code: def apply_gradient_allreduce(module):
      # sync model parameters
      for p in module.state_dict().values():
          if not torch.is_tensor(p):
              continue
          dist.broadcast(p, 0)
  
      def allreduce_params():
          if module.needs_reduction:
              module.needs_reduction = False
              # bucketing params based on value types
              buckets = {}
              for param in module.parameters():
                  if param.requires_grad and param.grad is not None:
                      tp = type(param.data)
                      if tp not in buckets:
                          buckets[tp] = []
                      buckets[tp].append(param)
              for tp in buckets:
                  bucket = buckets[tp]
                  grads = [param.grad.data for param in bucket]
                  coalesced = _flatten_dense_tensors(grads)
                  dist.all_reduce(coalesced, op=dist.reduce_op.SUM)
                  coalesced /= dist.get_world_size()
                  for buf, synced in zip(
                      grads, _unflatten_dense_tensors(coalesced, grads)
                  ):
                      buf.copy_(synced)
  
      for param in list(module.parameters()):
  
          def allreduce_hook(*_):
              Variable._execution_engine.queue_callback(
                  allreduce_params
              )  # pylint: disable=protected-access
  
          if param.requires_grad:
              param.register_hook(allreduce_hook)
  
      def set_needs_reduction(self, *_):
          self.needs_reduction = True
  
      module.register_forward_hook(set_needs_reduction)
      return module
  
file_path: TTS/TTS/utils/distribute.py
imports: import math
  import torch
  import torch.distributed
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  from torch.autograd import Variable
  from torch.utils.data.sampler import Sampler
label_desc: Use image node_id 3 for calling a global function with example usage: apply_gradient_allreduce(module) and returns: module