class_docstr: A distribution strategy for running on a single device.
  
  Using this strategy will place any variables created in its scope on the
  specified device. Input distributed through this strategy will be
  prefetched to the specified device. Moreover, any functions called via
  `strategy.run` will also be placed on the specified device
  as well.
  
  Typical usage of this strategy could be testing your code with the
  tf.distribute.Strategy API before switching to other strategies which
  actually distribute to multiple devices/machines.
  
  For example:
  ```
  strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
  
  with strategy.scope():
    v = tf.Variable(1.0)
    print(v.device)  # /job:localhost/replica:0/task:0/device:GPU:0
  
  def step_fn(x):
    return x * 2
  
  result = 0
  for i in range(10):
    result += strategy.run(step_fn, args=(i,))
  print(result)  # 90
  ```
class_docstr_tok: ["A","distribution","strategy","for","running","on","a","single","device",".","Using","this","strategy","will","place","any","variables","created","in","its","scope","on","the","specified","device",".","Input","distributed","through","this","strategy","will","be","prefetched","to","the","specified","device",".","Moreover",",","any","functions","called","via","`","strategy.run","`","will","also","be","placed","on","the","specified","device","as","well",".","Typical","usage","of","this","strategy","could","be","testing","your","code","with","the","tf.distribute.Strategy","API","before","switching","to","other","strategies","which","actually","distribute","to","multiple","devices\/machines",".","For","example",":","``","`","strategy","=","tf.distribute.OneDeviceStrategy","(","device=","''","\/gpu:0","''",")","with","strategy.scope","(",")",":","v","=","tf.Variable","(","1.0",")","print","(","v.device",")","#","\/job",":","localhost\/replica:0\/task:0\/device",":","GPU:0","def","step_fn","(","x",")",":","return","x","*","2","result","=","0","for","i","in","range","(","10",")",":","result","+=","strategy.run","(","step_fn",",","args=","(","i",",",")",")","print","(","result",")","#","90","``","`"]
docstr: Returns the list of all local per-replica values contained in `value`.
  
  In `OneDeviceStrategy`, the `value` is always expected to be a single
  value, so the result is just the value in a tuple.
  
  Args:
    value: A value returned by `experimental_run()`, `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","`","OneDeviceStrategy","`",",","the","`","value","`","is","always","expected","to","be","a","single","value",",","so","the","result","is","just","the","value","in","a","tuple",".","Args",":","value",":","A","value","returned","by","`","experimental_run","(",")","`",",","`","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 `OneDeviceStrategy`, the `value` is always expected to be a single
      value, so the result is just the value in a tuple.
  
      Args:
        value: A value returned by `experimental_run()`, `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(OneDeviceStrategy, self).experimental_local_results(
          value
      )
  
imports: from tensorflow.python.distribute import device_util
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.distribute import distribute_utils
  from tensorflow.python.distribute import input_lib
  from tensorflow.python.distribute import input_util
  from tensorflow.python.distribute import numpy_dataset
  from tensorflow.python.distribute.v1 import input_lib
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import while_loop
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 4 for calling the OneDeviceStrategy obj's underlying member method code with example usage: obj.experimental_local_results(value) and returns: super