class_docstr: Periodically fetches new version of params and stores tf.saved_models.
class_docstr_tok: ["Periodically","fetches","new","version","of","params","and","stores","tf.saved_models","."]
code: def __init__(
      self,
      variable_source: core.VariableSource,
      models: Dict[
          str, Callable[[core.VariableSource], types.ModelToSnapshot]
      ],
      path: str,
      subdirectory: Optional[str] = None,
      max_to_keep: Optional[int] = None,
      add_uid: bool = False,
  ):
      self._variable_source = variable_source
      self._models = models
      if subdirectory is not None:
          self._path = paths.process_path(
              path, subdirectory, add_uid=add_uid
          )
      else:
          self._path = paths.process_path(path, add_uid=add_uid)
      self._max_to_keep = max_to_keep
      self._snapshot_paths: Optional[List[str]] = None
  
file_path: acme/acme/jax/snapshotter.py
imports: import os
  import time
  from typing import Callable, Dict, List, Optional, Sequence, Tuple
  from absl import logging
  from acme import core
  from acme.jax import types
  from acme.utils import signals
  from acme.utils import paths
  from jax.experimental import jax2tf
  import tensorflow
label_desc: Use image node_id 1 to create a new JAXSnapshotter object from inherited base classes: core with example: obj = JAXSnapshotter(variable_source, models, path, subdirectory, max_to_keep, add_uid)