}
	case "kube-proxy":
		// generate manpage for kube-proxy
		proxy := proxyapp.NewProxyCommand()
		genMarkdown(proxy, "", outDir)
		for _, c := range proxy.Commands() {
			genMarkdown(c, "kube-proxy", outDir)
		}
	case "kube-scheduler":
		// generate manpage for kube-scheduler
		scheduler := schapp.NewSchedulerCommand()
		genMarkdown(scheduler, "", outDir)
		for _, c := range scheduler.Commands() {

    statistics.histogram_statistics.bin_width = 1.0

    hist_freq = np.zeros(501, dtype=np.int32)

    # Advanced calibration methods that use histograms detect outliers, so they
    # don't use the outliers as min/max values.
    hist_freq[0] = 1
    hist_freq[-1] = 1

    # The majority of the data exists around the center.
    hist_freq[250] = 1000
    for i in range(1, 201):

                </div>
            </div>
        </div>
        <section class="content">
            <div class="row">
                <div class="col-md-6">
                    <div class="card card-outline card-primary">
                        <div class="card-header">
                            <h3 class="card-title">
                                <la:message key="labels.system_info_env_title"/>

  std::ostream& o_;

 public:
  explicit TaggedValueIOStreamVisitor(std::ostream& o) : o_(o) {}

  std::ostream& operator()(const ListPtr& x) {
    OutList(o_, x->begin(), x->end(), '[', ']');
    return o_;
  }
  std::ostream& operator()(const TuplePtr& x) {
    OutList(o_, x->begin(), x->end(), '(', ')');
    return o_;
  }
  std::ostream& operator()(const DictPtr& x) {
    o_ << *x;
    return o_;
  }

	if trimmedText == "" {
		return ""
	}

	const indent = `  `
	inLines := strings.Split(trimmedText, "\n")
	outLines := make([]string, 0, len(inLines))

	for _, line := range inLines {
		outLines = append(outLines, indent+strings.TrimSpace(line))
	}

	return strings.Join(outLines, "\n")

                                                            data-dismiss="modal">
                                                        <la:message key="labels.crud_button_cancel"/>
                                                    </button>
                                                    <button type="submit" class="btn btn-outline-light"

	"log"
	"os"
	"path"
	"strings"
	"text/template"

	. "go/types"
)

func main() {
	if len(os.Args) != 2 {
		log.Fatal("missing argument: generrordocs <dir>")
	}
	outDir := os.Args[1]
	if err := os.MkdirAll(outDir, 0755); err != nil {
		log.Fatal("unable to create output directory: %s", err)
	}
	walkCodes(func(name string, vs *ast.ValueSpec) {
		// ignore unused errors
		if name == "_" {
			return
		}

// completes.
//
// The first two differences can easily be handled by straightforward
// adaptation of the concept called "R(t)" in the original paper and
// "virtual time" in the implementation outline. In that
// implementation outline, the notation now() is used to mean reading
// the virtual clock. In the original paper’s terms, "R(t)" is the
// number of "rounds" that have been completed at real time t ---

	$

5. Merge profiles together:

	$ go tool covdata merge -i=indir1,indir2 -o=outdir -modpaths=github.com/go-delve/delve
	$

6. Subtract one profile from another

	$ go tool covdata subtract -i=indir1,indir2 -o=outdir
	$

7. Intersect profiles

	$ go tool covdata intersect -i=indir1,indir2 -o=outdir
	$

8. Dump a profile for debugging purposes.

	$ go tool covdata debugdump -i=indir

					</p>
				</div>
				<div class="modal-footer justify-content-between">
					<button type="button" class="btn btn-outline-light"
						data-dismiss="modal">
						<la:message key="labels.crud_button_cancel" />
					</button>
					<button type="submit" class="btn btn-outline-light"
						name="delete"
						value="<la:message key="labels.crud_button_delete" />">
						<em class="fa fa-trash">