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>):

  %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,

  %1 = "tf.Const"() {value = dense<0.0> : tensor<f32>} : () -> tensor<f32>
  %2 = "tf.Const"() {value = dense<0.0> : tensor<f32>} : () -> tensor<f32>
  %3 = "tf.Const"() {value = dense<0.0> : tensor<f32>} : () -> tensor<f32>
  func.return %0, %1, %2, %3 : tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>
}

// CHECK-LABEL: func @tflite_custom_nms(

func.func @mul_complex32(%arg0: tensor<14xcomplex<f32>>, %arg1: tensor<14xcomplex<f32>>) -> tensor<14xcomplex<f32>> {
  %0 = "tf.Mul"(%arg0, %arg1) : (tensor<14xcomplex<f32>>, tensor<14xcomplex<f32>>) -> tensor<14xcomplex<f32>>
  func.return %0: tensor<14xcomplex<f32>>

// CHECK-LABEL: mul_complex32
// CHECK:  tfl.mul %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<14xcomplex<f32>>
// CHECK:  return
}

  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) : (tensor<8x8x8x8xf32>, tensor<f32>, tensor<f32>) -> tensor<8x8x8x8xf32>
  func.return %0 : tensor<8x8x8x8xf32>
}

// -----

// Test invalid tf.FakeQuantWithMinMaxVars
func.func @testInvalidFakeQuantWithMinMaxVarsWrongAttr(tensor<8x8x8x8xf32>, tensor<f32>, tensor<f32>) -> tensor<8x8x8x8xf32> {

        // expected-remark@above {{ID: 0}}
        %const = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
        // expected-remark@above {{ID: 1}}
        %1 = "tf._XlaRecvAtHostV2"(%arg0, %arg1) {_xla_has_host_transfer = true, key = "host_compute_channel_1_args"} : (tensor<!tf_type.string>, tensor<i64>) -> tensor<f32>
        // expected-remark@above {{ID: 2}}

          %0 = "tf.opA"(%arg0) : (tensor<f32>) -> tensor<f32>
          return %0 : tensor<f32>
        }
      }
    }
    ```

    will be transformed into:

    ```mlir
    module  {
      func @foo(%arg0: tensor<f32>) -> tensor<f32> {
        %0 = tf_executor.graph {
          %outputs, %control = tf_executor.island {
            %1 = "tf.opA"(%arg0) : (tensor<f32>) -> tensor<f32>

    %1:2 = "tfl.unpack"(%0) {axis = 0 : i32, num = 2 : i32} : (tensor<2x!quant.uniform<u8:f32, 0.007:128>>) -> (tensor<*x!quant.uniform<u8:f32, 0.007:128>>, tensor<*x!quant.uniform<u8:f32, 0.007:128>>)
    func.call @quant_fn(%1#0) : (tensor<*x!quant.uniform<u8:f32, 0.007:128>>) -> ()

    func.return %1#1 : tensor<*x!quant.uniform<u8:f32, 0.007:128>>
  }