%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>
  }

  // CHECK-LABEL: func.func private @merge_relu_fusion
  func.func private @merge_relu_fusion(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> {
    %0 = stablehlo.constant() {value = dense<127> : tensor<4x3xi8>} : () -> tensor<4x3x!quant.uniform<i8<-127:127>:f32:1, {5.000000e-03,5.000000e-03,5.000000e-03}>>
    %1 = stablehlo.uniform_quantize %arg0 : (tensor<1x4xf32>) -> tensor<1x4x!quant.uniform<i8:f32, 6.000000e-03:-128>>

  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:.*]]

  // CHECK-LABEL: @qu8
  // CHECK: <{qtype = tensor<3x!quant.uniform<u8<1:255>:f32, 1.000000e+00>>, value = dense<1> : tensor<3xi8>}> : () -> tensor<3x!quant.uniform<u8<1:255>:f32, 1.000000e+00>>
  %0 = "tfl.pseudo_qconst"() { qtype = tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>>, value = dense<1> : tensor<3xi8>} : () -> tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>>
  func.return %0 : tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>>
}

// 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(

}

// 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>

    return %0 : tensor<1x3xf32>
  }
}

// CHECK-LABEL: quantize_dot_general_per_tensor
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x2xf32>
// CHECK: %[[CST:.+]] = stablehlo.constant() <{value = dense<127> : tensor<2x3xi8>}> : () -> tensor<2x3x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>

TEST_F(QuantizationReportTest, InitializeWithModuleOp) {
  constexpr absl::string_view kQuantizedDotGeneral = R"mlir(
    func.func @main(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> {
      %0 = stablehlo.constant() {value = dense<127> : tensor<2x3xi8>} : () -> tensor<2x3x!quant.uniform<i8<-127:127>:f32:1, {1.000000e+0,2.000000e+0,3.000000e+0}>>
      %1 = stablehlo.uniform_quantize %arg0 : (tensor<1x2xf32>) -> tensor<1x2x!quant.uniform<i8:f32, 4.000000e+0>>

}

func.func @RemoveTrivialSubInt8(%arg0: tensor<2x2xi8>) -> tensor<2x2xi8> {
  %cst = arith.constant dense<0> : tensor<2x2xi8>
  %0 = "tf.Sub"(%arg0, %cst) : (tensor<2x2xi8>, tensor<2x2xi8>) -> tensor<2x2xi8>
  func.return %0 : tensor<2x2xi8>

  // CHECK-LABEL: RemoveTrivialSubInt8
  // CHECK-NEXT: return %arg0 : tensor<2x2xi8>
}

func.func @RemoveTrivialMul(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {

  // CHECK: %[[WHILE_RESULT:.*]]:2 = mhlo.while(%[[ARG0:.*]] = %[[INT0]], %[[ARG1:.*]] = %[[INT1]])
  // CHECK-SAME: tensor<2x2xi8>, tensor<2x2xi8>

  // CHECK: cond

  // CHECK: do
  // CHECK: mhlo.return %[[ARG0]], %[[ARG1]] : tensor<2x?xi8>, tensor<?x2xi8>

  %2:2 = "tf.WhileRegion"(%0, %1) ({
  ^bb0(%carg0: tensor<2x2x!tf_type.qint8>, %carg1: tensor<2x2x!tf_type.qint8>):