quantized_fraction = static_cast<int32_t>(std::round(fraction * (1L << 15)));
  // Clip extreme values.  These are more than enough to overflow int8, the
  // storage type for quantized values, and the final values will be clamped
  // no matter what.
  if (quantized_fraction == (1L << 15)) {
    quantized_fraction /= 2;
    ++shift;
  }
  if (shift < -15) {
    shift = 0;

// Stores information about how to quantize a user-specified custom operation.
// CustomOpInfo contains info of its corresponding CustomOp registered in the
// CustomOpMap. 'quantizable_input_indices' is used to determine which indices
// of the CustomOp are quantizable. 'is_weight_only' is used specify whether the
// custom op is quantized only for storage and dequantized at runtime.

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;

  // Initializes `QuantizationReport` by collecting `QuantizationResults` from

#define GET_OP_CLASSES
#include "tensorflow/compiler/mlir/quantization/tensorflow/passes/tf_quant_ops.h.inc"

namespace mlir {
namespace quant {

// Function to register TensorFlow Uniform Quantized ops.
void RegisterOps();

}  // namespace quant
}  // namespace mlir

// RUN: tfr-opt %s -tfr-decompose -tfr-rewrite-quantized-io -verify-diagnostics | FileCheck %s

// CHECK-LABEL: @tf__my_requantize
tfr.func @tf__my_requantize(%input: !tfr.tensor) -> !tfr.tensor {
  %raw_data = tfr.quant_raw_data(%input) : (!tfr.tensor) -> !tfr.tensor
  %scale, %zp = tfr.quant_qparam(%input) : (!tfr.tensor) -> (!tfr.tensor, !tfr.tensor)
  %result = tfr.call @tf__requantize(%raw_data, %scale, %zp) : (!tfr.tensor, !tfr.tensor, !tfr.tensor) -> !tfr.tensor

      "metadata": {
        "id": "cqeGmbO6PPNd"
      },
      "source": [
        "This example shows a JAX Keras reference model converted into a StableHLO module and via `jax2tf`, then quantized in the ODML Converter via the StableHLO Quantizer.\n",
        "\n",

constexpr StringRef kCompositeFuncPrefix = "composite_";
constexpr StringRef kEmptyNodeName = "_empty_node";

// Returns a pair: `func_name` and `node_name` for the lifted function. In TF
// quantizer, both are filled. For StableHLO quantizer, the func_name is only
// filled and node_name is always set to "_empty_node".
std::pair<std::string, std::string> GetFuncNameAndNodeName(
    TF::PartitionedCallOp call_op, const FlatSymbolRefAttr &f_attr) {

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Generates the quantized function library contained header file."""

import ast
import re
import string
from typing import Sequence

from absl import app
from absl import flags

// checkpoint saving and restoring. This function returns a `SaverDef` instance
// with four fields populated: `version`, `filename_tensor_name`,
// `restore_op_name` and `save_tensor_name`. For valid quantized `graph_def` and
// `control_ret_node_names`, it should be able to retrieve the last three fields
// if there is at lest one variable in the graph.
//

#include "mlir/IR/Builders.h"  // from @llvm-project

namespace mlir::quant {

// Caclulate padding values for XLA ops.
// Padding values for Uniform Quantized ops can be generated with this method as
// well as it shares the same definition for padding attribute with the XLA ops.
Value CalculatePaddingAndPadIfNeeded(OpBuilder &builder, Location loc,