class_docstr: Megatron specific extensions of torch Module with support
  for pipelining.
code: def sync_initial_position_embeddings(self):
      # Ensure that the encoder first stage and decoder first have the same
      # initial position embedding parameter values.
      # NOTE: We don't currently support T5 with the interleaved schedule.
      if (
          parallel_state.is_rank_in_position_embedding_group()
          and parallel_state.get_pipeline_model_parallel_split_rank()
          is not None
      ):
          # TODO: Support tokentype embedding.
          # self.language_model.embedding.cuda()
          position_embeddings = self.position_embeddings_weight()
          torch.distributed.all_reduce(
              position_embeddings.data,
              group=parallel_state.get_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 9 for calling the MegatronModule obj's underlying member method code with example usage: obj.sync_initial_position_embeddings() without return types