%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>
  }

func.func @dequantize_pushdown(%arg0: tensor<1000x1000x!quant.uniform<i8:f32, 7.812500e-03>>, %arg1: tensor<1x1xi32>) -> tensor<1x1x1000xf32> {
  %0 = "tfl.dequantize"(%arg0) : (tensor<1000x1000x!quant.uniform<i8:f32, 7.812500e-03>>) -> tensor<1000x1000xf32>
  %1 = "tfl.gather"(%0, %arg1) {axis = 0 : i32, batch_dims = 0 : i32}: (tensor<1000x1000xf32>, tensor<1x1xi32>) -> tensor<1x1x1000xf32>
  func.return %1 : tensor<1x1x1000xf32>

// CHECK-NEXT: tfl.gather

    %21 = "tf.Cast"(%20) {Truncate = false} : (tensor<2x2xf32>) -> tensor<2x2xi8>
    %22 = "tf.Identity"(%21) {device = ""} : (tensor<2x2xi8>) -> tensor<2x2xi8>
    %23 = "tf.Identity"(%22) {device = ""} : (tensor<2x2xi8>) -> tensor<2x2xi8>
    %24 = "tf.Cast"(%23) : (tensor<2x2xi8>) -> tensor<2x2xi32>
    %25 = "tf.Sub"(%24, %cst_4) : (tensor<2x2xi32>, tensor<i32>) -> tensor<2x2xi32>

// 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: %[[cst:.*]] = arith.constant dense<[-1, 3]> : tensor<2xi32>
// CHECK-NEXT: %[[q1:.*]] = "tfl.quantize"(%arg0) <{qtype = tensor<1x1x3x!quant.uniform<i16:f32, {{.*}}>>}> {volatile} : (tensor<1x1x3xf32>) -> tensor<1x1x3x!quant.uniform<i16:f32, {{.*}}>>
// CHECK-NEXT: %[[dq1:.*]] = "tfl.dequantize"(%[[q1]]) : (tensor<1x1x3x!quant.uniform<i16:f32, {{.*}}>>) -> tensor<1x1x3xf32>

func.func @cast_op_qint8_int8(%arg0: tensor<1x!tf_type.qint8>) -> tensor<1xi8> {
  // CHECK: "tf.Cast"(%arg0) <{Truncate = false}> : (tensor<1xi8>) -> tensor<1xi8>
  %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x!tf_type.qint8>) -> tensor<1xi8>
  func.return %0: tensor<1xi8>
}

// -----

// CHECK-LABEL: func @cast_op_int8_qint8
func.func @cast_op_int8_qint8(%arg0: tensor<1xi8>) -> tensor<1x!tf_type.qint8> {

    ) -> 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>>
  }

  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: %[[quant:.*]] = "tfr.cast"(%arg0) : (tensor<1x10xi8>) -> !tfr.tensor
// CHECK: %[[scale_cast:.*]] = "tfr.cast"(%[[scale]])
// CHECK: %[[zp_cast:.*]] = "tfr.cast"(%[[zp]])
// CHECK: %[[requant:.*]] = tfr.call @tf__requantize(%[[quant]], %[[scale_cast]], %[[zp_cast]])
// CHECK: %[[result:.*]] = "tfr.cast"(%[[requant]])
// CHECK-NOT: quant.uniform
// CHECK: return %[[result]], %[[float_resunt:.*]] : tensor<1x10xi8>, tensor<1x5xf32>
}

// CHECK-DAG: %[[cst_1:.*]] = "tf.Const"() <{value = dense<-1> : tensor<1xi64>}> : () -> tensor<1xi64>
// CHECK: %[[arg1_i64:.*]] = "tf.Cast"(%arg1) <{Truncate = false}> : (tensor<1xi32>) -> tensor<1xi64>
// CHECK: %[[tensor_scatter_update:.*]] = "tf.TensorScatterUpdate"(%[[cst]], %[[cst_0]], %[[arg1_i64]]) : (tensor<2xi64>, tensor<1x1xi64>, tensor<1xi64>) -> tensor<2xi64>