code: def DistributedOptimizer(
      optimizer,
      name=None,
      device_dense="",
      device_sparse="",
      compression=Compression.none,
      sparse_as_dense=False,
      gradient_predivide_factor=1.0,
      op=Average,
      num_groups=0,
      groups=None,
  ):
      """
      An optimizer that wraps another keras.optimizers.Optimizer, using an allreduce to
      average gradient values before applying gradients to model weights.
  
      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.
          device_dense: Device to be used for dense tensors. Uses GPU by default
                        if Horovod was build with HOROVOD_GPU_OPERATIONS.
          device_sparse: Device to be used for sparse tensors. Uses GPU by default
                         if Horovod was build with HOROVOD_GPU_OPERATIONS.
          compression: Compression algorithm used to reduce the amount of data
                       sent and received by each worker node.  Defaults to not
                       using compression.
          sparse_as_dense: Treat all sparse gradients as dense tensors.  This can
                           help improve performance and memory utilization if
                           the original sparse gradient has high density.
                           Defaults to false.
          gradient_predivide_factor: gradient_predivide_factor splits the averaging
                                     before and after the sum. Gradients are scaled by
                                     1.0 / gradient_predivide_factor before the sum and
                                     gradient_predivide_factor / size after the sum.
          op: The reduction operation to use when combining gradients across
              different ranks. Defaults to Average.
          num_groups: Number of groups to assign gradient allreduce ops to for explicit
                      grouping. Defaults to no explicit groups.
          groups: The parameter to group the gradient allreduce ops. Accept values is a
                  non-negative integer or a list of list of tf.Variable.
                  If groups is a non-negative integer, it is the number of groups to assign
                  gradient allreduce ops to for explicit grouping.
                  If groups is a list of list of tf.Variable. Variables in the same
                  inner list will be assigned to the same group, while parameter that does
                  not appear in any list will form a group itself.
                  Defaults as None, which is no explicit groups.
      """
      if gradient_predivide_factor != 1.0 and rocm_built():
          raise ValueError(
              "gradient_predivide_factor not supported yet with ROCm"
          )
  
      if op != Average and op != Sum:
          raise ValueError("op currently only supports Average and Sum")
  
      if num_groups != 0:
          warnings.warn(
              "Parameter `num_groups` has been replaced by `groups` "
              "and will be removed in v0.23.0.",
              DeprecationWarning,
          )
          if groups is None:
              groups = num_groups
  
      if groups is not None:
          if not (isinstance(groups, list) or groups > 0):
              raise ValueError(
                  "groups should be a non-negative integer or "
                  "a list of list of tf.Variable."
              )
  
      return _impl.create_distributed_optimizer(
          keras=keras,
          optimizer=optimizer,
          name=name,
          device_dense=device_dense,
          device_sparse=device_sparse,
          compression=compression,
          sparse_as_dense=sparse_as_dense,
          gradient_predivide_factor=gradient_predivide_factor,
          op=op,
          groups=groups,
      )
  
imports: import warnings
  from packaging import version
  import keras
  import keras.backend
  import tensorflow
  from horovod.tensorflow import init
  from horovod.tensorflow import shutdown
  from horovod.tensorflow import size, local_size, cross_size, rank, local_rank, cross_rank
  from horovod.tensorflow import is_initialized, start_timeline, stop_timeline
  from horovod.tensorflow import mpi_threads_supported, mpi_enabled, mpi_built
  from horovod.tensorflow import gloo_enabled, gloo_built
  from horovod.tensorflow import nccl_built, ddl_built, ccl_built, cuda_built, rocm_built
  from horovod.tensorflow import Average, Sum, Adasum
  from horovod.tensorflow.compression import Compression
  from horovod.tensorflow.mpi_ops import global_process_set
  from horovod.keras import callbacks, elastic
  import horovod._keras
label_desc: Use image node_id 1 for calling a global function with example usage: DistributedOptimizer(optimizer, name, device_dense, device_sparse, compression, sparse_as_dense, gradient_predivide_factor, op, num_groups, groups) and returns: _impl