signature_def_map_serialized: dict[str, bytes],
    py_function_library: py_function_lib.PyFunctionLibrary,
) -> Any: ...  # Status

# LINT.ThenChange()

# LINT.IfChange(quantize_ptq_dynamic_range)
def quantize_ptq_dynamic_range(
    src_saved_model_path: str,
    dst_saved_model_path: str,
    quantization_options_serialized: bytes,
    *,
    signature_keys: list[str],

      py::arg("py_function_library"));

  m.def(
      // If the function signature changes, likely its corresponding .pyi type
      // hinting should also change.
      // LINT.IfChange
      "quantize_ptq_dynamic_range",
      [](const absl::string_view src_saved_model_path,
         const absl::string_view dst_saved_model_path,
         const QuantizationOptions& quantization_options,

      src_saved_model_path,
      quantization_options.signature_keys,
      quantization_options.tags,
  )

  # Apply post-training dynamic range quantization to the model.
  pywrap_quantize_model.quantize_ptq_dynamic_range(
      src_saved_model_path,
      dst_saved_model_path,
      quantization_options_serialized=quantization_options.SerializeToString(),
      signature_keys=list(quantization_options.signature_keys),