class_docstr: Implements distributed data parallelism that is based on
  ``torch.distributed`` package at the module level.
code: def __init__(self, model: nn.Module):
      super().__init__()
  
      # Synchronize the model
      params = list(model.parameters())
      with torch.no_grad():
          for p in params:
              torch.distributed.broadcast(p.data, 0)
  
      self.module = model
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new DifferentiallyPrivateDistributedDataParallel object from inherited base classes: nn with example: obj = DifferentiallyPrivateDistributedDataParallel(model)