code: def broadcast_optimizer_state(
      optimizer, root_rank, prefix="Parameter."
  ):
      """
      Broadcasts an optimizer state from root rank to all other processes.
      Arguments:
          optimizer: An optimizer.
          root_rank: The rank of the process from which the optimizer will be
                     broadcasted to all other processes.
      """
      if isinstance(optimizer, torch.optim.LBFGS):
          # TODO(travis): L-BFGS cannot be easily supported without serializing
          # the entire state_dict, as its structure is deeply nested and contains
          # None type parameter values
          raise ValueError("cannot broadcast torch.optim.LBFGS state")
  
      state_dict = optimizer.state_dict()
  
      # Newly created optimizers will not have their state initialized, so
      # do that initialization here
      if len(state_dict["state"]) == 0:
          for group in optimizer.param_groups:
              for p in group["params"]:
                  p.grad = p.data.new(p.size()).zero_()
          # This function accepts a torch.optim.Optimizer or a DistributedOptimizer
          # wrapped around a torch optimizer. Calling step() with a DistributedOptimizer
          # forces push_pull on all model parameters, which will result in deadlock
          # unless every rank calls step(). Therefore, to finish state initialization
          # only call optimizer.step() with a torch.optim.Optimizer.
          if optimizer.__module__ == DistributedOptimizer.__module__:
              super(optimizer.__class__, optimizer).step()
          else:
              optimizer.step()
          state_dict = optimizer.state_dict()
  
      # If the state_dict is still empty after initialization, then
      # the optimizer is stateless, and there is nothing to broadcast.
      # Furthermore, attempting to access the state dict would result in
      # an error.
      if len(state_dict["state"]) == 0:
          return
  
      params = []
      scalars = {}
      callbacks = {}
      occurrences = collections.defaultdict(int)
  
      # Returns the full type structure of the possibly nested objects for recursive casting back
      def _get_types(x):
          if isinstance(x, collections.Iterable):
              return type(x), [_get_types(xi) for xi in x]
          else:
              return type(x)
  
      # Casts an object encoded in a tensor back into its original type and subtypes
      def _recursive_cast(x, dtype):
          if isinstance(dtype, tuple):
              t, dtypes = dtype
              x = t(x)
              return t(
                  [
                      _recursive_cast(x[i], dtypes[i])
                      for i in range(len(x))
                  ]
              )
          else:
              return dtype(x)
  
      # Some optimizer parameters may be represented as scalars instead of
      # tensors.  In such cases, we place the scalars into a single dict,
      # then pickle and broadcast with broadcast_object (under the assumption
      # that there are not many scalars, and so the overhead of pickling will
      # be relatively low). Because broadcast_object is performed out-of-place,
      # we then use a callback to assign the new value to the correct element
      # of the optimizer state.
      def _create_state_callback(pid, name):
          def _assign_state(v):
              state_dict["state"][pid][name] = v
  
          return _assign_state
  
      def _create_option_callback(index, option_key):
          def _assign_option(v):
              optimizer.param_groups[index][option_key] = v
  
          return _assign_option
  
      # Param groups are an ordered list, normally there is only one per model,
      # but users can add additional param groups for example to train
      # previously frozen layers
      for index, group in enumerate(state_dict["param_groups"]):
          # Broadcast options like learning rate
          for option_key, option_value in group.items():
              if option_key == "params":
                  continue
  
              # Options like the learning rate are scalar, and need to be broadcast separately
              key = "%s.%d" % (option_key, index)
              dtypes = _get_types(option_value)
              option_tensor = torch.Tensor([option_value]).cuda()
              scalars[key] = option_value
              callbacks[key] = _create_option_callback(
                  index, option_key
              )
  
          # The params list here is ordered by the layers in the model
          for pid in group["params"]:
              if pid not in state_dict["state"]:
                  # The param has not set requires_grad, so skip broadcast
                  continue
  
              param_state = state_dict["state"][pid]
              for name, p in param_state.items():
                  # Some parameter names may appear more than once, in which
                  # case we ensure they have a unique identifier defined by
                  # their order
                  occurrences[name] += 1
                  key = "%s.%d" % (str(name), occurrences[name])
  
                  if torch.is_tensor(p):
                      # Tensor -> use broadcast_parameters
                      params.append((key, p))
                  else:
                      # Scalar -> use broadcast_object
                      scalars[key] = p
                      callbacks[key] = _create_state_callback(pid, name)
  
      # Synchronized broadcast of all parameters
      broadcast_parameters(params, root_rank, prefix)
  
      # Broadcast and cleanup for non-tensor parameters
      scalars = broadcast_object(scalars, root_rank)
      for key, p in scalars.items():
          callbacks[key](p)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from contextlib import contextmanager
  from byteps.torch.compression import Compression
  from byteps.torch.ops import push_pull_async_inplace
  from byteps.torch.ops import push_pull
  from byteps.torch.ops import poll, synchronize, declare
  from byteps.torch.ops import init, shutdown, suspend, resume
  from byteps.torch.ops import size, local_size, rank, local_rank
  import os
  import torch
  import collections
  import io
  import cloudpickle
label_desc: Use image node_id 3 for calling a global function with example usage: broadcast_optimizer_state(optimizer, root_rank, prefix) and returns: _assign_state, _assign_option, type,  unknown, type, t, dtype