sb.append(comp);
      }
      dummy ^= sb.toString().length();
    }
    return dummy;
  }

  /**
   * Similar to the above, but keeps a boolean flag rather than checking for the string accumulated
   * so far being empty. As a result, it does not have the above-mentioned bug.
   */
  @Benchmark
  int booleanIfFirst(int reps) {
    int dummy = 0;
    for (int i = 0; i < reps; i++) {

 * way of "cutting" a "number line" (actually of instances of type {@code C}, not necessarily
 * "numbers") into two sections; this can be done below a certain value, above a certain value,
 * below all values or above all values. With this object defined in this way, an interval can
 * always be represented by a pair of {@code Cut} instances.
 *
 * @author Kevin Bourrillion
 */

But Python has a specific way to declare lists with internal types, or "type parameters":

### Import typing's `List`

In Python 3.9 and above you can use the standard `list` to declare these type annotations as we'll see below. 💡

But in Python versions before 3.9 (3.6 and above), you first need to import `List` from standard Python's `typing` module:

```Python hl_lines="1"
{!> ../../docs_src/body_nested_models/tutorial002.py!}
```

--- /dt9/usr/include/x86_64-linux-gnu/sys/cdefs.h       2013-09-30 13:58:17.000000000 +0000
+++ /dt9/usr/include/x86_64-linux-gnu/sys/cdefs.new.h   2022-11-04 17:17:31.727061220 +0000
@@ -320,7 +320,7 @@

 /* GCC 4.3 and above with -std=c99 or -std=gnu99 implements ISO C99
    inline semantics, unless -fgnu89-inline is used.  */
-#if (!defined __cplusplus || __GNUC_PREREQ (4,3)) && defined __GNUC__

    implementation("org.gradle.kotlin.kotlin-dsl:org.gradle.kotlin.kotlin-dsl.gradle.plugin:5.1.2")
    // This Kotlin version should only be updated when updating the above kotlin-dsl version
    implementation("org.jetbrains.kotlin:kotlin-gradle-plugin:2.0.21")
    implementation("org.gradle:test-retry-gradle-plugin:1.5.2")

In all the docs there are examples compatible with each version of Python (when there's a difference).

For example "**Python 3.6+**" means it's compatible with Python 3.6 or above (including 3.7, 3.8, 3.9, 3.10, etc). And "**Python 3.9+**" means it's compatible with Python 3.9 or above (including 3.10, etc).

So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses.

And of course, it supports the same:

* data validation
* data serialization

distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:

The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.