class_docstr: Loads a SavedModel without using Sessions.
class_docstr_tok: ["Loads","a","SavedModel","without","using","Sessions","."]
code: def _extract_saver_restore(self, wrapped, saver):
      if saver is None:
          return None
      saver_def = saver.saver_def
      filename_tensor = wrapped.graph.as_graph_element(
          saver_def.filename_tensor_name
      )
      # We both feed and fetch filename_tensor so we have an operation to use to
      # feed into variable initializers (only relevant for v1 graph building).
      return wrapped.prune(
          feeds=[filename_tensor],
          fetches=[
              filename_tensor,
              wrapped.graph.as_graph_element(saver_def.restore_op_name),
          ],
      )
  
imports: import functools
  from tensorflow.python.eager import context
  from tensorflow.python.eager import lift_to_graph
  from tensorflow.python.eager import wrap_function
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import func_graph
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.saved_model import function_deserialization
  from tensorflow.python.saved_model import loader_impl
  from tensorflow.python.saved_model import signature_serialization
  from tensorflow.python.saved_model.pywrap_saved_model import metrics
  from tensorflow.python.trackable import asset
  from tensorflow.python.trackable import autotrackable
  from tensorflow.python.trackable import resource
  from tensorflow.python.training import monitored_session
  from tensorflow.python.training import saver
  from tensorflow.python.util import nest
label_desc: Use image node_id 3 for calling the _EagerSavedModelLoader obj's underlying member method code with example usage: obj._extract_saver_restore(wrapped, saver) and returns: wrapped, None