```

You may run builds and tests against different versions of Python sequentially
on the same machine by simply switching the value of `HERMETIC_PYTHON_VERSION`
between the runs. All the python-agnostic parts of the build cache from the
previous build will be preserved and reused for the subsequent builds.

### Specifying Python dependencies

During bazel build all TensorFlow's Python dependencies are pinned to their

Ela é apenas uma extensão do OAuth2 especificando algumas coisas que são relativamente ambíguas no OAuth2, para tentar torná-lo mais interoperável.

Por exemplo, o login do Google usa OpenID Connect (que por baixo dos panos usa OAuth2).

Mas o login do Facebook não tem suporte para OpenID Connect. Ele tem a própria implementação do OAuth2.

### OpenID (não "OpenID Connect")

    * The contents are **encrypted**, even though they are being sent with the **HTTP protocol**.

///

## Returning a custom `Response` { #returning-a-custom-response }

The example above shows all the parts you need, but it's not very useful yet, as you could have just returned the `item` directly, and **FastAPI** would put it in a `JSONResponse` for you, converting it to a `dict`, etc. All that by default.

    }

    /**
     * Masks sensitive values in a string for security purposes.
     *
     * @param value the string potentially containing sensitive information
     * @return the string with sensitive parts masked, or the original string if masking is disabled
     */
    public static String maskSecretValue(final String value) {
        if (MASK_SECRET_VALUE && value != null) {

		expectedErrCode  APIErrorCode
	}{
		// Test case - 1.
		// SignElement doesn't have 2 parts on an attempt to split at '='.
		// ErrMissingFields expected.
		{
			inputSignElement: "Signature",
			expectedSignStr:  "",
			expectedErrCode:  ErrMissingFields,
		},
		// Test case - 2.
		// SignElement does have 2 parts but doesn't have valid signature value.
		// ErrMissingFields expected.
		{

		DataDir:          "bffea160-ca7f-465f-98bc-9b4f1c3ba1ef",
		XLV1:             false,
		ModTime:          time.Now().UTC(),
		Size:             0,
		Mode:             0,
		Metadata:         nil,
		Parts:            nil,
		Erasure: ErasureInfo{
			Algorithm:    ReedSolomon.String(),
			DataBlocks:   4,
			ParityBlocks: 2,
			BlockSize:    10000,
			Index:        1,
			Distribution: []int{1, 2, 3, 4, 5, 6, 7, 8},

	c.Assert(err, nil)
	// verify whether complete multipart was successful.
	c.Assert(response.StatusCode, http.StatusOK)
	var parts []CompletePart
	for _, part := range completeUploads.Parts {
		part.ETag = canonicalizeETag(part.ETag)
		parts = append(parts, part)
	}
	etag := getCompleteMultipartMD5(parts)
	c.Assert(canonicalizeETag(response.Header.Get(xhttp.ETag)), etag)

	signature := hex.EncodeToString(sumHMAC(signingKey, []byte(stringToSign)))

	// final Authorization header
	parts := []string{
		"AWS4-HMAC-SHA256" + " Credential=" + accessKey + SlashSeparator + scope,
		"SignedHeaders=" + signedHeaders,
		"Signature=" + signature,
	}
	auth := strings.Join(parts, ", ")
	req.Header.Set("Authorization", auth)

	return signature, nil
}

// Returns new HTTP request object.

Importe o `SQLModel` e crie um modelo de banco de dados:

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[1:11] hl[7:11] *}

A classe `Hero` é muito semelhante a um modelo Pydantic (na verdade, por baixo dos panos, ela *é um modelo Pydantic*).

Existem algumas diferenças: