logger().trace(message, arg1, arg2, arg3)
        logger().trace((Marker) null, message)
        logger().trace((Marker) null, message, new Exception())
        logger().trace((Marker) null, message, arg1)
        logger().trace((Marker) null, message, arg1, arg2)
        logger().trace((Marker) null, message, arg1, arg2, arg3)

        then:
        0 * context.outputEventListener._

  %0:2 = "tfl.while"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
    %1 = func.call @cond(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> tensor<i1>
    "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
    %1:2 = func.call @body(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>)

  %0:3 = "tfl.while"(%arg0, %arg1, %cst) ({
    ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>, %arg4: tensor<i32>):
      %cst_0 = arith.constant dense<0> : tensor<i32>
      %1 = "tfl.greater"(%arg2, %cst_0) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
      "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
    ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>, %arg4: tensor<i32>):

  %0:2 = "tf.WhileRegion"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<*xf32>, %arg3: tensor<*xf32>):
    %1 = "tf.OpA"() {is_stateless = true} : () -> tensor<i1>
    "tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
  ^bb0(%arg2: tensor<*xf32>, %arg3: tensor<*xf32>):
    %1 = "tf.OpB"(%arg2) {is_stateless = true} : (tensor<*xf32>) -> tensor<*xf32>
    %2 = "tf.OpC"(%arg3) {is_stateless = true} : (tensor<*xf32>) -> tensor<*xf32>

func.func @FusedBatchNormRelu(%arg0: tensor<8x8x8x8xf32>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>, %arg3: tensor<8xf32>, %arg4: tensor<8xf32>) -> (tensor<8x8x8x8xf32>) {
// CHECK-NEXT: %[[Y:[a-z0-9]*]], {{.*}}_FusedBatchNormEx
// CHECK-NEXT: return %[[Y]]

  -> (tensor<i64>) attributes {tf._tf_data_function = true, tf.signature.is_stateful} {
    %0 = "tf.JustPretend"(%arg1, %arg2, %arg3) : (tensor<32xf32>, tensor<!tf_type.resource<tensor<64xf32>>>, tensor<!tf_type.resource<tensor<128xf32>>>) -> (tensor<i64>)

    ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
      // CHECK: call @WhileOp_cond
      // CHECK-SAME: (tensor<*xi32>, tensor<*xf32>)
      %cst_0 = arith.constant dense<0> : tensor<i32>
      %1 = "tfl.greater"(%arg2, %cst_0) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
      "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
    ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
      // CHECK: call @WhileOp_body

              %arg2: i32 {tfr.name = "A"},
              %arg3: !tfr.attr {tfr.name = "T"})
  -> (!tfr.tensor<A>, !tfr.tensor_list<C>)
  attributes {A, C}

// -----

// CHECK-LABEL: tfr.func
tfr.func @Foo(%arg0: !tfr.tensor<A>,
              %arg1: !tfr.tensor_list<C>,
              %arg2: i32 {tfr.name = "A"},
              %arg3: vector<1xi32> {tfr.name = "C"})
  -> (!tfr.tensor<A>, !tfr.tensor_list<C>)

  %1 = "tf.Transpose"(%arg3, %cst) {T = f32, Tperm = i32, device = ""} : (tensor<*xf32>, tensor<2xi32>) -> tensor<?x?xf32>
  %2 = "tf.Rank"(%arg3) : (tensor<*xf32>) -> tensor<i32>
  %3 = "tf.Range"(%2, %cst_0, %cst_1) : (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<?xi32>
  %4 = "tf.Sub"(%3, %cst_2) : (tensor<?xi32>, tensor<i32>) -> tensor<?xi32>
  %5 = "tf.Transpose"(%arg3, %4) : (tensor<*xf32>, tensor<?xi32>) -> tensor<*xf32>

// CHECK-SAME: %[[ARG_7:[a-z0-9]*]]: tensor<!tf_type.string>
func.func @check_enqueue_ops_update_for_eval(%arg0: tensor<?x2xi32>, %arg1: tensor<?x2xi32>,
  %arg2 :tensor<?x2xi32>, %arg3: tensor<?xi32>, %arg4: tensor<?xi32>, %arg5: tensor<?xi32>,
  %arg6: tensor<!tf_type.string>, %arg7: tensor<!tf_type.string>) -> () {
  // CHECK: %[[CONST_0:.*]] = "tf.Const"()