Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).

final class Platform {

  private static final char[] CHAR_BUFFER = new char[1024];

  static char[] charBufferFromThreadLocal() {
    // ThreadLocal is not available to GWT, so we always reuse the same
    // instance.  It is always safe to return the same instance because
    // javascript is single-threaded, and only used by blocks that doesn't
    // involve async callbacks.
    return CHAR_BUFFER;
  }

  private Platform() {}

 *     their equivalent on the result of {@code Traverser.forTree(tree)} where {@code tree}
 *     implements {@code SuccessorsFunction}, which has a similar API as {@link #children} or can be
 *     the same lambda function as passed into {@link #using(Function)}.
 */
// This class is now only available externally for backwards compatibility; it should not be used

Then you declare your data model as a class that inherits from `BaseModel`.

Use standard Python types for all the attributes:

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


The same as when declaring query parameters, when a model attribute has a default value, it is not required. Otherwise, it is required. Use `None` to make it just optional.

        map sigbuild-r2.12-python3.9 2.12-python3.9
        map sigbuild-r2.12-python3.10 2.12-python3.10
        map sigbuild-r2.12-python3.11 2.12-python3.11
        # TF 2.12 + Clang (containers are the same, but env vars in configs.bzl are different)
        map sigbuild-r2.12-clang 2.12-python3.9
        map sigbuild-r2.12-clang-python3.8 2.12-python3.8
        map sigbuild-r2.12-clang-python3.9 2.12-python3.9

You can use the same dependency *functions* you use normally.

### Dependency requirements { #dependency-requirements }

They can declare request requirements (like headers) or other sub-dependencies:

{* ../../docs_src/dependencies/tutorial006_an_py310.py hl[8,13] *}

### Raise exceptions { #raise-exceptions }

These dependencies can `raise` exceptions, the same as normal dependencies:

    @DisplayName("Constructor stores and exposes same array reference")
    void constructor_and_get_referenceSemantics() {
        // Arrange
        byte[] data = new byte[] { (byte) 0xDE, (byte) 0xAD, (byte) 0xBE, (byte) 0xEF };

        // Act
        SecurityBlob blob = new SecurityBlob(data);

        // Assert
        assertSame(data, blob.get(), "get() should expose the same array reference passed in");

This is the same mechanism used when you give permissions while logging in with Facebook, Google, GitHub, etc:

<img src="/img/tutorial/security/image11.png">

## JWT token with scopes { #jwt-token-with-scopes }

Now, modify the token *path operation* to return the scopes requested.

         * processes one artifact at a time and hence cannot associate the artifacts from the same project to use the
         * same timestamp+buildno for the snapshot versions. Allowing the caller to pass in metadata from a previous
         * deployment allows to re-establish the association between the artifacts of the same project.
         */
        for (Metadata metadata : request.getMetadata()) {

Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.

The same way as with Pydantic models, you declare class attributes with type annotations, and possibly default values.

You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.