func.func @remove_quantized_io(
  %arg0: tensor<1x10x!quant.uniform<i8:f32, 0.1:-128>>,
  %arg1: tensor<1x5xf32>) -> (tensor<1x10x!quant.uniform<i8:f32, 0.2:42>>, tensor<1x5xf32>) {
  %0 = "tf.MyRequantize"(%arg0) : (tensor<1x10x!quant.uniform<i8:f32, 0.1:-128>>) -> tensor<1x10x!quant.uniform<i8:f32, 0.2:42>>
  %1 = "tf.Intermediate"(%arg1) : (tensor<1x5xf32>) -> tensor<1x5xf32>
  func.return %0, %1 : tensor<1x10x!quant.uniform<i8:f32, 0.2:42>>, tensor<1x5xf32>

    %0 = stablehlo.convert %arg0 : (tensor<1x2xf32>) -> tensor<1x2xi8>
    return %0 : tensor<1x2xi8>
  }
// CHECK: @uniform_quantize_0
  func.func private @uniform_quantize_1(%arg0: tensor<1x3xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1x1xi8>) -> tensor<1x3xi8> {
    %0 = stablehlo.convert %arg0 : (tensor<1x3xf32>) -> tensor<1x3xi8>
    return %0 : tensor<1x3xi8>
  }

  constexpr absl::string_view kProgram = R"mlir(
func.func @main(
    %input: tensor<10x10xi8>, %input_scale: tensor<f32>, %input_zp: tensor<i32>,
    %output_scale: tensor<10xf32>, %output_zp: tensor<10xi32>
  ) -> tensor<10x10xi8> {
  %0 = "tf.Cast"(%input) {} : (tensor<10x10xi8>) -> tensor<10x10x!tf_type.qint8>
  %1 = "tf.UniformRequantize"(
    %0, %input_scale, %input_zp, %output_scale, %output_zp

func.func @quant_raw_data(%arg0: tensor<1x10x!quant.uniform<i8:f32, 0.1:1>>) -> tensor<1x10x!quant.uniform<i8:f32, 0.2:2>> {
  %0 = "tfr.cast"(%arg0) : (tensor<1x10x!quant.uniform<i8:f32, 0.1:1>>) -> !tfr.tensor
  %1 = tfr.quant_raw_data(%0) : (!tfr.tensor) -> !tfr.tensor
  %2 = tfr.call @tf__risc(%1) : (!tfr.tensor) -> !tfr.tensor
  %3 = "tfr.cast"(%2) : (!tfr.tensor) -> tensor<1x10x!quant.uniform<i8:f32, 0.3:3>>

// CHECK:           return %[[VAL_2]] : tensor<1x4xi8>
// CHECK:         }
func.func @bitwise_or_broadcast_chlo(%arg0: tensor<1xi8>, %arg1: tensor<1x4xi8>) -> tensor<1x4xi8> {
  %0 = "chlo.broadcast_or"(%arg0, %arg1) {broadcast_dimensions = array<i64: 1>} : (tensor<1xi8>, tensor<1x4xi8>) -> tensor<1x4xi8>
  func.return %0 : tensor<1x4xi8>
}

// CHECK-LABEL:   func @bitwise_or_dynamic(

    func.return %2, %3 : tensor<1x10xf32>, tensor<1x10xi64>
  }

  // CHECK: %[[RESOURCE:.*]] = "tfl.var_handle"() <{container = "c", shared_name = "a"}> : () -> tensor<!tf_type.resource<tensor<1x10xf32>>>
  // CHECK: %[[RESOURCE_1:.*]] = "tfl.var_handle"() <{container = "c", shared_name = "b"}> : () -> tensor<!tf_type.resource<tensor<1x10xi64>>>

  %cst = "tfl.pseudo_qconst"() {qtype = tensor<1x2x!quant.uniform<u8:f32, 1.0>>, value = dense<-76> : tensor<1x2xi8>} : () -> tensor<1x2x!quant.uniform<u8:f32, 1.0>>

tensor<i8>, tensor<f64>, tensor<!tf_type.resource>)

# CHECK-SUBTYPE: func @main(%arg0: tensor<10xi32>, %arg1: tensor<!tf_type.variant<tensor<10xf32>>>, %arg2: tensor<10xi8>, %arg3: tensor<10xf64>, %arg4: tensor<!tf_type.resource<tensor<10xi32>>>) -> (tensor<10xi32>, tensor<!tf_type.variant<tensor<10xf32>>>, tensor<10xi8>, tensor<10xf64>, tensor<!tf_type.resource<tensor<10xi32>>>)...

// CHECK-LABEL: quantized_tensor
func.func @quantized_tensor(%arg0: tensor<1x10x!quant.uniform<i8:f32, 0.0038396485615521669:-128>>) -> tensor<1x10x!quant.uniform<i8:f32, 3.906250e-03:-128>> {
  %0 = "tf.Intermediate"(%arg0) : (tensor<1x10x!quant.uniform<i8:f32, 0.0038396485615521669:-128>>) -> tensor<1x10x!quant.uniform<i8:f32, 3.906250e-03:-128>>
  func.return %0 : tensor<1x10x!quant.uniform<i8:f32, 3.906250e-03:-128>>

    %cst_1 = "tfl.pseudo_qconst"() {qtype = tensor<1x20x!quant.uniform<i8:f32, 0.006:-34>>, value = dense<1> : tensor<1x20xi8>} : () -> tensor<1x20x!quant.uniform<i8:f32, 0.006:-34>>
    %cst_2 = "tfl.no_value"() {value = unit} : () -> none