}
  func.func private @quantized_conv2d_with_bias_and_relu_fn_0(%arg0: tensor<1x3x4x3xi8>, %arg1: tensor<2x3x3x2xi8>, %arg2: tensor<2xi32>, %arg3: tensor<f32>, %arg4: tensor<i32>, %arg5: tensor<f32>, %arg6: tensor<i32>, %arg7: tensor<f32>, %arg8: tensor<i32>, %arg9: tensor<f32>, %arg10: tensor<i32>) -> tensor<1x3x2x2xi8> {
    %cst = "tf.Const"() {value = dense<127> : tensor<i32>} : () -> tensor<i32>

  %4:2 = "mhlo.reduce"(%arg0, %3, %0, %1) ({
  ^bb0(%arg1: tensor<f32>, %arg2: tensor<i32>, %arg3: tensor<f32>, %arg4: tensor<i32>):
    %7 = "mhlo.compare"(%arg1, %arg3) {comparison_direction = #mhlo<comparison_direction GT>} : (tensor<f32>, tensor<f32>) -> tensor<i1>
    %8 = "mhlo.compare"(%arg1, %arg1) {comparison_direction = #mhlo<comparison_direction NE>} : (tensor<f32>, tensor<f32>) -> tensor<i1>

// CHECK:           return %[[VAL_4]] : tensor<2x3xf16>
// CHECK:         }
func.func @floordiv_f16_broadcast(%arg0: tensor<2x3xf16>, %arg1: tensor<3xf16>) -> tensor<2x3xf16> {
  %0 = "chlo.broadcast_divide"(%arg0, %arg1) {broadcast_dimensions = array<i64: 1>} : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>
  %1 = "chlo.broadcast_divide"(%arg0, %arg1) {broadcast_dimensions = array<i64: 1>} : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>

  // CHECK: %[[OUT:.*]], %[[MEAN:.*]], %[[VAR:.*]] = "mhlo.batch_norm_training"({{.*}}, %arg1, %arg2) <{epsilon = 1.000000e-03 : f32, feature_index = 3 : i64}> : (tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>) -> (tensor<8x8x8x8xf32>, tensor<8xf32>, tensor<8xf32>)

// CHECK: func @while_loop_test(%[[ARG_0:.*]]: tensor<i32>, %[[ARG_1:.*]]: tensor<i32>, %arg2: tensor<*xf32>)
func.func @while_loop_test(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<*xf32>) {
  // CHECK-NEXT: tf.WhileRegion
  %0:2 = "tf.WhileRegion"(%arg0, %arg2) ({
  // CHECK-NEXT: bb0(%[[ARG_3:.*]]: tensor<i32>, %[[ARG_4:.*]]: tensor<*xf32>)
  ^bb0(%arg3: tensor<i32>, %arg4: tensor<*xf32>):
    // CHECK-NEXT: %[[RESULT_1:.*]] = "tf.Identity"(%[[ARG_3]])

  // CHECK: func @f
  // CHECK: %arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v}
  // CHECK: %arg1: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @w}
  // CHECK: %arg2: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @x}
  // CHECK-NOT: %arg3
  // CHECK-NOT: %arg4
  func.func @f(

//CHECK-NEXT: return %0 : tensor<i1>
//CHECK-NEXT:}

func.func @dynamic_update_slice(%arg0: tensor<4x4xi64>, %arg1: tensor<2x3xi64>, %arg2: tensor<i64>, %arg3: tensor<i64>) -> tensor<4x4xi64> {

// CHECK:         tf.GlobalIterId
func.func @globaliterid_not_hoisted(%arg0: tensor<i32>, %arg1: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  %0:2 = "tf.WhileRegion"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
    %1 = "tf.OpA"() {is_stateless = true} : () -> tensor<i1>
    "tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
  ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
    %1 = "tf.GlobalIterId"() : () -> tensor<i64>

func.func @add(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  %0 = "tf.Add"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %0: tensor<1xf32>

// CHECK-LABEL: add
// CHECK:  tfl.add %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<1xf32>
// CHECK:  return
}

func.func @sub(%arg0: tensor<1xi64>, %arg1: tensor<1xi64>) -> tensor<1xi64> {

  // CHECK: "tfl.conv_2d"
}

// CHECK-LABEL: fused_batch_norm_v3_bf16
func.func @fused_batch_norm_v3_bf16(%arg0: tensor<8x8x8x8xbf16>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>, %arg3: tensor<8xf32>, %arg4: tensor<8xf32>) -> tensor<8x8x8x8xbf16> {