class_docstr: A one-machine strategy that puts all variables on a single device.
  
  Variables are assigned to local CPU or the only GPU. If there is more
  than one GPU, compute operations (other than variable update operations)
  will be replicated across all GPUs.
  
  For Example:
  ```
  strategy = tf.distribute.experimental.CentralStorageStrategy()
  # Create a dataset
  ds = tf.data.Dataset.range(5).batch(2)
  # Distribute that dataset
  dist_dataset = strategy.experimental_distribute_dataset(ds)
  
  with strategy.scope():
    @tf.function
    def train_step(val):
      return val + 1
  
    # Iterate over the distributed dataset
    for x in dist_dataset:
      # process dataset elements
      strategy.run(train_step, args=(x,))
  ```
class_docstr_tok: ["A","one-machine","strategy","that","puts","all","variables","on","a","single","device",".","Variables","are","assigned","to","local","CPU","or","the","only","GPU",".","If","there","is","more","than","one","GPU",",","compute","operations","(","other","than","variable","update","operations",")","will","be","replicated","across","all","GPUs",".","For","Example",":","``","`","strategy","=","tf.distribute.experimental.CentralStorageStrategy","(",")","#","Create","a","dataset","ds","=","tf.data.Dataset.range","(","5",")",".batch","(","2",")","#","Distribute","that","dataset","dist_dataset","=","strategy.experimental_distribute_dataset","(","ds",")","with","strategy.scope","(",")",":","@","tf.function","def","train_step","(","val",")",":","return","val","+","1","#","Iterate","over","the","distributed","dataset","for","x","in","dist_dataset",":","#","process","dataset","elements","strategy.run","(","train_step",",","args=","(","x",",",")",")","``","`"]
docstr: Returns the list of all local per-replica values contained in `value`.
  
  In `CentralStorageStrategy` there is a single worker so the value returned
  will be all the values on that worker.
  
  Args:
    value: A value returned by `run()`, `extended.call_for_each_replica()`,
    or a variable created in `scope`.
  
  Returns:
    A tuple of values contained in `value`. If `value` represents a single
    value, this returns `(value,).`
docstr_tok: ["Returns","the","list","of","all","local","per-replica","values","contained","in","`","value","`",".","In","`","CentralStorageStrategy","`","there","is","a","single","worker","so","the","value","returned","will","be","all","the","values","on","that","worker",".","Args",":","value",":","A","value","returned","by","`","run","(",")","`",",","`","extended.call_for_each_replica","(",")","`",",","or","a","variable","created","in","`","scope","`",".","Returns",":","A","tuple","of","values","contained","in","`","value","`",".","If","`","value","`","represents","a","single","value",",","this","returns","`","(","value",",",")",".","`"]
code: def experimental_local_results(
      self, value
  ):  # pylint: disable=useless-super-delegation
      """Returns the list of all local per-replica values contained in `value`.
  
      In `CentralStorageStrategy` there is a single worker so the value returned
      will be all the values on that worker.
  
      Args:
        value: A value returned by `run()`, `extended.call_for_each_replica()`,
        or a variable created in `scope`.
  
      Returns:
        A tuple of values contained in `value`. If `value` represents a single
        value, this returns `(value,).`
      """
      return super(
          CentralStorageStrategy, self
      ).experimental_local_results(value)
  
file_path: tensorflow-upstream/tensorflow/python/distribute/central_storage_strategy.py
imports: from tensorflow.python.distribute import device_util
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.distribute import parameter_server_strategy
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 4 for calling the CentralStorageStrategy obj's underlying member method code with example usage: obj.experimental_local_results(value) and returns: super