func.func @test5DAddWithImplicitBroadcast(%arg0: tensor<1x1x1x3x1xi32>, %arg1 : tensor<1x1x1x1x4xi32>) -> tensor<1x1x1x3x4xi32> {
  %0 = "tf.Add"(%arg0, %arg1): (tensor<1x1x1x3x1xi32>, tensor<1x1x1x1x4xi32>) -> tensor<1x1x1x3x4xi32>
  func.return %0 : tensor<1x1x1x3x4xi32>
// CHECK-LABEL: test5DAddWithImplicitBroadcast
// CHECK: %0 = tfl.add(%arg0, %arg1) <{fused_activation_function = "NONE"}> : (tensor<1x1x1x3x1xi32>, tensor<1x1x1x1x4xi32>) -> tensor<1x1x1x3x4xi32>

func.func @add_with_i32_five_dim_broadcasting(tensor<1x1x1x1x1xi32>, tensor<1xi32>) -> tensor<1x1x1x1x1xi32> {
^bb0(%arg0: tensor<1x1x1x1x1xi32>, %arg1: tensor<1xi32>):
  // CHECK: tfl.add(%arg0, %arg1) <{fused_activation_function = "RELU6"}>
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1x1x1x1x1xi32>, tensor<1xi32>) -> tensor<1x1x1x1x1xi32>
  func.return %0#0 : tensor<1x1x1x1x1xi32>
}

// -----

// CHECK-DAG: %[[CONST_1:.*]] = "tf.Const"() <{value = dense<-43> : tensor<i8>}> : () -> tensor<i8>
// CHECK-DAG: %[[CONST_2:.*]] = "tf.Const"() <{value = dense<-2322> : tensor<1x1x1x1x2xi32>}> : () -> tensor<1x1x1x1x2xi32>

// CHECK: %[[PAD:.*]] = "tf.PadV2"({{.*}}, %[[CONST]], %[[CONST_1]])
// CHECK: %[[CONV:.*]] = "tf.XlaConvV2"(%[[PAD]], %[[WEIGHT]]

  func.return %1 : tensor<1x1x1x1x1xf32>

  // CHECK-DAG: %[[CST1:.*]] = "tfl.no_value"() <{value}> : () -> none
  // CHECK-DAG: %[[CST2:.*]] = arith.constant dense<2.000000e+00> : tensor<1x1x1x1x1xf32>

  %10 = "tf.Pack"(%2, %9, %8, %2) {axis = 0 : i64, device = ""} : (tensor<1x1x2x1xi32>, tensor<1x1x2x1xi32>, tensor<1x1x2x1xi32>, tensor<1x1x2x1xi32>) -> tensor<4x1x1x2x1xi32>
  %11 = "tf.Reshape"(%10, %0) {device = ""} : (tensor<4x1x1x2x1xi32>, tensor<2xi32>) -> tensor<4x2xi32>
  %12 = "tf.Transpose"(%11, %6) {device = ""} : (tensor<4x2xi32>, tensor<2xi32>) -> tensor<2x4xi32>

  %0 = stablehlo.constant() {value = dense<3> : tensor<2x2x1x3xi8>} : () -> tensor<2x2x1x3x!quant.uniform<i8:f32:3, {3.000000e+00, 3.000000e+00, 3.000000e+00}>>
  %1 = stablehlo.constant() {value = dense<5> : tensor<1x1x1x3xi32>} : () -> tensor<1x1x1x3x!quant.uniform<i32:f32:3, {2.000000e+00, 2.000000e+00, 2.000000e+00}>>

  func.func @simple_folding(%arg0: tensor<1x1x1x1xi32>, %arg1: tensor<1x1x1x1xf32>) -> tensor<?x?x?x?xf32> {
    // CHECK: %[[SHAPE:.*]] = "tf.Shape"
    // CHECK: %[[CONV:.*]] = "tf.Conv2DBackpropInput"(%[[SHAPE]]
    // CHECK-SAME: (tensor<4xi32>, tensor<1x1x1x1xf32>, tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32>
    // CHECK: return %[[CONV]] : tensor<1x1x1x1xf32>
    %0 = "tf.Shape"(%arg0) : (tensor<1x1x1x1xi32>) -> tensor<4xi32>