%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>
  }

      %arg1: tensor<4x3x!quant.uniform<i8<-127:127>:f32:1, {5.000000e-03,5.000000e-03,5.000000e-03}>>
    ) -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e-03:-3>> attributes {_from_xla_call_module} {
    %0 = stablehlo.constant() {value = dense<2> : tensor<1x3xi8>} : () -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e-03:-3>>
    return %0 : tensor<1x3x!quant.uniform<i8:f32, 1.000000e-03:-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(

  return %2 : tensor<?x2x2x1xf32>
}

// -----

// CHECK-LABEL: func @dot_general_add_add
func.func @dot_general_add_add(
    %lhs: tensor<?x?x6xi8>, %rhs: tensor<6x8xi8>,
    %zp_offset: tensor<8xi32>, %bias: tensor<8xi32>
  ) -> tensor<?x?x8xi32> {
  // CHECK: %[[dot:.*]] = "mhlo.dot_general"
  // CHECK: %[[combined:.*]] = chlo.broadcast_add %[[zp_offset:.*]], %[[bias:.*]]

  %q1 = "quantfork.qcast"(%arg0) {volatile} : (tensor<1x3xf32>) -> tensor<1x3x!quant.uniform<i8:f32, 6.000000e-03:-128>>
  %q2 = "quantfork.qcast"(%cst) {volatile} : (tensor<3x2xf32>) -> tensor<3x2x!quant.uniform<i8<-127:127>:f32, 5.000000e-03>>

func.func @select(
    %arg0: tensor<1x3xi1>,
    %arg1: tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>>,
    %arg2: tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>>
  ) -> tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>> {
  %0 = "stablehlo.select"(%arg0, %arg1, %arg2) : (
    tensor<1x3xi1>,
    tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>>,
    tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>>

  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x3x3x!quant.uniform<i8:f32, 0.003:-128>>} : (tensor<1x3x3xf32>) -> tensor<1x3x3x!quant.uniform<i8:f32, 0.003:-128>>
  %1 = "tfl.logistic"(%0) : (tensor<1x3x3x!quant.uniform<i8:f32, 0.003:-128>>) -> tensor<1x3x3x!quant.uniform<i8:f32, 3.906250e-03:-128>>

  %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>>

}

// CHECK-LABEL: EliminateRedundantLogicalOr
func.func @EliminateRedundantLogicalOr(%arg0: tensor<1xi1>, %arg1: tensor<2x3xi1>) -> (tensor<1xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<1xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>) {
  %cst_false0 = arith.constant dense<false> : tensor<1xi1>

}

TEST_F(ConvertTfQuantToMhloIntTest, UniformRequantizeToValidGraph) {
  constexpr absl::string_view kProgram = R"mlir(
func.func @main(%input: tensor<10xi8>) -> tensor<10xi8> {
  %input_scale = "tf.Const"() { value = dense<0.2235> : tensor<f32> } : () -> tensor<f32>
  %input_zp = "tf.Const"() { value = dense<-2> : tensor<i32> } : () -> tensor<i32>