%add = tfr.call @tf__add(%dot, %bias) : (!tfr.tensor, !tfr.tensor) -> !tfr.tensor

  %relu = tfr.constant "relu" -> !tfr.attr
  %relu6 = tfr.constant "relu6" -> !tfr.attr

  %is_relu = tfr.equal %act, %relu -> i1
  %res = scf.if %is_relu -> !tfr.tensor {
    %applied_relu = tfr.call @tf__relu(%add) : (!tfr.tensor) -> !tfr.tensor
    scf.yield %applied_relu : !tfr.tensor

    t3 = constant_op.constant([[-10.0, -10.0], [-10.0, -10.0]])
    sq = biased_dense(t1, t2, t3, act='relu')
    self.assertAllEqual(sq.numpy().reshape(-1), [0, 0, 5, 12])

  def testWithKnownKernel(self):

    @def_function.function
    def biasd_dense_elu(x, y, z):
      dot = gen_composite_ops.my_biased_dense(x, y, z)
      return nn_ops.elu(dot)  # with known kernel, should not expand.

    t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])

    sq = gen_composite_ops.my_biased_dense(t1, t2, t3, act='relu')
    self.assertAllEqual(sq.numpy().reshape(-1), [0, 0, 5, 12])

  def testWithKnownKernel(self):

    def biasd_dense_elu(x, y, z):
      dot = gen_composite_ops.my_biased_dense(x, y, z)
      return nn_ops.elu(dot)  # with known kernel, should not expand.

    t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])

// Since the tf.AddV2 op has two uses, we have a _FusedBatchNormEx without the
// Relu activation and we only fuse the add.
// CHECK-NEXT: %[[Y:[a-z0-9]*]], {{.*}}_FusedBatchNormEx
// CHECK-NEXT: %[[relu:[a-z0-9]*]] ={{.*}}Relu"(%[[Y]]
// CHECK-NEXT: return %[[relu]]

    // CHECK: tfl.add
    // CHECK-SAME: tac.skip_target_annotation
    %1 = "tfl.add"(%arg0, %0) {fused_activation_function = "RELU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    // CHECK: tfl.relu
    // CHECK-SAME: tac.skip_target_annotation
    %2 = "tfl.relu"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
    func.return
  }
}

// -----

  // will be preserved since it has uses.
  %2 = "tf.MyCustomOp"(%1, %0) {fused_activation_function = "RELU", int_attr = 2 : i32}  : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
  // will be preserved since it has side-effect.
  "tf.MyCustomOp"(%1, %0) {fused_activation_function = "RELU", int_attr = 2 : i32}  : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
  func.return %2 : tensor<4xf32>

  // CHECK: device = "cpu"
  %2 = "tf.Relu"(%1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "cpu"} : (tensor<3x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "xpu"
  %3 = "tf.Relu"(%2) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"]} : (tensor<3x3xf32>) -> tensor<3x3xf32>
  func.return %3 : tensor<3x3xf32>

  if act == 'RELU':
    return tf.raw_ops.Relu(features=res)
  elif act == 'RELU6':
    return tf.raw_ops.Relu6(features=res)
  elif act == 'TANH':
    return tf.raw_ops.Tanh(x=res)
  else:
    return res


@tf.RegisterGradient('NewConv2D')
def _conv_add_relu_grad(op: ops.Operation, grad):
  act = op.get_attr('act')
  y = op.outputs[0]
  if act == 'RELU':
    grad = gen_nn_ops.relu_grad(grad, y)

func.func @main(%arg0: tensor<?xi32>) -> (tensor<?xi32>, tensor<?xi32>) {
    %0 = "tf.Relu"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>
    %1 = "tf.Relu"(%0) {device = "tpu0"} : (tensor<?xi32>) -> tensor<?xi32>
    %2 = "tf.Add"(%0, %1) {device = "tpu0"} : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>
    %3 = "tf.Relu"(%2) : (tensor<?xi32>) -> tensor<?xi32>
    %4 = "tf.Relu"(%1) {device = "tpu0"} : (tensor<?xi32>) -> tensor<?xi32>

  %cpu = corert.get_op_handler %ch "cpu"
  %0 = corert.executeop(%cpu) "tf.Relu"(%arg0) { T = f32 } : 1
  %arg1 = tfrt_fallback_async.corert_tensorhandle_to_fallback_tensor %arg1_th {_tfrt_cost = 1 : i64, device = "/CPU:0"} : (!corert.tensorhandle) -> (!tfrt_fallback.tf_tensor)
  %1 = tfrt_fallback_async.executeop key(0) cost(100) device("/CPU:0") "tf.Relu"(%arg1) { T = f32 } : 1
  tfrt.return