def _save_function_alias(
    saved_model_dir: str,
    tags: Collection[str],
    function_aliases: Mapping[str, str],
) -> None:
  """Saves the function alias to the SavedModel.

  SavedModelBuilder (TF1 saved model saver) does not support saving function
  aliases, so this function loads the SavedModel proto and adds the
  `function_aliases` field.

  Args:
    saved_model_dir: Path to the saved model directory.

    representative_dataset: rd.RepresentativeDataset,
) -> None:
  """Runs the representative dataset through a function for calibration.

  NOTE: This is intended to be run in graph mode (TF1).

  The function is identified by the SignatureDef.

  Args:
    sess: The Session object to run the function in.
    signature_def: A SignatureDef that identifies a function by specifying the

  absl::call_once(flags_init, &AllocateAndParseFlags);
  return *jitrt_flags;
}

ConfigProto::Experimental::MlirBridgeRollout GetMlirBridgeRolloutState(
    std::optional<const ConfigProto> config_proto) {
  // TF1 graphs that do not override Sessions's ConfigProto and TF2 graphs
  // can enable/disable the graph via tf_mlir_enable_mlir_bridge.
  auto tf_mlir_enable_mlir_bridge =
      GetMlirCommonFlags()->tf_mlir_enable_mlir_bridge;

from tensorflow.python.types import core

_CalibrationMethod = qc.CalibrationOptions.CalibrationMethod


# Test cases for Static Range Quantization.
# Tries to run all tests cases in both the graph mode (default in TF1) and the
# eager mode (default in TF2) to ensure support for when TF2 is disabled.
class StaticRangeQuantizationTest(quantize_model_test_base.QuantizedModelTest):

  @parameterized.parameters(

@test_util.run_all_in_graph_and_eager_modes
class QuantizationOptionsTest(quantize_model_test_base.QuantizedModelTest):
  """Test cases regarding the use of QuantizationOptions proto.

  Run all tests cases in both the graph mode (default in TF1) and the eager mode
  (default in TF2) to ensure support for when TF2 is disabled.
  """

  class SimpleModel(module.Module):

    def __init__(self):

        checkpoint, please change your optimizer to
        `tf.keras.optimizers.legacy.XXX` (e.g.
        `tf.keras.optimizers.legacy.Adam`).
    *   **TF1 compatibility.** The new optimizer does not support TF1 any more,
        so please use the legacy optimizer `tf.keras.optimizer.legacy.XXX`.
        We highly recommend to migrate your workflow to TF2 for stable
        support and new features.