}

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
}

  // CHECK: "mhlo.fft"(%arg0) <{fft_length = dense<8> : tensor<1xi64>, fft_type = #mhlo<fft_type IFFT>}> : (tensor<8xcomplex<f32>>
  %0 = "tf.IFFT"(%arg0) : (tensor<8xcomplex<f32>>) -> tensor<8xcomplex<f32>>
  func.return %0 : tensor<8xcomplex<f32>>
}

// -----

// CHECK-LABEL: func @rfft_1D
func.func @rfft_1D(%arg0: tensor<8xf32>) -> tensor<5xcomplex<f32>> {

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -emit-select-tf-ops -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<4xcomplex<f64>>, tensor<4xcomplex<f64>>) -> tensor<4xcomplex<f64>> {
^bb0(%arg0: tensor<4xcomplex<f64>>, %arg1: tensor<4xcomplex<f64>>):
// CHECK:  {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [ {
// CHECK-NEXT:    deprecated_builtin_code: 32,
// CHECK-NEXT:    custom_code: "FlexAdd",

// CHECK-SAME: (%[[Y:.*]]: tensor<*xcomplex<f32>>, %[[DY:.*]]: tensor<*xcomplex<f32>>)
func.func @tanhgrad_complex(%y : tensor<*xcomplex<f32>>, %dy: tensor<*xcomplex<f32>>) -> tensor<*xcomplex<f32>> {
  // CHECK-NOT: tf.TanhGrad
  %0 = "tf.TanhGrad"(%y, %dy) : (tensor<*xcomplex<f32>>, tensor<*xcomplex<f32>>) -> tensor<*xcomplex<f32>>

  func.return %0 : tensor<*xcomplex<f32>>
}

// CHECK-LABEL: func @ZerosLike_unranked

  %noncon3 = "tf.Complex"(%con1, %arg2) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xcomplex<f32>>

  %res1 = "tf.MulNoNan"(%arg0, %arg1) : (tensor<2xcomplex<f32>>, tensor<2xcomplex<f32>>) -> tensor<2xcomplex<f32>>
  %res2 = "tf.DivNoNan"(%arg0, %noncon2) : (tensor<2xcomplex<f32>>, tensor<2xcomplex<f32>>) -> tensor<2xcomplex<f32>>

  func.return %0 : tensor<4xcomplex<f32>>
}

func.func @complex128() -> tensor<4xcomplex<f64>> {
  // CHECK-LABEL: @complex128

  func.return %0 : tensor<4xcomplex<f32>>
}

func.func @complex128() -> tensor<4xcomplex<f64>> {
  // CHECK-LABEL: @complex128

}

func.func @batchmatmulv2_adj_complex(%arg0: tensor<2x5xcomplex<f32>>, %arg1: tensor<4x2xcomplex<f32>>) -> tensor<5x4xcomplex<f32>> {
// CHECK-LABEL:   func @batchmatmulv2_adj_complex(
// CHECK-SAME:                                    [[LHS:%.*]]: tensor<2x5xcomplex<f32>>, [[RHS:%.*]]: tensor<4x2xcomplex<f32>>) -> tensor<5x4xcomplex<f32>> {
// CHECK:           [[LHSRE:%.*]] = mhlo.real [[LHS]]
// CHECK:           [[LHSIM:%.*]] = mhlo.imag [[LHS]]

# CHECK-DAG: tf.IteratorGetNextSync{{.+}}-> (tensor<*xi16>, tensor<3x24xi32>, tensor<?x4x32xi64>)
# CHECK-DAG: tf.MultiDeviceIteratorGetNextFromShard{{.+}}-> (tensor<5x40xf16>, tensor<*xcomplex<f32>>, tensor<6x48x?xcomplex<f64>>)
# CHECK-DAG: tf.InfeedDequeueTuple{{.+}}-> (tensor<?x?x?xui16>, tensor<*xui32>, tensor<7x56xui64>)
# CHECK-DAG: tf.InfeedDequeue{{.+}}-> tensor<?x8x?xi8>
# CHECK-DAG: tf.InfeedDequeue{{.+}}-> tensor<8x64xui8>

func.func @decompose_adam_with_complex_inputs(%arg0: tensor<!tf_type.resource<tensor<2xcomplex<f32>>>>, %arg1: tensor<!tf_type.resource<tensor<2xcomplex<f32>>>>, %arg2: tensor<!tf_type.resource<tensor<2xcomplex<f32>>>>, %arg3: tensor<complex<f32>>, %arg4: tensor<complex<f32>>, %arg5: tensor<complex<f32>>, %arg6: tensor<complex<f32>>, %arg7: tensor<complex<f32>>, %arg8: tensor<complex<f32>>, %arg9: tensor<2xcomplex<f32>>) attributes {tf.entry_function = {control_outputs = "Adam/update_Variable_1/ResourceApplyAdam",...