// Marks users that are applicable for quantization where the criteria for
  // determining quantizable ops differs by the inference type.
  QuantizationUnits GetQuantizableOps(ConstantOp op) const {
    // Non-float tensors do not need quantization.
    QuantizationUnits quantizable_ops;
    const ShapedType type = mlir::dyn_cast<ShapedType>(op.getType());
    if (!type || !type.getElementType().isF32()) return quantizable_ops;

// The prepare-quantize-drq Pass.
//
namespace mlir {
namespace quant {

namespace {

using QuantizationUnit = std::pair<Operation*, int>;
using QuantizationUnits = llvm::SetVector<QuantizationUnit>;
using ::tensorflow::quantization::OpSet;

// Applies prepare quantization on the model in TF dialect for dynamic range
// quantization case.
class PrepareQuantizeDRQPass

// A boolean attribute used to describe whether input activations need to be
// asymmetrically quantized.
constexpr char kAsymmetricQuantizeInputsAttr[] = "asymmetric_quantize_inputs";

using QuantizationUnits = llvm::SetVector<std::pair<Operation*, int>>;

// Applies prepare dynamic range quantization on the model in TFL dialect.
// This pass runs before the quantization pass and apply preprocess if
// applicable.

namespace mlir::quant::stablehlo {

// A class that manages information about `QuantizableUnit`s post-quantization,
// internally in the form of `QuantizationUnits`. It is used to collect
// quantization summary from a quantized `ModuleOp` and emit it in a human- and
// machine-readable format.
class QuantizationReport {
 public:
  QuantizationReport() = default;

//
namespace mlir {
namespace quant {

namespace {

using QuantMethod =
    ::tensorflow::quantization::QuantizationMethod::PresetMethod;
using QuantizationUnit = std::pair<Operation*, int>;
using QuantizationUnits = llvm::SetVector<QuantizationUnit>;
using ::tensorflow::quantization::OpSet;

// Preprocesses ops to allow multi-axis quantization, prior to quantization