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 OAuth2PasswordRequestForm:
    """
    This is a dependency class to collect the `username` and `password` as form data
    for an OAuth2 password flow.

    The OAuth2 specification dictates that for a password flow the data should be
    collected using form data (instead of JSON) and that it should have the specific
    fields `username` and `password`.

    padding-right: 8px;
  }

  .daterangepicker .ranges, .daterangepicker .drp-calendar {
    float: left;
  }
}

@media (min-width: 730px) {
  .daterangepicker .ranges {
    width: auto;
  }

  .daterangepicker .ranges {
    float: left;
  }

  .daterangepicker.rtl .ranges {
    float: right;
  }

  .daterangepicker .drp-calendar.left {
    clear: none !important;
  }

    }

    /**
     * Encodes a float value in big-endian byte order.
     *
     * @param f the float value to encode
     * @param dst the destination byte array
     * @param di the starting index in the destination array
     * @return the number of bytes written (4)
     */
    public static int enc_floatbe(final float f, final byte[] dst, final int di) {
        return enc_uint32be(Float.floatToIntBits(f), dst, di);
    }

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(
            openapi_examples={

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

from fastapi import FastAPI, status
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/", status_code=status.HTTP_201_CREATED)
async def create_item(item: Item) -> Item:

      } else {
        EXPECT_EQ(dltensor_out->strides[i],
                  dltensor_out->shape[i + 1] * dltensor_out->strides[i + 1]);
      }
    }
  }
  const float* data_in = static_cast<const float*>(dltensor_in->data);
  const float* data_out = static_cast<const float*>(dltensor_out->data);
  for (size_t j = 0; j < num_elements; ++j) {
    EXPECT_EQ(data_out[j], data_in[j]);
  }

  TFE_CallDLManagedTensorDeleter(dlm_out);

 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
 */
package jcifs.internal.smb2.rdma;

/**
 * RDMA credit management for flow control.
 *
 * Credits are used to control the flow of messages between
 * RDMA peers to prevent buffer overflow.
 */
public class RdmaCredits {

    private int initialCredits;
    private int creditsGranted;

    /**

# Types de données supplémentaires { #extra-data-types }

Jusqu'à présent, vous avez utilisé des types de données courants, comme :

* `int`
* `float`
* `str`
* `bool`

Mais vous pouvez aussi utiliser des types de données plus complexes.

Et vous bénéficierez toujours des mêmes fonctionnalités que jusqu'à présent :

* Excellente prise en charge dans l'éditeur.
* Conversion des données à partir des requêtes entrantes.