*
 * <p>This interface is meant to be used as the type of a parameter to graph algorithms (such as
 * topological sort) that only need a way of accessing the predecessors of a node in a graph.
 *
 * <h3>Usage</h3>
 *
 * Given an algorithm, for example:
 *
 * <pre>{@code
 * public <N> someGraphAlgorithm(N startNode, PredecessorsFunction<N> predecessorsFunction);
 * }</pre>
 *

type DiskInfoOptions struct {
	DiskID  string `msg:"id"`
	Metrics bool   `msg:"m"`
	NoOp    bool   `msg:"np"`
}

// DiskInfo is an extended type which returns current
// disk usage per path.
// The above means that any added/deleted fields are incompatible.
//
// The above means that any added/deleted fields are incompatible.
//
//msgp:tuple DiskInfo
type DiskInfo struct {
	Total      uint64

  status)
	status_of_proc -p $PID_FILE fess fess && exit 0 || exit $?
    ;;
  restart|force-reload)
	if [ -f "$PID_FILE" ]; then
		$0 stop
		sleep 1
	fi
	$0 start
	;;
  *)
	log_success_msg "Usage: $0 {start|stop|restart|force-reload|status}"
	exit 1
	;;
esac

// errSkipFile returned by the fn() for readDirFn() when it needs
// to proceed to next entry.
var errSkipFile = errors.New("skip this file")

var errIgnoreFileContrib = errors.New("ignore this file's contribution toward data-usage")

// errXLBackend XL drive mode requires fresh deployment.
var errXLBackend = errors.New("XL backend requires fresh drive")

// StorageErr represents error generated by xlStorage call.
type StorageErr string

## Pydantic `Settings`

Por sorte, o Pydantic possui uma funcionalidade para lidar com essas configurações vindas de variáveis de ambiente utilizando <a href="https://docs.pydantic.dev/latest/usage/pydantic_settings/" class="external-link" target="_blank">Pydantic: Settings management</a>.

### Instalando `pydantic-settings`

Primeiro, instale o pacote `pydantic-settings`:

<div class="termy">

@REM activatorconfig.txt found in the ACTIVATOR_HOME or ACTIVATOR_HOME/ACTIVATOR_VERSION
@setlocal enabledelayedexpansion

@echo off

set "var1=%~1"
if defined var1 (
  if "%var1%"=="help" (
    echo.
    echo Usage activator [options] [command]
    echo.
    echo Commands:
    echo ui                 Start the Activator UI
    echo new [name] [template-id]  Create a new project with [name] using template [template-id]

///

/// info

You can also use:

* `response_model_exclude_defaults=True`
* `response_model_exclude_none=True`

  if (!status->status.ok()) {
    return 0;
  }
  return op_reg_data->op_def.is_stateful();
}

void TF_InitMain(const char* usage, int* argc, char*** argv) {
  tensorflow::port::InitMain(usage, argc, argv);
}

int TF_PickUnusedPortOrDie() {
  return tensorflow::internal::PickUnusedPortOrDie();
}

TFE_TensorHandle* TFE_NewTensorHandleFromScalar(TF_DataType data_type,

Pydantic 버전 2에서 <a href="https://docs.pydantic.dev/latest/usage/model_config/" class="external-link" target="_blank">Pydantic 공식 문서: Model Config</a>에 나와 있는 것처럼 `dict`를 받는 `model_config` 어트리뷰트를 사용할 것입니다.

`"json_schema_extra"`를 생성된 JSON 스키마에서 보여주고 싶은 별도의 데이터와 `examples`를 포함하는 `dict`으로 설정할 수 있습니다.

////

//// tab | Pydantic v1

   * protected to allow subclasses to override the fastpath escaping function to inline their
   * escaping test. See {@link CharEscaperBuilder} for an example usage.
   *
   * @param s the literal string to be escaped
   * @param index the index to start escaping from
   * @return the escaped form of {@code string}
   * @throws NullPointerException if {@code string} is null
   */