func.func @modified(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x401408xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "input", outputs = "output"}} {
  %cst = arith.constant dense<[1, 401408]> : tensor<2xi32>
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x224x224x3x!quant.uniform<i8:f32, 7.812500e-03>>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<i8:f32, 7.812500e-03>>

  let summary = "Restores function name from XlaCallModule op.";
}

def QuantizeCompositeFunctionsPass : Pass<"stablehlo-quantize-composite-functions", "ModuleOp"> {
  let summary = "Quantize composite functions with QDQ input / outputs.";
  let options = [
    Option<"enable_per_channel_quantized_weight_",
        "enable-per-channel-quantized-weight",

#include "tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h"

//===----------------------------------------------------------------------===//
// The post-quantize Passes.
//
namespace mlir {
namespace TFL {
namespace {
#define GEN_PASS_DEF_POSTQUANTIZEPASS
#define GEN_PASS_DEF_POSTQUANTIZEREMOVEQDQPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"

    assert(scales_.size() == zero_points_.size());
  }

  // Quantize an Attribute by the quantization parameters. Return nullptr if
  // the conversion fails or the input array isn't an ElementsAttr.
  ElementsAttr convert(Attribute real_value);

 private:
  // Quantize an DenseFPElementsAttr by the quantization parameters.
  DenseElementsAttr convert(DenseFPElementsAttr attr);

      // The input of the quantize op has already been quantized, i.e.
      // rescale.
      return failure();
    }

    Operation* operand_op = operand.getDefiningOp();
    if (operand_op == nullptr) {
      // When `QuantizeOpT`'s operand does not have a defining op, it means it
      // is a `BlockArgument`. The pattern does not match if there is no op to
      // quantize.
      return failure();
    }

  EXPECT_FALSE(IsOpFullyQuantized(*add_op_itr));
}

TEST_F(IsOpFullyQuantizedTest, FalseIfOpPartiallyQuantized) {
  constexpr absl::string_view kQuantizeOp = R"mlir(
    func.func @quantize(%arg0: tensor<2xf32>) -> tensor<2x!quant.uniform<i8:f32, 1.000000e+00:0>> {
      %0 = stablehlo.uniform_quantize %arg0 : (tensor<2xf32>) -> tensor<2x!quant.uniform<i8:f32, 1.000000e+00:0>>

  // of `quantized_type`.
  if (new_value_type == nullptr) return;

  auto quantize =
      builder_.create<quantfork::QuantizeCastOp>(loc, new_value_type, value);
  auto dequantize = builder_.create<quantfork::DequantizeCastOp>(
      loc, expressed_type, quantize.getResult());

  // This attribute is set to distinguish the quantize ops being added by the
  // quantization pass. These ops can be removed without losing original

  // Whether the func contains Quantize ops. This is used to determine whether
  // to use the quantization parameters from the fixed output range property.
  bool ContainsQuantizeOps(func::FuncOp func);

  QuantizationSpecs quant_specs_;

  Option<bool> enable_post_training_quantize_{
      *this, "post-training-quantize", llvm::cl::init(false),

    // 1. Collect quantizable ops.
    QuantizationUnits quantizable_ops = GetQuantizableOps(op);
    if (quantizable_ops.empty()) {
      return failure();
    }

    // 2. Quantize collected ops.
    if (!QuantizeOps(rewriter, op, quantizable_ops)) {
      return failure();
    }

    // 3. Complete the Q-DQ pair for each inference type.
    if (!ConvertToFloat16Constant(rewriter, op)) {

#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/monitoring/counter.h"

//===----------------------------------------------------------------------===//
// The prepare-quantize Pass.
//
namespace mlir {
namespace TFL {

namespace {
#define GEN_PASS_DEF_PREPAREQUANTIZEPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"