// 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

    func.return %0#0 : tensor<8x8x8x8xf32>
  }

  // CHECK-LABEL: fusedBatchNormV3_training

  %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>

      %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>

func.func @FoldSumKeepDim(%arg0: tensor<8x128xf32>) -> tensor<8x1xf32> {
  %cst = arith.constant dense<1> : tensor<1xi32>
  %cst_1 = arith.constant dense<[8, 1]> : tensor<2xi32>
  %0 = "tfl.sum"(%arg0, %cst) {keep_dims = false} : (tensor<8x128xf32>, tensor<1xi32>) -> tensor<8xf32>
  %1 = "tfl.reshape"(%0, %cst_1) : (tensor<8xf32>, tensor<2xi32>) -> tensor<8x1xf32>
  func.return %1 : tensor<8x1xf32>

// CHECK-LABEL: FoldSumKeepDim

  // CHECK-NEXT: %[[NEW_SIZE:.*]] = "tf.AddV2"(%[[ZERO]], %[[CONST1]]) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
  %push = "tf.TensorListPushBack"(%id, %elem) : (tensor<!tf_type.variant<tensor<f32>>>, tensor<f32>) -> tensor<!tf_type.variant<tensor<f32>>>
  // CHECK-NEXT: %[[COPY:.*]] = "tf.Identity"(%[[UPDATE]])
  // CHECK-NEXT: %[[CONST1_1:.*]] = "tf.Const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>

// 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>

func.func @tensorlistConst(%arg0 : tensor<1xi32>) -> tensor<2x3xi32> {
  // CHECK-DAG: %[[ELEMENT0:.*]] = "tf.Const"() <{value = dense<[0, 1, 2]> : tensor<3xi32>}> : () -> tensor<3xi32>
  // CHECK-DAG: %[[ELEMENT1:.*]] = "tf.Const"() <{value = dense<[3, 4, 5]> : tensor<3xi32>}> : () -> tensor<3xi32>
  // CHECK: %[[LIST:.*]] = "tf.Pack"(%[[ELEMENT0]], %[[ELEMENT1]]) <{axis = 0 : i64}> : (tensor<3xi32>, tensor<3xi32>) -> tensor<2x3xi32>

// CHECK:           [[VAL_7:%.*]] = "tfl.slice"([[VAL_1]], [[VAL_3]], [[VAL_5]]) : (tensor<1x8x8x1024xf32>, tensor<4xi32>, tensor<4xi32>) -> tensor<1x8x8x256xf32>
// CHECK:           [[VAL_8:%.*]] = "tfl.slice"([[VAL_1]], [[VAL_4]], [[VAL_5]]) : (tensor<1x8x8x1024xf32>, tensor<4xi32>, tensor<4xi32>) -> tensor<1x8x8x256xf32>

// CHECK:           [[VAL_23:%.*]] = "tf.Reshape"([[VAL_15]]#1, [[VAL_22]]) : (tensor<1x10xf32>, tensor<1xi32>) -> tensor<10xf32>
// CHECK-DAG:       [[VAL_24:%.*]] = "tf.Const"() <{value = dense<-1> : tensor<1xi32>}> : () -> tensor<1xi32>
// CHECK:           [[VAL_25:%.*]] = "tf.Reshape"([[VAL_15]]#2, [[VAL_24]]) : (tensor<1x10xf32>, tensor<1xi32>) -> tensor<10xf32>