{name: "large", data: bytes.Repeat([]byte("hello, world"), 1000000), wantIdx: true},
	}

	for _, tt := range tests {
		t.Run(tt.name, func(t *testing.T) {
			buf := make([]byte, 100) // make small buffer to ensure multiple reads are required for large case

			r, idxCB := newS2CompressReader(bytes.NewReader(tt.data), int64(len(tt.data)), false)
			defer r.Close()

			var rdrBuf bytes.Buffer

And we return the scopes as part of the JWT token.

/// danger

For simplicity, here we are just adding the scopes received directly to the token.

But in your application, for security, you should make sure you only add the scopes that the user is actually able to have, or the ones you have predefined.

///

{* ../../docs_src/security/tutorial005_an_py310.py hl[157] *}

	}

	return binary.LittleEndian.Uint64(buf[:8]), nil
}

func listxattr(path string) ([]string, error) {
	return xattr.LList(path)
}

func setxattr(path, name string, value uint64) error {
	data := make([]byte, 8)
	binary.LittleEndian.PutUint64(data, value)
	return xattr.LSet(path, name, data)
}

func main() {
	flag.StringVar(&path, "path", "", "path name where the attribute shall be applied")

* With this PR, kubectl and other RestClient's using the AuthProvider framework can make OIDC authenticated requests, and, if there is a refresh token present, the tokens will be refreshed as needed. ([#25270](https://github.com/kubernetes/kubernetes/pull/25270), [@bobbyrullo](https://github.com/bobbyrullo))

			zb0002, err = dc.ReadArrayHeader()
			if err != nil {
				err = msgp.WrapError(err, "Tags")
				return
			}
			if cap(z.Tags) >= int(zb0002) {
				z.Tags = (z.Tags)[:zb0002]
			} else {
				z.Tags = make([]BatchJobKV, zb0002)
			}
			for za0001 := range z.Tags {
				err = z.Tags[za0001].DecodeMsg(dc)
				if err != nil {
					err = msgp.WrapError(err, "Tags", za0001)
					return
				}
			}

			return err
		}
	}
	return e.EncodeToken(xml.EndElement{Name: start.Name})
}

// UnmarshalXML - decodes XML data.
func (d *Destination) UnmarshalXML(dec *xml.Decoder, start xml.StartElement) (err error) {
	// Make subtype to avoid recursive UnmarshalXML().
	type destination Destination
	dest := destination{}

	if err := dec.DecodeElement(&dest, &start); err != nil {
		return err
	}

// TestTags tests if the object tags satisfy the Filter tags requirement,
// it returns true if there is no tags in the underlying Filter.
func (f *Filter) TestTags(userTags string) bool {
	if f.cachedTags == nil {
		cached := make(map[string]string)
		for _, t := range append(f.And.Tags, f.Tag) {
			if !t.IsEmpty() {
				cached[t.Key] = t.Value
			}
		}
		f.cachedTags = cached
	}

## Dispatch

For synchronous calls, you bring your own thread and are responsible for managing how many simultaneous requests you make. Too many simultaneous connections wastes resources; too few harms latency.

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses in Nested Data Structures { #dataclasses-in-nested-data-structures }

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

        or hasattr(origin, "__pydantic_core_schema__")
        or hasattr(origin, "__get_pydantic_core_schema__")
    )


def field_annotation_is_scalar(annotation: Any) -> bool:
    # handle Ellipsis here to make tuple[int, ...] work nicely
    return annotation is Ellipsis or not field_annotation_is_complex(annotation)


def field_annotation_is_scalar_sequence(annotation: Union[type[Any], None]) -> bool: