%val = "tf.ReadVariableOp"(%arg1) : (tensor<*x!tf_type.resource>) -> (tensor<0xf32>)
    %0 = "tf.AddV2"(%val, %arg0) : (tensor<0xf32>, tensor<*xf32>) -> tensor<*xf32>
    func.return %0 : tensor<*xf32>
  }

  // CHECK: func private @testIfElse(%arg0: tensor<*xf32>) -> tensor<*xf32>
  func.func private @testIfElse(%arg0:tensor<*xf32>, %arg1:tensor<*x!tf_type.resource>) -> tensor<*xf32> {

    // CHECK: %[[MUL:.*]] = "tf.Mul"{{.*}} (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
    // CHECK: %[[UNKNOWN:.*]] = "tf.Unknown"(%[[MUL]], %[[MUL]]) : (tensor<1xf32>, tensor<1xf32>) -> tensor<*xf32>
    // CHECK: return %[[UNKNOWN]] : tensor<*xf32>
    %0 = "tf.Mul"(%arg0, %arg0) : (tensor<1xf32>, tensor<1xf32>) -> tensor<*xf32>
    %1 = "tf.Unknown"(%0, %0) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

  %3 = "tf.AddN"(%1, %arg0, %1) : (tensor<2xf32>, tensor<2xf32> , tensor<2xf32>) -> tensor<2xf32>
  %4 = "tf.AddN"(%1, %1) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  %5 = "tf.AddN"(%arg0, %1, %0) : (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  func.return %2, %3, %4, %5: tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>
}

// CHECK-LABEL: func @addNWithZerosInt

  // CHECK-NEXT: %[[CONST1:.*]] = "tf.Const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>
  // CHECK-NEXT: %[[NEW_SIZE:.*]] = "tf.AddV2"(%[[READ_SIZE]], %[[CONST1]]) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
  // CHECK-NEXT: "tf.AssignVariableOp"(%[[SIZE]], %[[NEW_SIZE]]) : (tensor<!tf_type.resource<tensor<1xi32>>>, tensor<1xi32>) -> ()

  %cst_1 = arith.constant dense<0> : tensor<4xi32>
  %cst_2 = arith.constant dense<0.000000e+00> : tensor<1xf32>
  %0 = "tfl.quantize"(%cst_2) {qtype = tensor<1x!quant.uniform<i32:f32, 1.0>>, volatile} : (tensor<1xf32>) -> tensor<1x!quant.uniform<i32:f32, 1.0>>
  %1 = "tfl.dequantize"(%0) : (tensor<1x!quant.uniform<i32:f32, 1.0>>) -> tensor<1xf32>
  %cst_3 = arith.constant dense<[[[[1.0]], [[1.0]], [[1.0]]]]> : tensor<1x3x1x1xf32>

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

  // CHECK-LABEL: fusedBatchNormV3_training

  %2 = chlo.broadcast_add %1, %bias : (
      tensor<?x2x2x1xi32>, tensor<1xi32>) ->tensor<?x2x2x1xi32>
  return %2 : tensor<?x2x2x1xi32>
}

// -----

// CHECK-LABEL: func @convolution_add_add_static
func.func @convolution_add_add_static(
    %lhs: tensor<2x3x2x1xi8>, %rhs: tensor<2x1x1x1xi8>,
    %zp_offset: tensor<2x2x2x1xi32>, %bias: tensor<1xi32>
  ) -> tensor<2x2x2x1xi32> {
  // CHECK-DAG: %[[conv:.*]] = mhlo.convolution

  func.func @batch_matmul_two_tensors_with_dynamic_shape(%arg0: tensor<2x?x?xf32>, %arg1: tensor<2x?x?xf32>) -> (tensor<2x?x?xf32>) {
    %cst = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
    %cst_0 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
    %cst_1 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
    %cst_2 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>

  func.return %0#0 : tensor<?x?x?x?xf32>
}

// -----

// CHECK-LABEL: fusedBatchNormV3_training_dynamic_unsupported1

  // return %[[SUM2]]
  %0 = "tf.AddN"(%arg0, %arg1, %arg2, %arg3) : (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

// CHECK-LABEL: func @addN_5
func.func @addN_5(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>, %arg2: tensor<*xf32>, %arg3: tensor<*xf32>, %arg4: tensor<*xf32>) -> tensor<*xf32> {
  // CHECK: %[[SUM0:.*]] = "tf.AddV2"(%arg0, %arg1)