%3 = "tf.Const"() {value = dense<1.0> : tensor<f32>} : () -> tensor<f32>
      %4 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>

// We therefore check exemplary 3 axes {rhs_, input_, }quantization_axis from
// previous accumulations.
constexpr std::array<absl::string_view, 3> kQuantizationAxisAttrs = {
    "input_quantization_axis", "quantization_axis", "rhs_quantization_axis"};

// Common suffixes for attributes used in FillQuantizationAttributes.
constexpr std::array<absl::string_view, 2> kSuffixes = {"_min_val", "_max_val"};

  %1 = "tf.UniformRequantize"(
    %0, %input_scale, %input_zp, %output_scale, %output_zp
  ) {
    Tin = "tfdtype$DT_QINT8", Tout = "tfdtype$DT_QINT8", attr_map = "",
    device = "", input_quantization_axis = -1,
    input_quantization_max_val = 127 : i64,
    input_quantization_min_val = -128 : i64,
    output_quantization_axis = -1 : i64,
    output_quantization_max_val = 127 : i64,

  } : (tensor<4xf32>, tensor<f32>, tensor<i32>) -> tensor<4x!tf_type.qint8>
  %1 = "tf.UniformRequantize"(%0, %scales_0, %zps_0, %scales_1, %zps_1) {
    input_quantization_axis = -1 : i64, input_quantization_min_val = -128 : i64, input_quantization_max_val = 127 : i64,
    output_quantization_axis = -1 : i64, output_quantization_min_val = -128 : i64, output_quantization_max_val = 127 : i64

    %requantize = "tf.UniformRequantize"(%input, %input_scale, %input_zp, %out_scale, %out_zp) {
            Tin = "tfdtype$DT_QINT32",
            Tout = "tfdtype$DT_QINT8",
            input_quantization_axis = -1,
            input_quantization_min_val = -2147483648,
            input_quantization_max_val = 2147483647,
            output_quantization_axis = -1,
            output_quantization_min_val = -128,

  // expected-error @below {{'tf.UniformRequantize' op quantization_axis is -1, scales must have 0 rank.}}
  %0 = "tf.UniformRequantize"(%arg0, %scales_0, %zps_0, %scales_1, %zps_1) {
    input_quantization_axis = -1 : i64, input_quantization_min_val = -2147483648 : i64, input_quantization_max_val = 2147483647 : i64,
    output_quantization_axis = -1 : i64, output_quantization_min_val = -128 : i64, output_quantization_max_val = 127 : i64

Per-tensor and per-axis quantization supported cases are followings:
* per-tensor -> per-tensor
* per-tensor -> per-axis
* per-axis -> per-axis where input_quantization_axis equals output_quantization_axis.
i.e. At least one among input_quantization_axis and output_quantization_axis must be -1, or two must be equal.
  }];

  let arguments = (ins