func (x *Int32) CompareAndSwap(old, new int32) (swapped bool) {
	return CompareAndSwapInt32(&x.v, old, new)
}

// Add atomically adds delta to x and returns the new value.
func (x *Int32) Add(delta int32) (new int32) { return AddInt32(&x.v, delta) }

// And atomically performs a bitwise AND operation on x using the bitmask
// provided as mask and returns the old value.

  ASSERT_THAT(result, IsOkOrFiltered());
  EXPECT_EQ(mlir_bridge_legalize_count.Delta("tf.Acos"), 0);
  EXPECT_THAT(result,
              IncrementedOrFiltered(counts.Delta(kMlirCombinedMlirSuccess), 1));
  EXPECT_THAT(result,
              IncrementedOrFiltered(counts.Delta(kMlirCombinedOldSuccess), 1));
}

TEST(LegalizeWithCombinedBridge,

    name=""
    uid="charlie"
    missing="false"
    beingDeleted="false"
    deletingDependents="false"
    virtual="false"
  ];
  "delta" [
    label="uid=delta\nnamespace=\n./\n"
    group=""
    version=""
    kind=""
    namespace=""
    name=""
    uid="delta"
    missing="false"
    beingDeleted="false"
    deletingDependents="false"
    virtual="false"
  ];
  "foxtrot" [

  TF_ASSERT_OK(CreateMlirModule("empty_func.mlir"));

  TF_EXPECT_OK(ExportFromTensorflowDialectToExecutor(*mlir_module_));

  EXPECT_EQ(compilation_status.Delta(kExportSuccess), 1);
  EXPECT_EQ(compilation_status.Delta(kExportFailed), 0);
}

TEST_F(TensorflowDialectToExecutorTest, ErrorsWhenCannotConvert) {
  CellReader<int64_t> compilation_status(kExportStreamzName);

  })";
  CreateModule(kMlirModuleStr);
  CellReader<int64_t> reader(kMetricsName);

  auto result = Run();

  EXPECT_TRUE(result.succeeded());
  EXPECT_EQ(reader.Delta("tf.ConcatV2"), 1);
  EXPECT_EQ(reader.Delta("func.return"), 1);
  EXPECT_EQ(reader.Delta("func.func"), 0);
}

TEST_F(LegalizeTfTypesTest, RecordsStreamzNoQuantOps) {
  static constexpr char kMlirModuleStr[] = R"(

	// histogram.
	mudDegree = 1024
)

type edge struct {
	// At x, the function increases by y.
	x, delta float64
	// Additionally at x is a Dirac delta function with area dirac.
	dirac float64
}

// add adds a uniform function over [l, r] scaled so the total weight
// of the uniform is area. If l==r, this adds a Dirac delta function.
func (d *mud) add(l, r, area float64) {
	if area == 0 {
		return
	}

	if r < l {

				types.UID("delta"):   deltaNode(),
				types.UID("foxtrot"): foxtrotNode(),
				types.UID("golf"):    golfNode(),
			},
			uids: []types.UID{types.UID("delta")},
			expectNodes: []*dotVertex{
				NewDOTVertex(deltaNode()),
				NewDOTVertex(foxtrotNode()),
				NewDOTVertex(golfNode()),
			},
			expectEdges: []dotEdge{
				{F: types.UID("delta"), T: types.UID("foxtrot")},

  CellReader<int64_t> static_error(kNonStaticOpStreamz);
  CreateModule(kNonStaticFailure);

  auto result = Run();

  EXPECT_TRUE(result.failed());
  EXPECT_EQ(legal_error.Delta("mhlo.dynamic_iota"), 0);
  EXPECT_EQ(static_error.Delta("mhlo.dynamic_iota"), 1);
}

TEST_F(VerifyTfxlaLegalizationTest, SkipsSpecificNonStaticInputs) {
  // Using a string constant here instead of testdata to make this compatible

          <y:Arrows source="delta" target="none"/>
        </y:QuadCurveEdge>
      </data>
    </edge>
    <edge id="e1" source="n5" target="n4">
      <data key="d9">
        <y:QuadCurveEdge straightness="0.1">
          <y:Path sx="70.0" sy="-0.0" tx="-70.0" ty="-10.0"/>
          <y:LineStyle color="#000000" type="line" width="1.0"/>
          <y:Arrows source="delta" target="none"/>
        </y:QuadCurveEdge>

  })";

  CellReader<int64_t> error(kHasTpuPartitionedCallStreamzName);
  CreateModule(kMlirModuleStr);

  auto result = Run();

  EXPECT_TRUE(result.succeeded());

  EXPECT_EQ(error.Delta("true"), 1);
  EXPECT_EQ(error.Delta("false"), 0);
}

TEST_F(InferenceMetricsPassTest, RecordsFalseForNonTPUPartitionedCallOp) {
  static constexpr char kMlirModuleStr[] = R"(