{
  %scales = "tf.Const"() { value = dense<1.0> : tensor<f32> } : () -> tensor<f32>
  %zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

  // CHECK: %[[qint:.*]] = "tf.UniformQuantize"
  // CHECK: %[[int:.*]] = "tf.Cast"(%[[qint]]) <{Truncate = false}> : (tensor<1x!tf_type.qint8>) -> tensor<1xi8>
  %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {

  // CHECK: return %[[DEQUANTIZE]] : tensor<2xf32>

  %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {
    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
  } : (tensor<2xf32>, tensor<f32>, tensor<i32>) -> tensor<2x!tf_type.qint8>
  %1 = "tf.UniformDequantize"(%0, %scales, %zps) {
    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64

                   cast_qtype.dyn_cast<quant::UniformQuantizedPerAxisType>()) {
      SmallVector<float> scales(qtype.getScales().begin(),
                                qtype.getScales().end());
      SmallVector<int32_t> zps(qtype.getZeroPoints().begin(),
                               qtype.getZeroPoints().end());
      const size_t num_channels = qtype.getScales().size();

      auto scales_type = RankedTensorType::get(