if ( preq.getPath() != null && preq.getPath().length() > 0 ) {
                    if ( log.isDebugEnabled() ) {
                        log.debug(String.format("Setting DFS request path from %s to %s", preq.getPath(), preq.getFullUNCPath()));
                    }
                    preq.setResolveInDfs(true);
                    preq.setPath(preq.getFullUNCPath());

Use o objeto `TestClient` da mesma forma que você faz com `httpx`.

Escreva instruções `assert` simples com as expressões Python padrão que você precisa verificar (novamente, `pytest` padrão).

```Python hl_lines="2  12  15-18"
{!../../docs_src/app_testing/tutorial001.py!}
```

/// tip | "Dica"

Observe que as funções de teste são `def` normais, não `async def`.

}

// STSError structure
type STSError struct {
	Code           string
	Description    string
	HTTPStatusCode int
}

// STSErrorResponse - error response format
type STSErrorResponse struct {
	XMLName xml.Name `xml:"https://sts.amazonaws.com/doc/2011-06-15/ ErrorResponse" json:"-"`
	Error   struct {
		Type    string `xml:"Type"`
		Code    string `xml:"Code"`

	}

	cfg.Enabled = true
	cfg.DialTimeout = defaultDialTimeout
	cfg.DialKeepAliveTime = defaultDialKeepAlive
	// Disable etcd client SDK logging, etcd client
	// incorrectly starts logging in unexpected data
	// format.
	cfg.LogConfig = &zap.Config{
		Level:    zap.NewAtomicLevelAt(zap.FatalLevel),
		Encoding: "console",
	}
	cfg.Endpoints = etcdEndpoints
	cfg.CoreDNSPath = env.Get(EnvEtcdCoreDNSPath, kvs.Get(CoreDNSPath))

        bufferIndex += 8;
        this.fileAttributes = SMBUtil.readInt4(buffer, bufferIndex);
        bufferIndex += 4;

        if ( log.isDebugEnabled() ) {
            log.debug(String.format("Closed %s (%s)", Hexdump.toHexString(this.fileId), this.fileName));
        }

        return bufferIndex - start;
    }

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  python3 tensorflow/tools/ci_build/update_version.py --nightly
fi

# This generates a pure python wheel of the format "*-py3-none-any.whl"
python3 tensorflow/tools/pip_package/setup.py bdist_wheel --dist-dir "$TFCI_OUTPUT_DIR"

# Get the name of the pure python wheel that was built. This should
# resolve to either

	for _, file := range zr.File {
		slc := strings.Split(file.Name, slashSeparator)
		if len(slc) != 2 { // expecting bucket/configfile in the zipfile
			rpt.SetStatus(file.Name, "", fmt.Errorf("malformed zip - expecting format bucket/<config.json>"))
			continue
		}
		bucket := slc[0]
		meta, err := readBucketMetadata(ctx, objectAPI, bucket)
		if err == nil {
			bucketMap[bucket] = &meta
		} else if err != errConfigNotFound {

6. Hier geben wir ein Dictionary zurück, das `items` enthält, welches eine Liste von Datenklassen ist.

    FastAPI ist weiterhin in der Lage, die Daten nach JSON zu <abbr title="Konvertieren der Daten in ein übertragbares Format">serialisieren</abbr>.

7. Hier verwendet das `response_model` als Typannotation eine Liste von `Author`-Datenklassen.

    Auch hier können Sie `dataclasses` mit Standard-Typannotationen kombinieren.

    In this case, it's a list of `Item` dataclasses.

6. Here we are returning a dictionary that contains `items` which is a list of dataclasses.

    FastAPI is still capable of <abbr title="converting the data to a format that can be transmitted">serializing</abbr> the data to JSON.

7. Here the `response_model` is using a type annotation of a list of `Author` dataclasses.

    Again, you can combine `dataclasses` with standard type annotations.

is stored under the `bucket/prefix` specified in the tier configuration with a custom name derived from a randomly generated uuid - e.g. `0b/c4/0bc4fab7-2daf-4d2f-8e39-5c6c6fb7e2d3`. The first two prefixes are characters 1-2,3-4 from the uuid. This format allows tiering to any cloud irrespective of whether the cloud in question supports versioning. The reference to the transitioned object name and transitioned tier is stored as part of the internal metadata for the object (or its version) on MinIO....