func.func @RemoveRedundantPack(%arg0: tensor<2x5xf32>) -> (tensor<2x5xf32>, tensor<5xf32>) {
  %0:2 = "tfl.unpack"(%arg0) {axis = 0 : i32, num = 2 : i32} : (tensor<2x5xf32>) -> (tensor<5xf32>, tensor<5xf32>)
  %1 = "tfl.pack"(%0#0, %0#1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<5xf32>, tensor<5xf32>) -> (tensor<2x5xf32>)
  func.return %1, %0#0: tensor<2x5xf32>, tensor<5xf32>
  // CHECK: %[[UNPACK:.*]]:2 = "tfl.unpack"
  // CHECK-NOT: pack

  // CHECK-LABEL: func @main(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32>
  func.func @main(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<*xi32> {
    // CHECK: %[[RESULT:.*]] = "tf.AddV2"
    // CHECK-SAME: (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
    // CHECK: return %[[RESULT]] : tensor<1xi32>
    %0 = "tf.Cast"(%arg0) : (tensor<1xi32>) -> tensor<*xi32>
    %1 = "tf.Cast"(%arg1) : (tensor<1xi32>) -> tensor<*xi32>

// CHECK-LABEL: func @testBroadcastGradientArgs
func.func @testBroadcastGradientArgs(%s0: tensor<4xi32>, %s1: tensor<4xi32>) -> (tensor<1xi32>, tensor<0xi32>) {
  // CHECK:     tf.BroadcastGradientArgs
  %r0, %r1 = "tf.BroadcastGradientArgs"(%s0, %s1) : (tensor<4xi32>, tensor<4xi32>) -> (tensor<1xi32>, tensor<0xi32>)
  func.return %r0, %r1 : tensor<1xi32>, tensor<0xi32>
}

// -----

// CHECK-LABEL: @acos

// CHECK: %[[v5:.*]] = "tf.Gather"(%3, %[[cst_1]]) <{validate_indices = true}> : (tensor<3xi32>, tensor<2xi32>) -> tensor<2xi32>
// CHECK: %[[v6:.*]] = "tf.Gather"(%4, %[[cst_2]]) <{validate_indices = true}> : (tensor<4xi32>, tensor<1xi32>) -> tensor<1xi32>
// CHECK: %[[v7:.*]] = "tf.Concat"(%[[cst_3]], %5, %6) : (tensor<i32>, tensor<2xi32>, tensor<1xi32>) -> tensor<3xi32>

// CHECK:  [[SLICE_SIZE:%.*]] = "tf.Concat"([[ZERO_2]], [[LEADING_DIM]], [[PARTIAL_SIZE]]) : (tensor<i32>, tensor<1xi32>, tensor<?xi32>) -> tensor<?xi32>
// CHECK:  [[SLICE:%.*]] = "tf.Slice"([[INPUT]], [[START_POS]], [[SLICE_SIZE]]) : (tensor<5xf32>, tensor<?xi32>, tensor<?xi32>) -> tensor<*xf32>

      %outputs_10, %control_11 = tf_executor.island wraps "tf.Pack"(%outputs_6, %outputs_8) {axis = 0 : i64, device = ""} : (tensor<0xi32>, tensor<0xi32>) -> tensor<*xi32>
      %outputs_12, %control_13 = tf_executor.island wraps "tf.Max"(%outputs_10, %outputs_4) {device = "", keep_dims = false} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>

// CHECK-DAG: %[[padding_rank_1:.*]] = "tf.Concat"({{.*}}, {{.*}}, {{.*}}, {{.*}}, {{.*}}, {{.*}}, {{.*}}, {{.*}}, {{.*}}) : (tensor<i32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<8xi32>

  %outputs_0 = "tf.Const"() {value = dense<224> : tensor<2xi32>} : () -> tensor<2xi32>
  %outputs_2 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %outputs_4 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %outputs_6 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>

// CHECK-DAG: %[[BROADCAST_DIM:.+]] = arith.constant dense<{{\[3, 2, 1, 1, 4\]}}> : tensor<5xi32>
// CHECK-DAG: %[[EXPAND_DIM1:.+]] = arith.constant dense<3> : tensor<1xi32>
// CHECK-DAG: %[[EXPAND_DIM0:.+]] = arith.constant dense<2> : tensor<1xi32>
// CHECK-DAG: %[[PERM:.+]] = arith.constant dense<{{\[1, 0, 2\]}}> : tensor<3xi32>

  %261 = "tf.Shape"(%254) {device = ""} : (tensor<?x?xi64>) -> tensor<2xi32>
  %262 = "tf.StridedSlice"(%261, %6, %15, %16) {begin_mask = 0 : i64, device = "", ellipsis_mask = 0 : i64, end_mask = 1 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<2xi32>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<0xi32>