func.func @testAbs(tensor<? x f32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<? x f32>):
  // CHECK: "tfl.abs"(%arg0)
  %0 = "tfl.abs"(%arg0): (tensor<? x f32>) -> tensor<? x f32>
  func.return %0 : tensor<? x f32>
}

// CHECK-LABEL: testAddN
func.func @testAddN(tensor<? x f32>, tensor<? x f32>, tensor<? x f32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<? x f32>, %arg1: tensor<? x f32>, %arg2: tensor<? x f32>):

    %6 = "quantfork.dcast"(%5) : (tensor<1x3x!quant.uniform<i8:f32, 0.13170163023705575:-1>>) -> tensor<1x3xf32>
    %7 = "quantfork.qcast"(%arg2) {volatile} : (tensor<f32>) -> tensor<!quant.uniform<i8:f32, 0.13170163023705575:-1>>
    %8 = "quantfork.dcast"(%7) : (tensor<!quant.uniform<i8:f32, 0.13170163023705575:-1>>) -> tensor<f32>

  %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
  %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

  // CHECK: %[[QUANTIZE:.*]] = mhlo.uniform_quantize %arg0 : (tensor<2xf32>) -> tensor<2x!quant.uniform<i8:f32, 1.000000e+00:3>>
  // CHECK: %[[CONVERT_1:.*]] = mhlo.bitcast_convert %[[QUANTIZE]] : (tensor<2x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<2xi8>

  %0:3 = "tf.CaseRegion"(%index) ({
      %mul = "tf.Mul"(%arg0, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
      "tf.Yield"(%arg1, %mul, %arg2) : (tensor<f32>, tensor<f32>, tensor<!tf_type.resource>) -> ()
    }, {
      %sub = "tf.Sub"(%arg0, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
      "tf.Yield"(%arg1, %sub, %arg2) : (tensor<f32>, tensor<f32>, tensor<!tf_type.resource>) -> ()
    }, {

// CHECK: return %[[CONV2D]] : tensor<1x3x3x2x!quant.uniform<i8:f32, 4.000000e+00>>

// -----

func.func @convolution_upstream_srq_non_const_filter(%arg0: tensor<1x3x3x4x!quant.uniform<i8:f32, 1.000000e+0:-100>>, %arg1: tensor<3x3x4x2x!quant.uniform<i8:f32:3, {2.000000e+2, 3.000000e+3}>>) -> tensor<1x3x3x2x!quant.uniform<i8:f32, 4.000000e+0>> {

  let arguments = (
    ins TFL_TensorOf<[F32, QI8, QI16]>:$input,

    // Weights
    TFL_TensorOfOrNone<[F32, QI8]>:$input_to_input_weights,
    TFL_TensorOf<[F32, QI8]>:$input_to_forget_weights,
    TFL_TensorOf<[F32, QI8]>:$input_to_cell_weights,
    TFL_TensorOf<[F32, QI8]>:$input_to_output_weights,

    // Recurrent weights
    TFL_TensorOfOrNone<[F32, QI8]>:$recurrent_to_input_weights,

}

func.func @branch0(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  %0 = "tf.Add" (%arg0, %arg1) {device = "/device:CPU:0"}  : (tensor<f32>, tensor<f32>) -> tensor<f32>
  func.return %0 : tensor<f32>
}

func.func @branch1(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  %0 = "tf.Add" (%arg0, %arg1) {device = "/device:CPU:0"}  : (tensor<f32>, tensor<f32>) -> tensor<f32>

// CHECK: %[[CUSTOM_AGGREGATOR_1:.*]], %[[MIN_1:.*]], %[[MAX_1:.*]], %[[HISTOGRAM_1:.*]] = "tf.CustomAggregator"

      f = @identity} : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>
  %2 = "tf.OptionalGetValue"(%1) : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<f32>
  %3 = "tf.OptionalNone"() : () -> tensor<!tf_type.variant<tensor<f32>>>
  return %2, %3 : tensor<f32>, tensor<!tf_type.variant<tensor<f32>>>
}

func.func private @identity(%arg0: tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>> {

^bb0(%arg0: tensor<1x56x56x24x!quant.uniform<u8:f32, 0.27583434161017922:119>>, %arg1: tensor<1x56x56x24x!quant.uniform<u8:f32, 0.40149296779258581:136>>):