code: def DistributedOptimizer(
      optimizer, name=None, use_locking=False, device=""
  ):
      """Construct a new DistributedOptimizer, which uses another optimizer
      under the hood for computing single-process gradient values and
      applying gradient updates after the gradient values have been averaged
      across all the Bluefog ranks.
  
      Args:
        optimizer:
          Optimizer to use for computing gradients and applying updates.
        name:
          Optional name prefix for the operations created when applying
          gradients. Defaults to "Distributed" followed by the provided
          optimizer type.
        use_locking:
          Whether to use locking when updating variables.
          See Optimizer.__init__ for more info.
        device:
          Device to be used for dense tensors. Uses GPU by default if CUDA is
          available
      """
      if isinstance(optimizer, _LegacyOptimizer):
          return _DistributedOptimizer(
              optimizer, name, use_locking, device
          )
      elif isinstance(optimizer, tf.keras.optimizers.Optimizer):
          raise NotImplementedError
      else:
          raise ValueError(
              "Provided optimizer doesn't inherit from either legacy "
              "TensorFlow or Keras optimizer: %s" % optimizer
          )
  
imports: import tensorflow
  from bluefog.tensorflow.mpi_ops import allreduce, broadcast, size
  from bluefog.tensorflow.util import _executing_eagerly, _cache
label_desc: Use image node_id 4 for calling a global function with example usage: DistributedOptimizer(optimizer, name, use_locking, device) and returns: _DistributedOptimizer