union QuantizationDetails {
  CustomQuantization,
}

// Parameters for converting a quantized tensor back to float.
table QuantizationParameters {
  // These four parameters are the asymmetric linear quantization parameters.
  // Given a quantized value q, the corresponding float value f should be:
  //   f = scale * (q - zero_point)

// TensorFlow.
std::unique_ptr<OperationPass<ModuleOp>>
CreatePrepareTpuComputationForTfExportPass();

// Rewrites ops that require quantized inputs or outputs to ops that allow
// non-quantized inputs and outputs.
std::unique_ptr<OperationPass<func::FuncOp>> CreateLowerQuantizedPass();

// Reorders ops so ops of the same dialect are next to each other.

//===----------------------------------------------------------------------===//

def DequantizeHalfRange : NativeCodeCall<
  "DequantizeHalfRange(&$_builder, $0)">;

// TODO(b/188530181): Generalize to more quantized input types,
//   allow num_slices > 1, and allow non default arguments for $mode,
//   $narrow_range, and $axis.
def LowerDequantizeOp : Pat<
  (TF_DequantizeOp:$result
    $input, $min_range, $max_range,

// RUN: tf-opt %s -tfl-prepare-quantize -tfl-quantize  | FileCheck %s
// RUN: tf-opt %s -tfl-quantize="legacy-quantize=true" | FileCheck --check-prefix=LEGACY %s
// RUN: tf-opt %s -tfl-prepare-quantize -tfl-quantize="ops-blocklist=tfl.fully_connected,tfl.softmax locs-blocklist=Block,NullBlock" | FileCheck --check-prefix=BLOCK %s

// CHECK-LABEL: QuantizeFloatConst
func.func @QuantizeFloatConst() -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>> {

// attribute which maps an attribute identifier to its attribute name. The
// identifier is the order of that attribute in `attributes`. This map
// is then used to set attributes in the quantized functions in the
// QuantizeCompositeFunctionsPass.
// For example, for tf.MatMul with `attributes` = {{"transpose_a", false},
// {"transpose_b", false}}, the generated attr_map is

// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range -tfl-quantize="enable-dynamic-range-quantization=true" | FileCheck %s
// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range -tfl-quantize="enable-dynamic-range-quantization=true enable-weight-only-quantization=true" | FileCheck --check-prefix=PerChannelWeightOnly %s

type Options struct {
	// NumColors is the maximum number of colors used in the image.
	// It ranges from 1 to 256.
	NumColors int

	// Quantizer is used to produce a palette with size NumColors.
	// palette.Plan9 is used in place of a nil Quantizer.
	Quantizer draw.Quantizer

	// Drawer is used to convert the source image to the desired palette.
	// draw.FloydSteinberg is used in place of a nil Drawer.
	Drawer draw.Drawer

    if (IsCallToQuantizableLiftedFunction(op)) {
      std::optional<StringRef> composite_function_name =
          GetCompsiteFunctionName(op);
      if (!composite_function_name.has_value()) return failure();

      // Quantize inputs of quantizable composite functions.
      for (OpOperand &input : op->getOpOperands()) {
        Type element_type = getElementTypeOrSelf(input.get().getType());
        // Non-float cases won't be calibrated.

        "passes/prepare_quantize.inc",
        "passes/prepare_quantize_drq.cc",
        "passes/preprocess_op.cc",
        "passes/preprocess_op.inc",
        "passes/propagate_quantize_type.cc",
        "passes/quantize.cc",
        "passes/quantize_composite_functions.cc",
        "passes/quantize_composite_functions.inc",
        "passes/quantize_weights.cc",
        "passes/quantized_function_library.h",

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-quantize -verify-each=false | FileCheck %s

module attributes {tf_saved_model.semantics} {
  // CHECK-LABEL: same_scale_after_composite
  // CHECK-SAME: %[[ARG0:.*]]: tensor<1x2xf32>
  // CHECK-SAME: %[[ARG1:.*]]: tensor<2x3xf32>
  func.func private @same_scale_after_composite(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<3x1xf32> {