code: def __init__(
      self,
      params,
      named_parameters,
      compression,
      backward_passes_per_step=1,
      op=Average,
      gradient_predivide_factor=1.0,
      groups=None,
      sparse_as_dense=False,
      process_set=global_process_set,
  ):
      super(self.__class__, self).__init__(params)
      self._compression = compression
  
      if named_parameters is not None:
          named_parameters = list(named_parameters)
      else:
          named_parameters = [
              (f"allreduce.noname.{i}.{j}", v)
              for i, param_group in enumerate(self.param_groups)
              for j, v in enumerate(param_group["params"])
          ]
      # make sure that named_parameters are tuples
      if any([not isinstance(p, tuple) for p in named_parameters]):
          raise ValueError(
              "named_parameters should be a sequence of "
              "tuples (name, parameter), usually produced by "
              "model.named_parameters()."
          )
  
      dups = _DistributedOptimizer.find_duplicates(
          [k for k, _ in named_parameters]
      )
      if len(dups) > 0:
          raise ValueError(
              "Parameter names in named_parameters must be unique. "
              "Found duplicates: %s" % ", ".join(dups)
          )
  
      all_param_ids = {
          id(v)
          for param_group in self.param_groups
          for v in param_group["params"]
      }
      named_param_ids = {id(v) for k, v in named_parameters}
      unnamed_param_ids = all_param_ids - named_param_ids
      if len(unnamed_param_ids):
          raise ValueError(
              "named_parameters was specified, but one or more model "
              "parameters were not named. Python object ids: "
              "%s" % ", ".join(str(id) for id in unnamed_param_ids)
          )
  
      self._parameter_names = {
          v: k for k, v in sorted(named_parameters)
      }
      self.backward_passes_per_step = backward_passes_per_step
      self._allreduce_delay = {
          v: self.backward_passes_per_step
          for _, v in sorted(named_parameters)
      }
      self.op = op
      self.gradient_predivide_factor = gradient_predivide_factor
      self.sparse_as_dense = sparse_as_dense
      self.process_set = process_set
  
      self._handles = {}
      self._grad_accs = []
      self._requires_update = set()
      self._synchronized = False
      self._should_synchronize = True
  
      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 torch.Tensor."
              )
          if isinstance(groups, list):
              grouped_parameter_ids = set()
              for l in groups:
                  for p in l:
                      if not isinstance(p, torch.Tensor):
                          raise ValueError(
                              "groups must consist of torch.Tensor."
                          )
                      if id(p) in grouped_parameter_ids:
                          raise ValueError(
                              "A parameter can only appear once in groups."
                          )
                      grouped_parameter_ids.add(id(p))
      self._groups = groups
      self._p_to_group = {}
      self._group_counts = {}
  
      if self.process_set.included() and (
          size() > 1 or os.environ.get("HOROVOD_ELASTIC") == "1"
      ):
          self._register_hooks()
  
imports: import os
  import warnings
  from contextlib import contextmanager
  import torch
  from horovod.common.util import split_list
  from horovod.torch.compression import Compression
  from horovod.torch.functions import broadcast_object
  from horovod.torch.mpi_ops import allreduce_async_, grouped_allreduce_async_, sparse_allreduce_async
  from horovod.torch.mpi_ops import synchronize
  from horovod.torch.mpi_ops import size
  from horovod.torch.mpi_ops import Average, Adasum, Sum
  from horovod.torch.mpi_ops import rocm_built
  from horovod.torch.mpi_ops import ProcessSet, global_process_set
label_desc: Use image node_id 1 to create a new _DistributedOptimizer object from inherited base classes: torch.optim with example: obj = _DistributedOptimizer(params, named_parameters, compression, backward_passes_per_step, op, gradient_predivide_factor, groups, sparse_as_dense, process_set)