* 其中數個 flow 適合用來建立 OAuth 2.0 身分驗證提供者（如 Google、Facebook、X（Twitter）、GitHub 等）：
        * `implicit`
        * `clientCredentials`
        * `authorizationCode`
    * 但有一個特定的 flow 可直接在同一個應用中處理身分驗證：
        * `password`：後續幾個章節會示範這個。
* `openIdConnect`：提供一種方式來定義如何自動發現 OAuth2 的身分驗證資訊。
    * 這種自動探索機制即由 OpenID Connect 規範定義。

/// tip | 提示
整合像 Google、Facebook、X（Twitter）、GitHub 等其他身分驗證/授權提供者也是可行而且相對容易。

# Run manually to reformat a file:
# clang-format -i --style=file <file>
BasedOnStyle: Google

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.google.common.collect;

import static com.google.common.base.Preconditions.checkNotNull;
import static com.google.common.collect.CollectPreconditions.checkNonnegative;

import com.google.common.annotations.GwtCompatible;
import java.util.Comparator;
import java.util.Iterator;
import java.util.List;

        <entry key="checkstyle-version" value="8.42" />
        <entry key="copy-libs" value="false" />
        <entry key="location-0" value="BUNDLED:(bundled):Sun Checks" />
        <entry key="location-1" value="BUNDLED:(bundled):Google Checks" />
        <entry key="location-2" value="PROJECT_RELATIVE:$PROJECT_DIR$/checkstyle_ide.xml:Elasticsearch" />
        <entry key="scan-before-checkin" value="false" />

 * limitations under the License.
 */

package com.google.common.cache;

import static java.lang.annotation.ElementType.FIELD;
import static java.lang.annotation.ElementType.METHOD;
import static java.lang.annotation.ElementType.PARAMETER;
import static java.lang.annotation.RetentionPolicy.CLASS;

import com.google.common.annotations.GwtCompatible;
import java.lang.annotation.Retention;

 * limitations under the License.
 */

package com.google.common.graph;

import static java.lang.annotation.ElementType.FIELD;
import static java.lang.annotation.ElementType.METHOD;
import static java.lang.annotation.ElementType.PARAMETER;
import static java.lang.annotation.RetentionPolicy.CLASS;

import com.google.common.annotations.GwtCompatible;
import java.lang.annotation.Retention;

 * limitations under the License.
 */

package com.google.common.hash;

import static java.lang.annotation.ElementType.FIELD;
import static java.lang.annotation.ElementType.METHOD;
import static java.lang.annotation.ElementType.PARAMETER;
import static java.lang.annotation.RetentionPolicy.CLASS;

import com.google.common.annotations.GwtCompatible;
import java.lang.annotation.Retention;

 * limitations under the License.
 */

package com.google.common.net;

import static java.lang.annotation.ElementType.FIELD;
import static java.lang.annotation.ElementType.METHOD;
import static java.lang.annotation.ElementType.PARAMETER;
import static java.lang.annotation.RetentionPolicy.CLASS;

import com.google.common.annotations.GwtCompatible;
import java.lang.annotation.Retention;

 * limitations under the License.
 */

package com.google.common.math;

import static java.lang.annotation.ElementType.FIELD;
import static java.lang.annotation.ElementType.METHOD;
import static java.lang.annotation.ElementType.PARAMETER;
import static java.lang.annotation.RetentionPolicy.CLASS;

import com.google.common.annotations.GwtCompatible;
import java.lang.annotation.Retention;

   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.