fprintFunc(p, f)
		fi.Close()
	}
}

type pass struct {
	name     string
	fn       func(*Func)
	required bool
	disabled bool
	time     bool            // report time to run pass
	mem      bool            // report mem stats to run pass
	stats    int             // pass reports own "stats" (e.g., branches removed)

    }

    def "credentials can be specified via DSL"() {
        httpBuildCacheServer.withBasicAuth("user", "pass")
        settingsFile << """
            buildCache {
                remote.credentials {
                    username = "user"
                    password = "pass"
                }
            }
        """

        when:
        withBuildCache().run "jar"
        then:

			pass:  true,
		},
		// Test 4 - supported s3 type with post policy.
		{
			authT: authTypePostPolicy,
			pass:  true,
		},
		// Test 5 - supported s3 type with streaming signed.
		{
			authT: authTypeStreamingSigned,
			pass:  true,
		},
		// Test 6 - supported s3 type with signature v2.
		{
			authT: authTypeSignedV2,
			pass:  true,
		},

func CheckReadable(pass *analysis.Pass, filename string) error {
	if slicesContains(pass.OtherFiles, filename) ||
		slicesContains(pass.IgnoredFiles, filename) {
		return nil
	}
	for _, f := range pass.Files {
		// TODO(adonovan): use go1.20 f.FileStart
		if pass.Fset.File(f.Pos()).Name() == filename {
			return nil
		}
	}

  // TODO(hinsu): Investigate if we really need SCCP pass before shape inference
  // and can do with just one pass after the shape inference.
  pm.addPass(mlir::createSCCPPass());
  // Guarantee all functions have one use, which enables shape inference.
  pm.addPass(mlir::TF::CreateGuaranteeAllFuncsOneUsePass());
  // Run shape inference pass before tensorlist decomposition to get buffer

//===----------------------------------------------------------------------===//
// The prepare-quantize Pass.
//
namespace mlir {
namespace quant {

namespace {

using QuantMethod = tensorflow::quantization::QuantizationMethod::PresetMethod;

// Applies prepare quantization on the model in TF dialect. This pass runs
// before the quantization pass and propagate the quantization parameters

func stackalloc(f *Func, spillLive [][]ID) [][]ID {
	if f.pass.debug > stackDebug {
		fmt.Println("before stackalloc")
		fmt.Println(f.String())
	}
	s := newStackAllocState(f)
	s.init(f, spillLive)
	defer putStackAllocState(s)

	s.stackalloc()
	if f.pass.stats > 0 {
		f.LogStat("stack_alloc_stats",
			s.nArgSlot, "arg_slots", s.nNotNeed, "slot_not_needed",

labels.ldapAdminSecurityPrincipal	=	Bind DN
labels.ldapAdminSecurityCredentials	=	Mot de passe
labels.ldapBaseDn	=	DN de base
labels.ldapAccountFilter	=	Filtre de compte
labels.ldapGroupFilter	=	Filtre de groupe
labels.ldapMemberofAttribute	=	Attribut memberOf
labels.oldPassword	=	Mot de passe actuel
labels.newPassword	=	nouveau mot de passe
labels.confirmNewPassword	=	Nouveau mot de passe (confirmer)

labels.menu_system	=	Système

Finally, the Run field contains a function to be called by the driver to
execute the analysis on a single package. The driver passes it an
instance of the Pass type.

# Pass

A [Pass] describes a single unit of work: the application of a particular
Analyzer to a particular package of Go code.
The Pass provides information to the Analyzer's Run function about the
package being analyzed, and provides operations to the Run function for

#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "mlir/IR/Operation.h"  // from @llvm-project
#include "mlir/IR/PatternMatch.h"  // from @llvm-project
#include "mlir/IR/Value.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"