%0 = tfl.mul %arg0, %arg1 {fused_activation_function = "NONE"}: tensor<? x complex<f32>>
  func.return %0#0 : tensor<? x complex<f32>>
}

// CHECK-LABEL: testAddWithI64Broadcasting
func.func @testAddWithI64Broadcasting(tensor< 2x3xi64>, tensor<3xi64>) -> tensor<2x3xi64> {
^bb0(%arg0: tensor<2x3xi64>, %arg1: tensor<3xi64>):
  // CHECK: tfl.add(%arg0, %arg1)

  %3 = "tf.AddV2"(%arg1, %0) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
  %4 = "tf.AddV2"(%0, %arg1) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>

  // CHECK: %[[CONST:.*]] = "tf.Const"()
  // CHECK-DAG: %[[ADD1:.*]] = "tf.AddV2"(%arg0, %[[CONST]])
  // CHECK-DAG: %[[ADD2:.*]] = "tf.AddV2"(%arg0, %[[CONST]])
  // CHECK: return %[[ADD1]], %[[ADD2]], %arg1, %arg1

^bb0(%arg0: tensor<8x16xi16>, %arg1: tensor<8x16xi16>):
  %0 = "tf.FloorDiv"(%arg0, %arg1) : (tensor<8x16xi16>, tensor<8x16xi16>) -> tensor<8x16xi16>
  func.return %0 : tensor<8x16xi16>

// CHECK-LABEL: floor_div_i16
// CHECK:  tfl.floor_div %arg0, %arg1 : tensor<8x16xi16>
// CHECK:  return
}

func.func @not_equal(%arg0: tensor<8x16xf32>, %arg1: tensor<8x16xf32>) -> tensor<8x16xi1> {

func.func @testValidIfRegionOpWithMultipleResults(%arg0: tensor<i1>, %arg1: tensor<2xf32>) -> tensor<2xf32> {
  %0, %1, %2 = "tf.IfRegion"(%arg0) ({
     %t0 = "tf.Abs"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     %t1 = "tf.Acos"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     %t2 = "tf.Acosh"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>

		// unsigned arg0 >> arg2, shifting in bits from arg1 (==(arg1<<64+arg0)>>arg2, keeping low 64 bits), shift amount is mod 64
		{name: "SHRDQ", argLength: 3, reg: gp31shift, asm: "SHRQ", resultInArg0: true, clobberFlags: true},
		// unsigned arg0 << arg2, shifting in bits from arg1 (==(arg0<<64+arg1)<<arg2, keeping high 64 bits), shift amount is mod 64

	x13 := (uint64(arg1[53]) << 40)
	x14 := (uint64(arg1[52]) << 32)
	x15 := (uint64(arg1[51]) << 24)
	x16 := (uint64(arg1[50]) << 16)
	x17 := (uint64(arg1[49]) << 8)
	x18 := arg1[48]
	x19 := (uint64(arg1[47]) << 56)
	x20 := (uint64(arg1[46]) << 48)
	x21 := (uint64(arg1[45]) << 40)
	x22 := (uint64(arg1[44]) << 32)
	x23 := (uint64(arg1[43]) << 24)
	x24 := (uint64(arg1[42]) << 16)

  // CHECK: TensorListReserve
  // CHECK-SAME: -> tensor<!tf_type.variant<tensor<!tf_type.variant>>>
    %0 = "tf.TensorListReserve"(%arg1, %arg1) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*x!tf_type.variant>>>

// Here:
//   id0 = arg0
//   while-inputs = (id0/arg0, arg1, arg1)
//   while body pass through first and second arg, not last one
//   while-results = (arg0, arg1, Unknown)
//   #ID 2: read(arg0)      -> succ{5}
//   #ID 3: read(arg1)      -> succ{6}
//   #ID 4: read(unknown)   -> succ{5,6}
//   #ID 5 : write(arg0)
//   #ID 6 : write(arg1)


// CHECK-LABEL: func @output_of_while_op

module{
func.func @embedding(%arg0: tensor<*xf32>, %arg1: tensor<*xi32>) -> tensor<*xf32> attributes  {tf._implements = "embedding_matmul", tf._reference = "mlir"} {
  %0 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %1 = "tf.ExpandDims"(%arg1, %0) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
  %2 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>

    ```mlir
        func.func @dangling_print(%arg0: tensor<*xi32>, %arg1: tensor<i32>) -> (tensor<*xi32>, tensor<*xi32>) {
          %graph:2 = tf_executor.graph {
            %island1:3 = tf_executor.island {
              %add1 = "tf.Add"(%arg0, %arg1) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
              %add2 = "tf.Add"(%add1, %arg1) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>