bucketName      string
		objName         string
		uploadID        string
		PartID          int
		inputReaderData string
		inputMd5        string
		inputSHA256     string
		inputDataSize   int64
		// flag indicating whether the test should pass.
		shouldPass bool
		// expected error output.
		expectedMd5   string
		expectedError error
	}{
		// Test case  1-4.
		// Cases with invalid bucket name.

Python tiene soporte para "anotaciones de tipos" opcionales (también llamadas "type hints").

Estas **"anotaciones de tipos"** o type hints son una sintaxis especial que permite declarar el <abbr title="por ejemplo: str, int, float, bool">tipo</abbr> de una variable.

Al declarar tipos para tus variables, los editores y herramientas te pueden proporcionar un mejor soporte.

پایتون از "نوع‌نما"های اختیاری (که بهشون "type hints" یا "type annotations" هم می‌گن) پشتیبانی می‌کنه.

این **"نوع‌نماها"** یا annotationها یه سینتکس خاص هستن که بهت اجازه می‌دن <abbr title="مثلاً: str, int, float, bool">نوع</abbr> یه متغیر رو مشخص کنی.

با مشخص کردن نوع متغیرها، ویرایشگرها و ابزارها می‌تونن پشتیبانی بهتری بهت بدن.

```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):

   * it should be interpreted as a pattern. This flag will be false if its `Set-Cookie` header
   * included a `domain` attribute.
   *
   * For example, suppose the cookie's domain is `example.com`. If this flag is true it matches
   * **only** `example.com`. If this flag is false it matches `example.com` and all subdomains

Python has support for optional "type hints" (also called "type annotations").

These **"type hints"** or annotations are a special syntax that allow declaring the <abbr title="for example: str, int, float, bool">type</abbr> of a variable.

By declaring types for your variables, editors and tools can give you better support.

```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="2  7-10  23-25"
from fastapi import FastAPI
from pydantic import BaseModel
from typing import Optional

app = FastAPI()


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


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


@app.get("/items/{item_id}")
def read_item(item_id: int, q: Optional[str] = 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):