%2 = stablehlo.add %1, %0 : tensor<1x3x2x4xf32>
  return %2 : tensor<1x3x2x4xf32>
}
// CHECK: %[[TRANSPOSE_0:.+]] = stablehlo.transpose
// CHECK: %[[ADD_0:.+]] = stablehlo.add %[[TRANSPOSE_0]], {{.*}}
// CHECK: return %[[ADD_0]]

// -----

// [No change] Tests that the activation transpose whose rank is not 4 is not
// deferred.

// CHECK-LABEL: add_with_activation_transpose_rank_two

// CHECK: %[[ADD_0:.+]] = stablehlo.add %[[DOT_GENERAL_0]], %[[ARG_3]] : tensor<1x3x!quant.uniform<i32:f32:1, {{.*}}>>
// CHECK: %[[UNIFORM_QUANTIZE_1:.+]] = stablehlo.uniform_quantize %[[ADD_0]] : (tensor<1x3x!quant.uniform<i32:f32:1, {{.*}}>>) -> tensor<1x3x!quant.uniform<i8:f32, {{.*}}>>

  [(IsNotInLiftedFunc $res),
   (FloatValueEquals<"0"> $cst), (IsStableHLOConstantOp $bias), (AreTheSameValue $conv_0, $conv_1), (AreTheSameValue $add_0, $add_1)], [], (addBenefit 15)>;

def LiftDotGeneralWithBiasAndReluDynamic : Pat<
  (StableHLO_MaxOp:$res
    (StableHLO_AddOp:$add_0
      (StableHLO_DotGeneralOp:$dot_general_0 $lhs, $rhs, $dot_dimension_numbers, $precision_config),
      (StableHLO_DynamicBroadcastInDimOp

  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %0: tensor<1xf32>
// CHECK: %[[ADD_0:.*]] = "tf.AddV2"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
// CHECK: return %[[ADD_0]] : tensor<1xf32>
}

// CHECK-LABEL: softmax
func.func @softmax(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> {

  ops::Switch switch_b(root.WithOpName("switch_b"), value, cond_b);

  Output add_a = ops::Add(root.WithOpName("add_a"), switch_a.output_true,
                          switch_b.output_true);
  Output add_b = ops::Add(root.WithOpName("add_b"), switch_a.output_true,
                          switch_b.output_true);
  Output add = ops::Add(root.WithOpName("add_c"), add_a, add_b);

  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));

  for (mlir::Attribute a : attr.getValue()) {
    if (auto attr = mlir::dyn_cast<mlir::BoolAttr>(a)) {
      list->add_b(attr.getValue());
    } else if (auto attr = mlir::dyn_cast<mlir::IntegerAttr>(a)) {
      list->add_i(attr.getInt());
    } else if (auto attr = mlir::dyn_cast<mlir::FloatAttr>(a)) {
      list->add_f(attr.getValueAsDouble());
    } else if (auto attr = mlir::dyn_cast<mlir::StringAttr>(a)) {
      AttrValue nested_value;

      ->set_global_jit_level(OptimizerOptions::ON_2);
  std::unique_ptr<Session> session(NewSession(options));

  TF_ASSERT_OK(session->Create(graph_def));

  std::vector<std::string> output_names = {std::string("add_4:0")};

  Tensor tensor_2x2(DT_FLOAT, TensorShape({2, 2}));
  for (int i = 0; i < 4; i++) {
    tensor_2x2.matrix<float>()(i / 2, i % 2) = 5 * i;
  }

  Tensor tensor_3x3(DT_FLOAT, TensorShape({3, 3}));

  ops::Switch sw_3 = CreateSwitch(root, "3");

  Output add0 =
      ops::Add(root.WithOpName("add0"), sw_0.output_false, sw_1.output_false);
  Output add1 =
      ops::Add(root.WithOpName("add1"), sw_2.output_false, sw_3.output_false);

  ops::Merge m0(root.WithOpName("m0"), {add0, add1});
  ops::Merge m1(root.WithOpName("m1"), {add0, add1});

  Output add2 = ops::Add(root.WithOpName("add2"), m0.output, m1.output);

    // CHECK-ROUNDTRIP:  %0 = stablehlo.composite "stablehlo.add_n" %arg0 {composite_attributes = {test_bool = false, test_int = 2 : i64, test_string = "test"}, decomposition = @add_n.impl} : (tensor<i64>) -> tensor<i64>
    %0 = stablehlo.composite "stablehlo.add_n" %arg0 { composite_attributes = { test_int = 2 : i64, test_bool = 0 : i1, test_string = "test"}, decomposition = @add_n.impl } : (tensor<i64>) -> tensor<i64>
    return %0 : tensor<i64>
  }

		return
	}
	tp0 := s.typePointersOfType(typ, addr)
	tp1 := s.typePointersOf(addr, size)
	failed := false
	for {
		var addr0, addr1 uintptr
		tp0, addr0 = tp0.next(addr + size)
		tp1, addr1 = tp1.next(addr + size)
		if addr0 != addr1 {
			failed = true
			break
		}
		if addr0 == 0 {
			break
		}
	}
	if failed {
		tp0 := s.typePointersOfType(typ, addr)
		tp1 := s.typePointersOf(addr, size)