case *ast.ReturnStmt:
			checkCopyLocksReturnStmt(pass, node)
		}
	})
	return nil, nil
}

// checkCopyLocksAssign checks whether an assignment
// copies a lock.
func checkCopyLocksAssign(pass *analysis.Pass, as *ast.AssignStmt) {
	for i, x := range as.Rhs {
		if path := lockPathRhs(pass, x); path != nil {

Joe Tsai <******@****.***> 1456950113 -0800

    TF_Status* status);

// ----------------------------  PJRT  -----------------------------------------
// Passes the pointer to a vector of PJRT_NamedValue and number of options to
// set options for creating a PJRT client. Passes nullptr for create_options and
// 0 for num_options if no options need to be set. You can use
// ConvertToPjRtNamedValueList in

#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "tensorflow/core/lib/core/status.h"

namespace tensorflow {
namespace tf2xla {
namespace v1 {

// Run all the passes involved in transforming the graph before execution so
// that it is suitable for targeting devices when called via the TF1 Session
// API.
// These transformations take as input a Tensorflow Graph as an MLIR Module

#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "mlir/Transforms/Passes.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/quantization/tensorflow/passes/passes.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"

namespace tensorflow {

#include "mlir/IR/BuiltinTypes.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"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"

namespace mlir {
namespace TFL {

namespace {

#include "mlir/IR/BuiltinTypeInterfaces.h"  // from @llvm-project
#include "mlir/Transforms/DialectConversion.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"  // IWYU pragma: keep
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"  // IWYU pragma: keep

namespace mlir {
namespace odml {

// Given a Composite op that wraps a core.aten.avg_pool2d, returns the padding

#else
#define RARG0 DI
#define RARG1 SI
#define RARG2 DX
#define RARG3 CX
#endif

// void runtime·libfuzzerCall4(fn, hookId int, s1, s2 unsafe.Pointer, result uintptr)
// Calls C function fn from libFuzzer and passes 4 arguments to it.
TEXT	runtime·libfuzzerCall4(SB), NOSPLIT, $0-40
	MOVQ	fn+0(FP), AX
	MOVQ	hookId+8(FP), RARG0
	MOVQ	s1+16(FP), RARG1
	MOVQ	s2+24(FP), RARG2
	MOVQ	result+32(FP), RARG3

	get_tls(R12)

#include "mlir/IR/PatternMatch.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "mlir/Transforms/Passes.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/utils/validators.h"

`.add_task()` reçoit comme arguments :

* Une fonction de tâche à exécuter en arrière-plan (`write_notification`).
* Les arguments positionnels à passer à la fonction de tâche dans l'ordre (`email`).
* Les arguments nommés à passer à la fonction de tâche (`message="some notification"`).

## Injection de dépendances