from fastapi import Body, FastAPI
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: int,
    item: Item = Body(
        examples=[
            {
                "name": "Foo",
                "description": "A very nice Item",

from typing import Annotated

from fastapi import Body, FastAPI
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: int,
    item: Annotated[
        Item,
        Body(
            examples=[
                {

    """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] = []

    client = TestClient(mod.app)
    return client


@pytest.mark.parametrize("price", ["50.5", 50.5])
def test_post_with_tax(client: TestClient, price: str | float):
    response = client.post(
        "/items/",
        json={"name": "Foo", "price": price, "description": "Some Foo", "tax": 0.3},
    )
    assert response.status_code == 200
    assert response.json() == {

# Usage: buildall.bash [-e] [pattern]
#
# buildall.bash builds the standard library for all Go-supported
# architectures.
#
# Originally the Go build system used it as a smoke test to quickly
# flag portability issues in builders named "misc-compile" or "all-compile".
# As of CL 464955, the build system uses make.bash -compile-only instead,
# so this script no longer runs in any automated fashion.
#
# Options:

    )

    class FakeNumpyArray:
        def __init__(self):
            self.data = [1.0, 2.0, 3.0]

    FakeNumpyArrayPydantic = Annotated[
        FakeNumpyArray,
        WithJsonSchema(TypeAdapter(list[float]).json_schema()),
        PlainSerializer(lambda v: v.data),
    ]

    class MyModel(BaseModel):
        model_config = ConfigDict(arbitrary_types_allowed=True)
        custom_field: FakeNumpyArrayPydantic

	case simdjson.TypeFloat:
		v, err := iter.Float()
		if err != nil {
			return nil, err
		}
		return sql.FromFloat(v), nil
	case simdjson.TypeInt:
		v, err := iter.Int()
		if err != nil {
			return nil, err
		}
		return sql.FromInt(v), nil
	case simdjson.TypeUint:
		v, err := iter.Int()
		if err != nil {
			// Can't fit into int, convert to float.
			v, err := iter.Float()
			return sql.FromFloat(v), err
		}

target to the srcbucket on myminio.By default, if --replicate flag is not specified, replication of delete marker, permanent deletes, existing object replication and replica modification sync are all enabled. If you are using older mc versions, the ARN needs to be generated as a separate step before adding a replication rule.

> NOTE: If you are using a mc version below `RELEASE.2022-12-24T15-21-38Z`, the --remote-bucket flag needs an ARN generated by `mc admin bucket remote add` command....

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")
async def create_item(item: Item) -> Item:
    """
    Create an item with all the information:

    - **name**: each item must have a name

#include "tensorflow/core/protobuf/rewriter_config.pb.h"
#include "tensorflow/core/util/port.h"

using tensorflow::string;
using tensorflow::tstring;

TFE_TensorHandle* TestScalarTensorHandle(TFE_Context* ctx, float value) {
  float data[] = {value};
  TF_Status* status = TF_NewStatus();
  TF_Tensor* t = TFE_AllocateHostTensor(ctx, TF_FLOAT, nullptr, 0, status);
  memcpy(TF_TensorData(t), &data[0], TF_TensorByteSize(t));