}

func isReplicationPermissionCheck(err error) bool {
	_, ok := err.(ReplicationPermissionCheck)
	return ok
}

// DataMovementOverwriteErr - captures the error when a data movement activity
// like rebalance incorrectly tries to overwrite an object.
type DataMovementOverwriteErr GenericError

func (de DataMovementOverwriteErr) Error() string {

//       name: NAME # match object names that satisfy the wildcard expression.
//       olderThan: 70h # match objects older than this value
//       createdBefore: "2006-01-02T15:04:05.00Z" # match objects created before "date"
//       tags:
//         - key: name
//           value: pick* # match objects with tag 'name', all values starting with 'pick'
//       metadata:
//         - key: content-type

여러분은 타입을 이용한 표준 파이썬을 다음과 같이 적을 수 있습니다:

```Python
from datetime import date

from pydantic import BaseModel

# 변수를 str로 선언
# 그 후 함수 안에서 편집기 지원을 받으세요
def main(user_id: str):
    return user_id


# Pydantic 모델
class User(BaseModel):
    id: int
    name: str
    joined: date
```

위의 코드는 다음과 같이 사용될 수 있습니다:

```Python

* Para passar um corpo JSON, passe um objeto Python (por exemplo, um `dict`) para o parâmetro `json`.
* Se você precisar enviar *Dados de Formulário* em vez de JSON, use o parâmetro `data`.
* Para passar *headers*, use um `dict` no parâmetro `headers`.
* Para *cookies*, um `dict` no parâmetro `cookies`.

		// MaxUploads is set more than number of meta data entries in the result.
		// Expecting no `Uploads` metadata.
		{
			MaxUploads:  2,
			Prefix:      "orange",
			IsTruncated: false,
		},
		// listMultipartResults - 14.
		// `Prefix` is set. It doesn't contain object name as its preifx.
		// MaxUploads is set more than number of meta data entries in the result.

        ":c_api",
        ":c_api_experimental",
        ":env",
        ":kernels",
    ],
)

tf_cuda_cc_test(
    name = "c_api_test",
    size = "medium",
    srcs = ["c_api_test.cc"],
    data = [
        ":test_op1.so",
        "//tensorflow/cc/saved_model:saved_model_half_plus_two",
    ],
    extra_copts = if_pywrap(["-DTENSORFLOW_NO_SHARED_OBJECTS"]),
    linkopts = select({

    void injectMirror(List<ArtifactRepository> repositories, List<Mirror> mirrors);

    /**
     * Injects the proxy information into the specified repositories. For each repository that is matched by a proxy,
     * its proxy data will be set accordingly. Repositories without a matching proxy will have their proxy cleared.
     * Note: This method must be called after {@link #injectMirror(List, List)} or the repositories will end up

package kms

import (
	"bytes"
	"sort"
	"unicode/utf8"
)

// Context is a set of key-value pairs that
// are associated with a generate data encryption
// key (DEK).
//
// A KMS implementation may bind the context to the
// generated DEK such that the same context must be
// provided when decrypting an encrypted DEK.
type Context map[string]string

Ça, ajouté au fait que Python soit le langage le plus populaire pour la **Data Science**, le **Machine Learning** et surtout le **Deep Learning**, font de **FastAPI** un très bon choix pour les APIs et applications de **Data Science** / **Machine Learning**.

Zuerst müssen Sie `BaseModel` von `pydantic` importieren:

{* ../../docs_src/body/tutorial001_py310.py hl[2] *}

## Ihr Datenmodell erstellen { #create-your-data-model }

Dann deklarieren Sie Ihr Datenmodell als eine Klasse, die von `BaseModel` erbt.

Verwenden Sie Standard-Python-Typen für alle Attribute:

{* ../../docs_src/body/tutorial001_py310.py hl[5:9] *}