app = FastAPI()


class Item(BaseModel):
    name: str = Field(examples=["Foo"])
    description: str | None = Field(default=None, examples=["A very nice Item"])
    price: float = Field(examples=[35.4])
    tax: float | None = Field(default=None, examples=[3.2])


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: set[str] = set()


@app.post(
    "/items/",
    summary="Create an item",
    description="Create an item with all the information, name, description, price, tax and a set of unique tags",
)

# TODO: pv2 should this return strings instead?
def decimal_encoder(dec_value: Decimal) -> int | float:
    """
    Encodes a Decimal as int if there's no exponent, otherwise float

    This is useful when we use ConstrainedDecimal to represent Numeric(x,0)
    where an integer (but not int typed) is used. Encoding this as a float
    results in failed round-tripping between encode and parse.
    Our Id type is a prime example of this.

from typing import Annotated

from fastapi import FastAPI, Path
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None


@app.put("/items/{item_id}")
async def update_item(
    item_id: Annotated[int, Path(title="The ID of the item to get", ge=0, le=1000)],
    q: str | None = None,
    item: Item | None = None,

from fastapi import FastAPI
from pydantic import BaseModel


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None


app = FastAPI()


@app.post("/items/")
async def create_item(item: Item):

from dataclasses import dataclass

from fastapi import FastAPI


@dataclass
class Item:
    name: str
    price: float
    description: str | None = None
    tax: float | None = None


app = FastAPI()


@app.post("/items/")
async def create_item(item: Item):

    """JSONL async generator with no internal await."""
    i = 0
    while True:
        yield i
        i += 1


async def _run_asgi_and_cancel(app: FastAPI, path: str, timeout: float) -> bool:
    """Call the ASGI app for *path* and cancel after *timeout* seconds.

    Returns `True` if the cancellation was delivered (i.e. it did not hang).
    """
    chunks: list[bytes] = []

  Tensor t_cc(DT_FLOAT, TensorShape({2, 3}));
  t_cc.flat<float>().setConstant(1.0);
  tensorflow::TensorProto t_proto;
  t_cc.AsProtoField(&t_proto);

  TF_Buffer* t_buffer = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(t_proto, t_buffer));

  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(

data-validation-allowing"]="range["+j+";"+i+"]",0!==j.indexOf("-")&&0!==i.indexOf("-")||(h["data-validation-allowing"]+=",negative"),(j.indexOf(".")>-1||i.indexOf(".")>-1||k.indexOf(".")>-1)&&(h["data-validation-allowing"]+=",float")):h["data-validation-allowing"]+=",float,negative"}if(e.attr("pattern")&&(b.push("custom"),h["data-validation-regexp"]=e.attr("pattern")),e.attr("maxlength")&&(b.push("length"),h["data-validation-length"]="max"+e.attr("maxlength")),!c&&e.attr("list")){var l=[],m=a("#...

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: set[str] = set()


@app.post(
    "/items/",
    summary="Create an item",
    response_description="The created item",
)
async def create_item(item: Item) -> Item:
    """