class_docstr: An asynchronously available value of a scheduled function.
  
  This class is used as the return value of
  `tf.distribute.experimental.coordinator.ClusterCoordinator.schedule` where
  the underlying value becomes available at a later time once the function has
  been executed.
  
  Using `tf.distribute.experimental.coordinator.RemoteValue` as an input to
  a subsequent function scheduled with
  `tf.distribute.experimental.coordinator.ClusterCoordinator.schedule` is
  currently not supported.
  
  Example:
  
  ```python
  strategy = tf.distribute.experimental.ParameterServerStrategy(
      cluster_resolver=...)
  coordinator = (
      tf.distribute.experimental.coordinator.ClusterCoordinator(strategy))
  
  with strategy.scope():
    v1 = tf.Variable(initial_value=0.0)
    v2 = tf.Variable(initial_value=1.0)
  
  @tf.function
  def worker_fn():
    v1.assign_add(0.1)
    v2.assign_sub(0.2)
    return v1.read_value() / v2.read_value()
  
  result = coordinator.schedule(worker_fn)
  # Note that `fetch()` gives the actual result instead of a `tf.Tensor`.
  assert result.fetch() == 0.125
  
  for _ in range(10):
    # `worker_fn` will be run on arbitrary workers that are available. The
    # `result` value will be available later.
    result = coordinator.schedule(worker_fn)
  ```
docstr: Wait for the result of `RemoteValue` and return the tensor result.
  
  This makes the value concrete by copying the remote tensor to local.
  
  Returns:
    The actual output (in the form of `tf.Tensor`s) of the `tf.function`
    associated with this `RemoteValue`, previously returned by a
    `tf.distribute.experimental.coordinator.ClusterCoordinator.schedule` call.
    This can be a single Tensor, or a structure of Tensors, depending on the
    output of the `tf.function`.
  
  Raises:
    tf.errors.CancelledError: If the function that produces this `RemoteValue`
      is aborted or cancelled due to failure.
docstr_tok: ["Wait","for","the","result","of","`","RemoteValue","`","and","return","the","tensor","result",".","This","makes","the","value","concrete","by","copying","the","remote","tensor","to","local",".","Returns",":","The","actual","output","(","in","the","form","of","`","tf.Tensor","`","s",")","of","the","`","tf.function","`","associated","with","this","`","RemoteValue","`",",","previously","returned","by","a","`","tf.distribute.experimental.coordinator.ClusterCoordinator.schedule","`","call",".","This","can","be","a","single","Tensor",",","or","a","structure","of","Tensors",",","depending","on","the","output","of","the","`","tf.function","`",".","Raises",":","tf.errors.CancelledError",":","If","the","function","that","produces","this","`","RemoteValue","`","is","aborted","or","cancelled","due","to","failure","."]
code: def get(self):
      """Wait for the result of `RemoteValue` and return the tensor result.
  
      This makes the value concrete by copying the remote tensor to local.
  
      Returns:
        The actual output (in the form of `tf.Tensor`s) of the `tf.function`
        associated with this `RemoteValue`, previously returned by a
        `tf.distribute.experimental.coordinator.ClusterCoordinator.schedule` call.
        This can be a single Tensor, or a structure of Tensors, depending on the
        output of the `tf.function`.
  
      Raises:
        tf.errors.CancelledError: If the function that produces this `RemoteValue`
          is aborted or cancelled due to failure.
      """
      raise NotImplementedError("Must be implemented in subclasses.")
  
file_path: tensorflow-upstream/tensorflow/python/distribute/coordinator/remote_value.py
imports: import enum
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 2 for calling the RemoteValue obj's underlying member method code with example usage: obj.get() without return types