expectedSize, float loadFactor) {
    Preconditions.checkArgument(expectedSize >= 0, "Initial capacity must be non-negative");
    Preconditions.checkArgument(loadFactor > 0, "Illegal load factor");

* Conversão de dados
* Validação de dados
* Documentação automatica

## Tipos especiais e validação { #special-types-and-validation }

Além dos tipos singulares normais como `str`, `int`, `float`, etc. Você também pode usar tipos singulares mais complexos que herdam de `str`.

from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class ItemGroup(BaseModel):
    items: list[tuple[str, str]]


class Coordinate(BaseModel):
    x: float
    y: float


@app.post("/model-with-tuple/")
def post_model_with_tuple(item_group: ItemGroup):
    return item_group


@app.post("/tuple-of-models/")

        final long docFreq = Long.parseLong(source.get(FieldNames.DOC_FREQ).toString());
        final long queryFreq = Long.parseLong(source.get(FieldNames.QUERY_FREQ).toString());
        final float userBoost = Float.parseFloat(source.get(FieldNames.USER_BOOST).toString());
        final List<String> tags = SuggestUtil.getAsList(source.get(FieldNames.TAGS));

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

                            <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>

def get_items(item_a: str, item_b: int, item_c: float, item_d: bool, item_e: bytes):