%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>
  }

  %1 = "tfl.quantize"(%arg0) {qtype = tensor<1x2x!quant.uniform<u8:f32, 1.0>>, volatile} : (tensor<1x2xf32>) -> tensor<1x2x!quant.uniform<u8:f32, 1.0>>
  %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>>

func.func @CheckLegacyQuantizeAdd() -> tensor<1x2x!quant.uniform<i8:f32, 0.0078431372549019607:-128>> {
  %cst = arith.constant dense<[[1.000000e+00, 2.000000e+00]]> : tensor<1x2xf32>
  %0 = "tfl.quantize"(%cst) {qtype = tensor<1x2x!quant.uniform<i8:f32, 0.0078431372549019607:-128>>, volatile} : (tensor<1x2xf32>) -> tensor<1x2x!quant.uniform<i8:f32, 0.0078431372549019607:-128>>
  func.return %0 : tensor<1x2x!quant.uniform<i8:f32, 0.0078431372549019607:-128>>

// 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: return %[[ADD]] : tensor<14xf32>
}

func.func @scatter_nd_updates_bool(%arg0: tensor<1x24xi1>, %arg1: tensor<1x2x2xi32>, %arg2: tensor<1x2xi1>) -> tensor<1x24xi1> {
  %0 = "tf.TensorScatterUpdate"(%arg0, %arg1, %arg2) : (tensor<1x24xi1>, tensor<1x2x2xi32>, tensor<1x2xi1>) -> tensor<1x24xi1>
  func.return %0 : tensor<1x24xi1>

// CHECK-LABEL: scatter_nd_updates_bool(

func.func @equal_broadcast(%arg0: tensor<1xi32>, %arg1: tensor<1x2xi32>) -> tensor<1x2xi1> {
  // CHECK-DAG: %[[LHS_BCAST:.+]] = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<1> : tensor<1xi64>}>
  // CHECK-NEXT: mhlo.compare EQ, %[[LHS_BCAST]], %arg1
  %0 = "tf.Equal"(%arg0, %arg1) : (tensor<1xi32>, tensor<1x2xi32>) -> tensor<1x2xi1>
  func.return %0: tensor<1x2xi1>
}

// CHECK: %[[CONST:.+]] = stablehlo.constant() <{value = dense<127> : tensor<1x2xi8>}> : () -> tensor<1x2x!quant.uniform<i8:f32, {{.*}}>>
// CHECK: %[[CONST_0:.+]] = stablehlo.constant() <{value = dense<127> : tensor<2x3xi8>}> : () -> tensor<2x3x!quant.uniform<i8<-127:127>:f32:1, {{.*}}>>
// CHECK: %[[UNIFORM_QUANTIZE:.+]] = stablehlo.uniform_quantize %[[ARG]] : (tensor<1x2xf32>) -> tensor<1x2x!quant.uniform<i8:f32, {{.*}}>>

func.func @dequantize_pushdown_gather_with_reduction(%arg0: tensor<2xi32>) -> tensor<2x2xf32> {
  %w = "tfl.pseudo_qconst"() {qtype = tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<12x2xi8>} : () -> tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
  %dq_w = "tfl.dequantize"(%w) : (tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<12x2xf32>

    ) -> 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: %[[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>):