code: def _restore_checkpoint_and_maybe_run_saved_model_initializers(
      sess: session.Session, saver: saver_lib.Saver, path: str
  ):
      """Restores checkpoint values and SavedModel initializers if found."""
      # NOTE: All references to SavedModel refer to SavedModels loaded from the
      # load_v2 API (which does not require the `sess` argument).
  
      # If the graph contains resources loaded from a SavedModel, they are not
      # restored when calling `saver.restore`. Thus, the SavedModel initializer must
      # be called with `saver.restore` to properly initialize the model.
  
      # The SavedModel init is stored in the "saved_model_initializers" collection.
      # This collection is part of the MetaGraph's default_init_op, so it is already
      # called by MonitoredSession as long as the saver doesn't restore any
      # checkpoints from the working dir.
      saved_model_init_ops = ops.get_collection(
          "saved_model_initializers"
      )
      if saved_model_init_ops:
          sess.run(saved_model_init_ops)
  
      # The saver must be called *after* the SavedModel init, because the SavedModel
      # init will restore the variables from the SavedModel variables directory.
      # Initializing/restoring twice is not ideal but there's no other way to do it.
      saver.restore(sess, path)
  
imports: import time
  from typing import Optional, Tuple
  import numpy
  from tensorflow.python.checkpoint import checkpoint_management
  from tensorflow.python.client import session
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.training import saver
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 2 for calling a global function with example usage: _restore_checkpoint_and_maybe_run_saved_model_initializers(sess, saver, path) without return types