class_docstr: Megatron specific extensions of torch Module with support
  for pipelining.
code: def sync_initial_decoder_cross_attention_relative_position_embeddings(
      self,
  ):
      if (
          parallel_state.is_rank_in_decoder_relative_position_embedding_group()
      ):
          position_embeddings = (
              self.decoder_cross_attention_relative_position_embeddings_weight()
          )
          torch.distributed.all_reduce(
              position_embeddings.data,
              group=parallel_state.get_decoder_relative_position_embedding_group(),
          )
  
imports: import torch
  from torch.autograd import Variable
  from torch.nn.parameter import Parameter
  from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults
  from nemo.utils import logging
label_desc: Use image node_id 13 for calling the MegatronModule obj's underlying member method code with example usage: obj.sync_initial_decoder_cross_attention_relative_position_embeddings() without return types