code: def freeze_model(
      checkpoint_path: str,
      meta_graph_def: meta_graph_pb2.MetaGraphDef,
      output_prefix: str,
      signature_def_key: str,
      variables_to_feed: List[str],
  ) -> Tuple[str, str]:
      """Freeze a `MetaGraphDef` in preparation for tfcompile`.
  
      The graph is always optimized with grappler, and optionally (by default)
      variables are frozen as constants, before compilation happens.
  
      Args:
        checkpoint_path: Python string.  Path to checkpoints/variables.
        meta_graph_def: Instance of `MetaGraphDef`.
        output_prefix: Python string.  Path prefix for outputs.
        signature_def_key: String, the signature_def to use in the SavedModel.
        variables_to_feed: A list of strings, the variables that will be fed by the
          user; these won't be frozen.  If `None`, then we will extract all the
          variables in the graph and mark them as to-feed.  The default behavior is
          an empty tuple: all variables must be frozen.
      Returns:
        a pair containing the path to the frozen model and the path to the config.
      Raises:
        RuntimeError: If tensorflow was not built with XLA.
        ImportError: If tensorflow was built with XLA but there was another
          issue importing the tfcompile python wrapper.
        ValueError: If `meta_graph_def.signature_def[signature_def_key]` is
          missing or has empty outputs.
      """
      if _pywrap_tfcompile_import_error:
          raise _pywrap_tfcompile_import_error  # pylint: disable=raising-bad-type
  
      signature_def_map = meta_graph_def.signature_def
      if signature_def_key not in signature_def_map:
          raise ValueError(
              f"Unable to find signature_def_key '{signature_def_key}' in signature "
              "def map of `meta_graph_def`. Available keys: "
              f"{list(signature_def_map.keys())}"
          )
      signature_def = signature_def_map[signature_def_key]
      if not signature_def.outputs:
          raise ValueError(
              f"Signature key {signature_def_key} must have outputs, but saw none:\n"
              f"{str(signature_def)}"
          )
  
      file_io.recursive_create_dir(output_prefix)
      if logging.get_verbosity() >= logging.INFO:
          original_graph_def_location = os.path.join(
              output_prefix, "original_graph.pb"
          )
          with file_io.FileIO(
              original_graph_def_location, "wb"
          ) as graph_writer:
              graph_writer.write(
                  meta_graph_def.graph_def.SerializeToString()
              )
  
      # This updates graph_def in place.
      _replace_input_placeholders_with_default_values(
          meta_graph_def.graph_def, signature_def
      )
  
      graph_def = _optimize_graph(meta_graph_def, signature_def)
  
      all_variables = _get_variable_nodes_from_graph_def(graph_def)
      if variables_to_feed is None:
          variable_nodes_to_feed = list(all_variables.values())
      else:
          not_in_graph = set(variables_to_feed).difference(
              list(all_variables)
          )
          if not_in_graph:
              raise ValueError(
                  "Asked to feed variables that were not found in graph: "
                  f"{not_in_graph}. Variables contained in the graph: "
                  f"{list(all_variables)}"
              )
          variable_nodes_to_feed = [
              all_variables[name] for name in variables_to_feed
          ]
  
      if logging.get_verbosity() >= logging.INFO:
          prefrozen_graph_def_location = os.path.join(
              output_prefix, "prefrozen_graph.pb"
          )
          with file_io.FileIO(
              prefrozen_graph_def_location, "wb"
          ) as graph_writer:
              graph_writer.write(graph_def.SerializeToString())
  
      # Load the Variables so that we can freeze the graph.
      with session.Session(graph=ops_lib.Graph()) as sess:
          restorer = saver_lib.import_meta_graph(
              meta_graph_def, clear_devices=True
          )
          if restorer is not None:
              restorer.restore(sess, checkpoint_path)
          graph_def.CopyFrom(
              convert_to_constants.convert_variables_to_constants(
                  sess,
                  graph_def,
                  output_node_names=[
                      _parse_tensor_name(n.name)[0]
                      for n in signature_def.outputs.values()
                  ],
                  variable_names_blacklist=[
                      n.name for n, _ in variable_nodes_to_feed
                  ],
              )
          )
  
      signature_def = _prune_removed_feed_nodes(
          signature_def, graph_def
      )
  
      frozen_graph_def_location = os.path.join(
          output_prefix, "frozen_graph.pb"
      )
      config_pbtxt_location = os.path.join(
          output_prefix, "config.pbtxt"
      )
      logging.info(
          "Writing graph def to: {}".format(frozen_graph_def_location)
      )
      with file_io.FileIO(
          frozen_graph_def_location, "wb"
      ) as graph_writer:
          graph_writer.write(graph_def.SerializeToString())
      config = _signature_to_tf2xla_config(
          signature_def, variable_nodes_to_feed=variable_nodes_to_feed
      )
      logging.info(
          "Writing config_pbtxt to: {}".format(config_pbtxt_location)
      )
      with file_io.FileIO(
          config_pbtxt_location, mode="w"
      ) as config_writer:
          config_writer.write(str(config))
      return frozen_graph_def_location, config_pbtxt_location
  
imports: import collections
  import copy
  import os
  import re
  import shlex
  from typing import List, Tuple
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.core.protobuf import meta_graph_pb2
  from tensorflow.python.client import session
  from tensorflow.python.framework import convert_to_constants
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import versions
  from tensorflow.python.grappler import tf_optimizer
  from tensorflow.python.lib.io import file_io
  from tensorflow.python.ops import array_ops
  from tensorflow.python.platform import sysconfig
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.training import saver
label_desc: Use image node_id 7 for calling a global function with example usage: freeze_model(checkpoint_path, meta_graph_def, output_prefix, signature_def_key, variables_to_feed) and returns: frozen_graph_def_location,  config_pbtxt_location