well defined data structure. To understand `xl.meta` here are the few things to start with

`xl.meta` carries first 8 bytes an XL header which describes the current format and the format version,
allowing the unmarshaller's to automatically use the right data structures to parse the subsequent content in the stream.

### v1.0

| Entry     | Encoding    | Content

# Tutorial - User Guide

This tutorial shows you how to use **FastAPI** with most of its features, step by step.

Each section gradually builds on the previous ones, but it's structured to separate topics, so that you can go directly to any specific one to solve your specific API needs.

It is also built to work as a future reference so you can come back and see exactly what you need.

## Run the code

    * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data.
* Validate **complex structures**:
    * Use of hierarchical Pydantic models, Python `typing`’s `List` and `Dict`, etc.

		l.LastSec++
	}
}

// LastMinuteHistogram keeps track of last minute sizes added.
type LastMinuteHistogram [sizeLastElemMarker]lastMinuteLatency

// Merge safely merges two LastMinuteHistogram structures into one
func (l LastMinuteHistogram) Merge(o LastMinuteHistogram) (merged LastMinuteHistogram) {
	for i := range l {
		merged[i] = l[i].merge(o[i])
	}
	return merged
}

- Azure: Support multiple services sharing one IP address ([#94991](https://github.com/kubernetes/kubernetes/pull/94991), [@nilo19](https://github.com/nilo19)) [SIG Cloud Provider]

    * Porque las estructuras de datos de Pydantic son solo <abbr title='En español: ejemplares. Aunque a veces los llaman incorrectamente "instancias"'>instances</abbr> de clases que tu defines, el auto-completado, el linting, mypy y tu intuición deberían funcionar bien con tus datos validados.
* Valida **estructuras complejas**:

  }

  /**
   * Creates a {@code CompactLinkedHashMap} instance, with a high enough "initial capacity" that it
   * <i>should</i> hold {@code expectedSize} elements without rebuilding internal data structures.
   *
   * @param expectedSize the number of elements you expect to add to the returned set
   * @return a new, empty {@code CompactLinkedHashMap} with enough capacity to hold {@code

	return nil, ErrNotSupported
}

// parseCiphertext parses and converts a ciphertext into
// the format expected by a secretKey.
//
// Previous implementations of the secretKey produced a structured
// ciphertext. parseCiphertext converts all previously generated
// formats into the expected format.
func parseCiphertext(b []byte) ([]byte, kms.SecretKeyType) {
	if len(b) == 0 {
		return b, kms.AES256
	}

  }

  /**
   * Creates a {@code CompactLinkedHashMap} instance, with a high enough "initial capacity" that it
   * <i>should</i> hold {@code expectedSize} elements without rebuilding internal data structures.
   *
   * @param expectedSize the number of elements you expect to add to the returned set
   * @return a new, empty {@code CompactLinkedHashMap} with enough capacity to hold {@code

Por ejemplo, puedes usar:

* `int`
* `float`
* `bool`
* `bytes`

```Python hl_lines="1"
{!../../docs_src/python_types/tutorial005.py!}
```

### Tipos con sub-tipos

Existen algunas estructuras de datos que pueden contener otros valores, como `dict`, `list`, `set` y `tuple`. Los valores internos pueden tener su propio tipo también.

Para declarar esos tipos y sub-tipos puedes usar el módulo estándar de Python `typing`.