code: def snapshot(
      savefun=None,
      filename="snapshot_iter_{.updater.iteration}",
      **kwargs,
  ):
      """snapshot(savefun=None, filename='snapshot_iter_{.updater.iteration}', \
  *, target=None, condition=None, writer=None, snapshot_on_error=False, \
  n_retains=-1, autoload=False)
  
      Returns a trainer extension to take snapshots of the trainer.
  
      This extension serializes the trainer object and saves it to the output
      directory. It is used to support resuming the training loop from the saved
      state.
  
      This extension is called once per epoch by default. To take a
      snapshot at a different interval, a trigger object specifying the
      required interval can be passed along with this extension
      to the `extend()` method of the trainer.
  
      The default priority is -100, which is lower than that of most
      built-in extensions.
  
      .. note::
         This extension first writes the serialized object to a temporary file
         and then rename it to the target file name. Thus, if the program stops
         right before the renaming, the temporary file might be left in the
         output directory.
  
      Args:
          savefun: Function to save the trainer. It takes two arguments: the
              output file path and the trainer object.
              It is :meth:`chainer.serializers.save_npz` by default.
              If ``writer`` is specified, this argument must be ``None``.
          filename (str): Name of the file into which the trainer is serialized.
              It can be a format string, where the trainer object is passed to
              the :meth:`str.format` method.
          target: Object to serialize. If it is not specified, it will
              be the trainer object.
          condition: Condition object. It must be a callable object that returns
              boolean without any arguments. If it returns ``True``, the snapshot
              will be done.
              If not, it will be skipped. The default is a function that always
              returns ``True``.
          writer: Writer object.
              It must be a callable object.
              See below for the list of built-in writers.
              If ``savefun`` is other than ``None``, this argument must be
              ``None``. In that case, a
              :class:`~chainer.training.extensions.snapshot_writers.SimpleWriter`
              object instantiated with specified ``savefun`` argument will be
              used.
          snapshot_on_error (bool): Whether to take a snapshot in case trainer
              loop has been failed.
          n_retains (int): Number of snapshot files to retain
              through the cleanup. Must be a positive integer for any cleanup to
              take place. Automatic deletion of old snapshots only works when the
              filename is string.
          num_retain (int): Same as ``n_retains`` (deprecated).
          autoload (bool): With this enabled, the extension
              automatically finds the latest snapshot and loads the data
              to the target.  Automatic loading only works when the
              filename is a string. It is assumed that snapshots are generated
              by :func:`chainer.serializers.save_npz` .
  
      Returns:
          Snapshot extension object.
  
      .. testcode::
         :hide:
  
         from chainer import training
         class Model(chainer.Link):
             def __call__(self, x):
                 return x
         train_iter = chainer.iterators.SerialIterator([], 1)
         optimizer = optimizers.SGD().setup(Model())
         updater = training.updaters.StandardUpdater(
             train_iter, optimizer, device=0)
         trainer = training.Trainer(updater)
  
      .. admonition:: Using asynchronous writers
  
          By specifying ``writer`` argument, writing operations can be made
          asynchronous, hiding I/O overhead of snapshots.
  
          >>> from chainer.training import extensions
          >>> writer = extensions.snapshot_writers.ProcessWriter()
          >>> trainer.extend(extensions.snapshot(writer=writer), \
  trigger=(1, 'epoch'))
  
          To change the format, such as npz or hdf5, you can pass a saving
          function as ``savefun`` argument of the writer.
  
          >>> from chainer.training import extensions
          >>> from chainer import serializers
          >>> writer = extensions.snapshot_writers.ProcessWriter(
          ...     savefun=serializers.save_npz)
          >>> trainer.extend(extensions.snapshot(writer=writer), \
  trigger=(1, 'epoch'))
  
      This is the list of built-in snapshot writers.
  
          - :class:`chainer.training.extensions.snapshot_writers.SimpleWriter`
          - :class:`chainer.training.extensions.snapshot_writers.ThreadWriter`
          - :class:`chainer.training.extensions.snapshot_writers.ProcessWriter`
          - :class:`chainer.training.extensions.snapshot_writers.\
  ThreadQueueWriter`
          - :class:`chainer.training.extensions.snapshot_writers.\
  ProcessQueueWriter`
  
      .. seealso::
  
          - :meth:`chainer.training.extensions.snapshot_object`
      """
      if "num_retain" in kwargs:
          warnings.warn(
              "Argument `num_retain` is deprecated. "
              "Please use `n_retains` instead",
              DeprecationWarning,
          )
          kwargs["n_retains"] = kwargs.pop("num_retain")
  
      (
          target,
          condition,
          writer,
          snapshot_on_error,
          n_retains,
          autoload,
      ) = argument.parse_kwargs(
          kwargs,
          ("target", None),
          ("condition", None),
          ("writer", None),
          ("snapshot_on_error", False),
          ("n_retains", -1),
          ("autoload", False),
      )
      argument.assert_kwargs_empty(kwargs)
  
      if savefun is not None and writer is not None:
          raise TypeError(
              "savefun and writer arguments cannot be specified together."
          )
  
      if writer is None:
          if savefun is None:
              savefun = npz.save_npz
          writer = snapshot_writers.SimpleWriter(savefun=savefun)
  
      return _Snapshot(
          target=target,
          condition=condition,
          writer=writer,
          filename=filename,
          snapshot_on_error=snapshot_on_error,
          n_retains=n_retains,
          autoload=autoload,
      )
  
imports: import os
  import warnings
  import chainer
  from chainer.serializers import npz
  from chainer.training import extension
  from chainer.training.extensions import snapshot_writers
  from chainer.utils import argument
label_desc: Use image node_id 5 for calling a global function with example usage: snapshot(savefun, filename) and returns: _Snapshot