func.func @quant_act_range(%arg0: !tfr.attr, %arg1: f32, %arg2: i64) -> !tfr.tensor {
  %0:2 = "tfr.quant_act_range"(%arg0, %arg1, %arg2) : (!tfr.attr,f32,i64) -> (!tfr.tensor,!tfr.tensor)
  func.return %0#0 : !tfr.tensor
}

// -----

// CHECK-LABEL: quant_rescale
func.func @quant_rescale(%arg0: !tfr.tensor, %arg1: !tfr.tensor, %arg2: i64) -> !tfr.tensor {
  %0 = "tfr.quant_rescale"(%arg0, %arg1, %arg2) : (!tfr.tensor, !tfr.tensor, i64) -> (!tfr.tensor)

func.func @replicated(%arg0: tensor<!tf_type.resource<tensor<10x3xf32>>>, %arg1: tensor<!tf_type.resource<tensor<10x3xf32>>>, %arg2: tensor<!tf_type.resource<tensor<10x3xf32>>>, %arg3: tensor<!tf_type.resource<tensor<10x3xf32>>>) -> tensor<!tf_type.resourc...

  %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>
    "tf.Yield"(%arg2, %arg3) : (tensor<i32>, tensor<i32>) -> ()

  // CHECK: %[[ARG1_TPOSE:[0-9]*]] = "tf.Transpose"(%arg1, %[[ARG_PERM]])

  // CHECK: "tf.FusedBatchNormGradV3"
  // CHECK-SAME: (%[[ARG0_TPOSE]], %[[ARG1_TPOSE]], %arg2, %arg2, %arg2, %arg2)
  // CHECK-SAME: data_format = "NCHW"
  // CHECK-SAME: (tensor<1x64x28x28xf32>, tensor<1x64x28x28xf32>,
  // CHECK-SAME: -> (tensor<1x64x28x28xf32>,

  // CHECK: %[[RES_PERM:.*]] = "tf.Const"()

    %add, %add_control = tf_executor.island wraps "tf.Add"(%arg2, %arg2) : (tensor<f32>, tensor<f32>) -> tensor<f32>
    %mul, %mul_control = tf_executor.island wraps "tf.Mul"(%arg2, %arg2) : (tensor<f32>, tensor<f32>) -> tensor<f32>

func.func @testConv2D(tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x32x32x16xf32> {
^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>):
  // CHECK: _arithmetic_count = 230686720 : i64

    def create(self, arg0: Location, arg1: Value, arg2: Value) -> Operation: ...

class Tf_GreaterEqualOp:
    def __init__(self, *args, **kwargs) -> None: ...
    def create(self, arg0: Location, arg1: Value, arg2: Value) -> Operation: ...

class Tf_GreaterOp:
    def __init__(self, *args, **kwargs) -> None: ...
    def create(self, arg0: Location, arg1: Value, arg2: Value) -> Operation: ...

class Tf_LegacyCallOp:

  %0 = "tfl.slice"(%arg0, %arg1, %arg2) : (tensor<4x384x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x384x32xf32>
  func.return %0 : tensor<1x384x32xf32>
}

// PARTIAL-LABEL: @arg_reuse_test_2

        log(logLevel, throwable, format, new Object[]{arg});
    }

    private void log(LogLevel logLevel, Throwable throwable, String format, Object arg1, Object arg2) {
        log(logLevel, throwable, format, new Object[]{arg1, arg2});
    }

    private void log(LogLevel logLevel, Throwable throwable, String format, Object[] args) {
        FormattingTuple tuple = MessageFormatter.arrayFormat(format, args);

  %0:1 = "tfl.while"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
    %1 = func.call @WhileOp_cond(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> tensor<i1>
    "tfl.yield"(%1) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
    %1:2 = func.call @WhileOp_body(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>)