important to us to keep Guava as easy to use and understand as we can. That means boiling
        features down to compact but powerful abstractions, and controlling feature bloat carefully.


        Guava's main yardstick for evaluating proposed features can be summed up as [utility times
        ubiquity](https://github.com/google/guava/wiki/PhilosophyExplained#utility-times-ubiquity).

{* ../../docs_src/response_model/tutorial004_py310.py hl[9,11:12] *}

* `description: Union[str, None] = None` (or `str | None = None` in Python 3.10) has a default of `None`.
* `tax: float = 10.5` has a default of `10.5`.
* `tags: List[str] = []` has a default of an empty list: `[]`.

but you might want to omit them from the result if they were not actually stored.

{* ../../docs_src/response_model/tutorial004_py310.py hl[9,11:12] *}

* `description: Union[str, None] = None` (ou `str | None = None` no Python 3.10) tem um padrão de `None`.
* `tax: float = 10.5` tem um padrão de `10.5`.
* `tags: List[str] = []` tem um padrão de uma lista vazia: `[]`.

mas você pode querer omiti-los do resultado se eles não foram realmente armazenados.

  /** Used for concurrent threads reading the list for the first time. */
  private val readCompleteLatch = CountDownLatch(1)

  // The lists are held as a large array of UTF-8 bytes. This is to avoid allocating lots of strings
  // that will likely never be used. Each rule is separated by '\n'. Please see the
  // PublicSuffixListGenerator class for how these lists are generated.
  // Guarded by this.
  override lateinit var bytes: ByteString

```Python hl_lines="4  9-12  25-27"
from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):

```Python hl_lines="4  9-12  25-27"
from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):

```Python hl_lines="4  9-12  25-27"
from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):

```Python hl_lines="4  9-12  25-27"
from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    price: float
    is_offer: Union[bool, None] = None


@app.get("/")
def read_root():
    return {"Hello": "World"}


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Union[str, None] = None):

                || height < getThumbnailHtmlImageMinHeightAsInteger().intValue()) {
            return false;
        }

        final float ratio = getThumbnailHtmlImageMaxAspectRatioAsDecimal().floatValue();
        if ((float) width / (float) height > ratio || (float) height / (float) width > ratio) {
            return false;
        }

        return true;
    }

    String getHttpProxyHost();

	buckets     []string
}

// NewMetricsGroup creates a new MetricsGroup. To create a metrics group for
// metrics with a `bucket` dimension (label), use `NewBucketMetricsGroup`.
//
// The `loader` function loads metrics from the cache and the system.
func NewMetricsGroup(path collectorPath, descriptors []MetricDescriptor,
	loader MetricsLoaderFn,
) *MetricsGroup {
	mg := &MetricsGroup{
		CollectorPath: path,