// CHECK-NEXT:  return 
//
module {
  func.func @serving_default() -> tensor<*xi32> {
    %cst = "tf.Const"() <{value = dense<"some_test.txt"> : tensor<!tf_type.string>}> : () -> tensor<!tf_type.string>
    %0 = "tf.VarHandleOp"() <{container = "", shared_name = "Variable"}> : () -> tensor<!tf_type.resource<tensor<!tf_type.string>>>

// CHECK-LABEL: HandleReturnedDequantizeWithAnotherUse
func.func @HandleReturnedDequantizeWithAnotherUse(%arg0: tensor<128x16xf32>) -> (tensor<128x16xf32>, tensor<128xi32>) {
// CHECK-NEXT:  %[[cst:.*]] = arith.constant dense<1> : tensor<i32>
  %cst = arith.constant dense<1> : tensor<i32>
// CHECK-NEXT:  %[[softmax:.*]] = "tfl.softmax"(%arg0) <{beta = 1.000000e+00 : f32}> : (tensor<128x16xf32>) -> tensor<128x16xf32>

// If the tfl.dequantize op wasn't fused, we shouldn't quantize the floating
// point constant.
def : Pat<(TFL_DequantizeOp
             (TFL_QuantizeOp (Arith_ConstantOp F32ElementsAttr:$cst), $qt)),
          (TFL_ConstOp $cst)>;

// Transpose conv supports hybrid computation with quantized weights.
def FoldQuantWeightsIntoTposeConv : Pat<
  (TFL_TransposeConvOp
    $output_shape,
    (TFL_DequantizeOp $quant_weights),

    "Is not a constant.">;

def : Pat<(StableHLO_DivOp $l,
            (StableHLO_ConstantOp:$divisor FloatElementsAttr<32>:$cst)),
          (StableHLO_MulOp $l,
            (StableHLO_DivOp
              (StableHLO_ConstantOp (CloneF32ElementsAttrWithOnes $cst)),
              $divisor)),

func.func @main(tensor<40x37xf32>, tensor<40x37xf32>) -> tensor<40x40xf32> {
^bb0(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>):
  %cst = arith.constant dense<1.0> : tensor<40xf32>
  %0:2 = "tfl.fully_connected"(%arg0, %arg1, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<40x37xf32>, tensor<40x37xf32>, tensor<40xf32>) -> (tensor<40x40xf32>, tensor<40x40xf32>)

// CHECK: %[[CST:.*]] = "tfl.no_value"() <{value}> : () -> none

  %cst = "tf.Const"() {device = "/job:localhost/replica:0/task:0/device:CPU:0", value = dense<3> : tensor<i32>} : () -> tensor<i32>
  %0 = "tf.Less"(%arg0, %cst) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i1>
  %1 = "tf.Less"(%arg1, %cst) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i1>

// CHECK-NEXT: "func.return"([[res]]) : (tensor<*xf32>) -> ()


// -----

// CHECK-LABEL: standalone_same_scale_test
func.func @standalone_same_scale_test(%arg0: tensor<*xf32>) -> tensor<*xf32> {
  %cst = arith.constant dense<[-1, 144]> : tensor<2xi32>
  %0 = "quantfork.qcast"(%arg0) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>

  %cst = "tfl.no_value"() {value = unit} : () -> none

// CHECK-NOT: tf.ReadVariableOp
  %0 = "tf.ReadVariableOp"(%arg0) : (tensor<*x!tf_type.resource<tensor<1xf32>>>) -> tensor<1xf32>
  %cst = "tf.Const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
  %cst_0 = "tf.Const"() <{value = dense<1> : tensor<1xi64>}> : () -> tensor<1xi64>
  %fill = "tf.Fill"(%cst_0, %cst) : (tensor<1xi64>, tensor<f32>) -> tensor<1xf32>
  tf_device.replicate([%0, %fill] as %arg_r0: tensor<1xf32>) {n = 2 : i32} {