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=[
                {

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

    )

    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

    Map<Float, String> m = ImmutableMap.of(4.0f, "A", 3.0f, "B", 2.0f, "C", 1.0f, "D");
    Predicate<? super Integer> h = Predicates.equalTo(42);
    Function<? super String, Integer> g = new HashCodeFunction();
    Function<Float, String> f = Functions.forMap(m, "F");

    Predicate<Float> p1 = Predicates.compose(Predicates.compose(h, g), f);
    Predicate<Float> p2 = Predicates.compose(h, Functions.compose(g, f));

from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str | None = None
    description: str | None = None
    price: float | None = None
    tax: float = 10.5
    tags: list[str] = []


items = {
    "foo": {"name": "Foo", "price": 50.2},
    "bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},

     * @return a new instance of {@link ConcurrentHashMap}
     * @see ConcurrentHashMap#ConcurrentHashMap(int, float, int)
     */
    public static <K, V> ConcurrentHashMap<K, V> newConcurrentHashMap(final int initialCapacity, final float loadFactor,
            final int concurrencyLevel) {
        return new ConcurrentHashMap<>(initialCapacity, loadFactor, concurrencyLevel);
    }

			if intA > result {
				result = intA
			}
		}
		v.setInt(result)
		return nil
	}

	floatV, _ := v.ToFloat()
	floatA, _ := a.ToFloat()
	var result float64
	if !isMax {
		result = math.Min(floatV, floatA)
	} else {
		result = math.Max(floatV, floatA)
	}
	v.setFloat(result)
	return nil
}

func inferTypeAsTimestamp(v *Value) error {
	if s, ok := v.ToString(); ok {

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(
        openapi_examples={
            "normal": {
                "summary": "A normal example",

	Timestamp1 *PrimaryTerm `parser:" @@ \",\" "`
	Timestamp2 *PrimaryTerm `parser:" @@ \")\" "`
}

// LitValue represents a literal value parsed from the sql
type LitValue struct {
	Float   *float64       `parser:"(  @Float"`
	Int     *float64       `parser:" | @Int"` // To avoid value out of range, use float64 instead
	String  *LiteralString `parser:" | @LitString"`
	Boolean *Boolean       `parser:" | @(\"TRUE\" | \"FALSE\")"`

#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));