code: def _dynamic_range_quantize(
      src_saved_model_path: str,
      dst_saved_model_path: str,
      quantization_options: _QuantizationOptions,
  ) -> autotrackable.AutoTrackable:
      """Quantizes the given SavedModel via post-training dynamic range quantization.
  
      Args:
        src_saved_model_path: Path to the saved model.
        dst_saved_model_path: The path to save the output SavedModel. The directory
          will be overwritten if not empty.
        quantization_options: QuantizationOptions proto describing quantization
          related config.
  
      Returns:
        A SavedModel object with TF quantization applied.
  
      Raises:
        ValueError: when the model is QAT model.
      """
      mode_str = "dynamic-range quantization"
      if _is_qat_saved_model(src_saved_model_path):
          raise ValueError(
              "The models trained with quantization-aware training (QAT) is not "
              "supported for %s." % mode_str
          )
  
      logging.info(
          "Running post-training %s on model: %s",
          mode_str,
          src_saved_model_path,
      )
      logging.info("QuantizationOptions: \n%s", quantization_options)
  
      loader = saved_model_loader.SavedModelLoader(src_saved_model_path)
  
      function_aliases = loader.get_meta_graph_def_from_tags(
          quantization_options.tags
      ).meta_info_def.function_aliases
  
      signature_def_map = save_model.get_signatures_from_saved_model(
          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),
          signature_def_map_serialized=_serialize_signature_def_map(
              signature_def_map
          ),
          function_aliases=dict(function_aliases),
          py_function_library=py_function_lib.PyFunctionLibrary(),
      )
  
      return saved_model_load.load(dst_saved_model_path)
  
imports: import tempfile
  from typing import Mapping, Optional
  from absl import logging
  from tensorflow.compiler.mlir.quantization.tensorflow import quantization_options_pb2
  from tensorflow.compiler.mlir.quantization.tensorflow.python import py_function_lib
  from tensorflow.compiler.mlir.quantization.tensorflow.python import pywrap_quantize_model
  from tensorflow.compiler.mlir.quantization.tensorflow.python import representative_dataset
  from tensorflow.compiler.mlir.quantization.tensorflow.python import save_model
  from tensorflow.core.protobuf import meta_graph_pb2
  from tensorflow.python.lib.io import file_io
  from tensorflow.python.saved_model import load
  from tensorflow.python.saved_model import loader_impl
  from tensorflow.python.saved_model import signature_constants
  from tensorflow.python.saved_model import tag_constants
  from tensorflow.python.trackable import autotrackable
  from tensorflow.python.util import tf_export
label_desc: Use image node_id 6 for calling a global function with example usage: _dynamic_range_quantize(src_saved_model_path, dst_saved_model_path, quantization_options) and returns: saved_model_load