quantization_options.mutable_quantization_method()->set_preset_method(
          quantization::QuantizationMethod::METHOD_DYNAMIC_RANGE_INT8);
      quantization_options.set_op_set(quantization::UNIFORM_QUANTIZED);
      quantization_options.set_min_num_elements_for_weights(
          kWeightsMinNumElementsDefault);
      quantization::AddQuantizePtqDynamicRangePasses(pass_manager,

// -----

func.func @testUniformQuantize(%arg0: tensor<*xf32>, %scales: tensor<2xf32>, %zps: tensor<i32>) -> tensor<*x!tf_type.qint8> {
  // expected-error @below {{'tf.UniformQuantize' op quantization_axis is -1, scales must have 0 rank.}}
   %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {
    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64

  for (auto [arg, arg_type, arg_loc] :
       llvm::zip_equal(entry.getArguments(), arg_types, arg_locs)) {
    arg.setType(arg_type);
    arg.setLoc(arg_loc);
  }
}

// Creates a UniformQuantize op and sets it as return op.
// The requantize scale and zero point should be determined from the
// `entry_func_op`'s output, containing information on layerStats of the
// entire function.

  TF_DerivedOperandTypeAttr Tin = TF_DerivedOperandTypeAttr<0>;
  TF_DerivedResultTypeAttr Tout = TF_DerivedResultTypeAttr<0>;

  let hasVerifier = 1;
}

def TF_UniformQuantizeOp : TF_Op<"UniformQuantize", [Pure]> {
  let summary = "Perform quantization on Tensor `input`.";

  let description = [{
Given `input`, `scales` and `zero_points`, performs quantization using the formula: